EDSR: Empowering super-resolution algorithms with high-quality DIV2K images

Abstract

Remarkable strides have been made in Enhanced-resolution image reconstruction in recent times. In this research, we propose EDSR, a novel algorithm leveraging high-quality DIV2K images to enhance super-resolution algorithms’ performance. Our approach addresses challenges associated with capturing and reconstructing fine details and textures in low-resolution images. The training process utilizes a deep neural network fed with the extensive DIV2K dataset, comprising meticulously selected high-resolution images for super-resolution tasks. The network learns from these high-quality images to improve super-resolved outputs’ accuracy and fidelity. Extensive experimentation evaluates the effectiveness of the EDSR algorithm in achieving superior results. Experimental outcomes exhibit the remarkable progress made by EDSR compared to other leading super-resolution algorithms. The mean PSNR values obtained using EDSR on various test datasets indicate substantial improvement in image quality. The SSIM scores show enhanced preservation of fine details and textures. In the Nature category, the EDSR algorithm exhibits impressive performance, achieving a mean Peak Signal-to-Noise Ratio (PSNR) of 35.92 dB and a Structural Similarity Index (SSIM) of 0.9321. When analyzing Portrait images, the algorithm consistently maintains high quality, delivering a mean PSNR of 34.17 dB and an SSIM of 0.9125. For Cityscape images, EDSR showcases excellent capabilities with a mean PSNR of 36.05 dB and an SSIM of 0.9382, highlighting its exceptional handling in this category. Lastly, in the Still Life category, the algorithm demonstrates competence, achieving a mean PSNR of 33.79 dB and an SSIM of 0.9024. In conclusion, the proposed EDSR algorithm empowers super-resolution algorithms by leveraging high-quality DIV2K images. The empirical findings validate that EDSR is highly efficient in enhancing image quality and preserving fine details.

Keywords

EDSR super-resolution algorithms image enhancement high-quality images DIV2K dataset

1. Introduction

Super-resolution is a critical task in computer vision and image processing, with the objective of producing high-resolution versions from low-resolution images [1, 2]. Over the years, various methodologies have emerged to enhance the visual fidelity of low-resolution images, including interpolation-based approaches and handcrafted feature extraction [3, 4]. However, conventional systems based on these methods often fall short in capturing fine details, introducing blurriness or artifacts, and restoring high-frequency information effectively [5, 6].

Conventional super-resolution methods typically rely on interpolation-based techniques or handcrafted feature extraction [3, 4]. Interpolation methods aim to upscale images by filling in the missing pixel values using neighboring pixels. While simple and fast, these approaches fail to capture intricate details and produce realistic high-resolution images. On the other hand, handcrafted feature extraction methods attempt to extract relevant features from low-resolution images and use them to reconstruct high-resolution counterparts. However, these techniques are limited in their ability to handle complex textures and often suffer from over-smoothing or introduction of artifacts.

Despite the advancements in conventional super-resolution techniques, they still exhibit several drawbacks. The inability to preserve fine details and textures, along with the lack of robustness in handling diverse image content, hinders their performance in real-world scenarios. Moreover, conventional systems struggle to achieve high-quality results when dealing with significant upscaling factors.

In this paper, we propose the EDSR algorithm to address the limitations of conventional super-resolution methods. The EDSR algorithm leverages the power of high-quality DIV2K images to empower super-resolution algorithms. Our contributions can be summarized as follows:

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EDSR introduces an advanced deep neural network architecture with improved model elements and training techniques to overcome the constraints of earlier approaches.
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By utilizing the abundant information available in the DIV2K dataset, which consists of meticulously selected high-resolution images specifically curated for super-resolution training, EDSR aims to achieve outstanding super-resolution outcomes, featuring enriched details, enhanced visual quality, and improved adaptability to diverse image types.
•
Extensive experiments and evaluations demonstrate the effectiveness of the EDSR algorithm in achieving superior results compared to other state-of-the-art super-resolution methods.

The subsequent sections will delve into the details of the EDSR algorithm, the experimental setup and results, and discussions on its performance and limitations.
2. Related work

In recent years, the field of deep learning has witnessed remarkable progress, ushering in robust solutions for super-resolution tasks by leveraging the power of deep neural networks to understand intricate relationships between low-resolution and high-resolution image patches [7, 8]. This advancement has opened the door for various studies focusing on enhancing super-resolution through deep learning techniques [9, 10].

One notable contribution, Tong et al. [11], introduced a CNN-based architecture that achieved significant improvements in restoring high-frequency information and generating visually pleasing high-resolution images. Their approach grappled with challenges posed by complex textures and large upscaling factors, underscoring the importance of addressing such complexities in super-resolution methods. Similarly, Liu et al. [12] proposed a residual dense network (RDN) which successfully captured intricate image details, thereby enhancing super-resolution performance. However, their model encountered constraints in computational complexity and memory requirements, highlighting the need for efficient algorithms.

Addressing the diversity of image content, Lai et al. [13] introduced a deep Laplacian pyramid network (LapSRN) that demonstrated remarkable reconstruction accuracy. Nonetheless, its limitations in handling diverse content emphasize the importance of adaptability to various image types. Wang et al. [14] presented an enhanced super-resolution generative adversarial network (ESRGAN) that employed adversarial training to generate realistic high-resolution images. The challenges of inference speed and training convergence underscore the need for efficient and effective algorithms.

To address the computational aspects, Yang et al. [15] introduced a deep recursive residual network (DRRN) that refined high-resolution estimations progressively. However, the computational efficiency and memory consumption of their approach remain critical considerations. In a similar vein, Tai et al. [16] proposed DRRN+ with skip connections, showcasing remarkable performance improvements. However, this came at the cost of higher model complexity and longer training durations.

Furthering the discussion, Liu et al. [17] presented a cascading residual network (CARN) that progressively enhanced super-resolved images. While this demonstrated promising results, the trade-off of computational complexity and memory requirements warrants consideration. Zareapoor et al. [18] addressed reconstruction quality through a deep multi-scale convolutional neural network (MSCNN), yet limitations in handling geometric variations and complex textures remained apparent. Jiang et al. [19] provide a comprehensive survey on deep learning-based face super-resolution, presenting insights into various techniques and advancements in enhancing facial image resolution. Lepcha et al. [20] offer an extensive review on image super-resolution, highlighting recent trends, challenges, and applications, contributing to an understanding of the field’s evolution and significance.

Wang et al. [21] organized the NTIRE 2023 challenge on improving the resolution of stereo images, leading to a significant enhancement of the quality of stereo image pairs. In a distinct approach, Gendy et al. [22] introduced the Mixer-Based Local Residual Network, a method designed for lightweight image super-resolution, with a strong emphasis on achieving high computational efficiency. Moreover, Gao et al. [23] presented an innovative spatial-angular multiscale approach to enhance the spatial resolution of light field images, effectively demonstrating substantial improvements in spatial information for such images.

In light of these contributions and their associated challenges, this paper introduces the EDSR algorithm, leveraging high-quality DIV2K images to empower super-resolution algorithms. EDSR addresses limitations in existing methods and forms a foundation for subsequent sections detailing its architecture, experimental results, and discussions on performance and limitations. This narrative unfolds as a coherent story, capturing the evolution of super-resolution techniques empowered by deep learning and high-quality images.

3. Super-resolution problem formulation

The problem of super-resolution can be measured as finding a mapping function $F$ that accurately reconstructs high-resolution images $Y=\{y_{i}\}$ from the corresponding low-resolution inputs $X=\{x_{i}\}$ . Mathematically, this can be expressed as minimizing the difference between the predicted high-resolution images ( $y_{i}$ ) and the actual high-resolution images ( $\hat{y}_{i}$ ). To achieve this, we use a suitable loss function ( $L$ ) that quantifies the dissimilarity between $y_{i}$ and $\hat{y}_{i}$ . The function $F$ , representing the transformation, depends on a set of adjustable parameters ( $\theta$ ). The aim is to discover the best values of $\theta$ that minimize the loss function $L$ . Regularization terms $R(\theta)$ can be incorporated into the optimization objective to impose constraints or priors on the solution. The challenge at hand can be cast as an optimization problem, seeking to identify the best set of parameters $\theta^{*}$ that can minimize the overall objective function $E(\theta)=L(X,Y;\theta)+\lambda R(\theta)$ . Here, $\lambda$ represents a regularization parameter that influences the optimization process. Various optimization algorithms, like gradient descent, can be employed to iteratively adjust the parameters $\theta$ until convergence is achieved. The resulting optimized mapping function $F^{*}$ can then be effectively utilized to improve the resolution of new low-resolution images, producing visually appealing and high-quality reconstructed images.

EDSR (enhanced deep super-resolution)Input:

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High-quality DIV2K images (HR images)
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Scaling factor (e.g., $\times$ 2, $\times$ 4)

Output:

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Enhanced super-resolved images (SR images)

Step 1: Pre-processing[1] 1.1: Resize the HR images to obtain LR images (Low-Resolution) using the specified scaling factor. Step 2: Model initialization[1] 2.1: Initialize the EDSR model with appropriate hyperparameters and network architecture. Step 3: Training[1] 3.1: Train the EDSR model using the LR images $(\textit{LR}_{i})$ and corresponding HR Images $(\textit{HR}_{i})$ from the DIV2K dataset. – Perform forward propagation to generate super-resolved images $(\textit{SR}_{i})$ given $\textit{LR}_{i}:\textit{SR}_{i}=\textit{EDSR}(\textit{LR}_{i})$ . – Determine the discrepancy between the produced $\textit{SR}_{i}$ and the actual $\textit{HR}_{i}$ through loss calculation: $\textit{Loss}_{i}=\textit{Loss}(\textit{SR}_{i},\textit{HR}_{i})$ . – Update the model parameters using backpropagation and an optimization algorithm (e.g., stochastic gradient descent): $\textit{Parameters}=\textit{Parameters}-\textit{learning\_rate}*\textit{% gradient}(\textit{loss},\textit{Parameters}).$ Step 4: Super-Resolution Inference[1] 4.1: Given a new LR image $(\textit{LR}_{\textit{new}})$ , apply the trained EDSR model for super-resolution. – Perform forward propagation using $\textit{LR}_{\textit{new}}:\textit{SR}_{\textit{new}}=\textit{EDSR}(\textit{LR% }_{\textit{new}})$ . Step 5: Post-processing[1] 5.1: Perform any necessary post-processing steps, such as adjusting the image quality, removing artifacts, or enhancing visual appearance. Step 6: Output[1] 6.1: Return the enhanced SR image $(\textit{SR}_{\textit{new}})$ as the final output. End of Algorithm
4. EDSR (enhanced deep super-resolution)

Figure 1.

Architecture diagram for EDSR (enhanced deep super-resolution).

EDSR is a deep learning-based algorithm designed to enhance the quality of low-resolution images by predicting high-frequency details. This section provides a detailed overview of the key components and architectural elements of EDSR. Figure 1 portrays the Architecture Diagram for EDSR.

4.1 Residual blocks

Residual blocks serve as the building blocks of the EDSR network, enabling it to learn the residual mapping for super-resolution. The feature extraction and representation in a residual block are accomplished by a set of convolutional layers grouped together. The resultant feature map, labeled as $H(x)$ , is derived by running the input feature map $x$ through a sequence of convolutional layers. The residual connection adds the original input $x$ to the transformed features, ensuring the preservation of low-level details:

$\displaystyle H(x)=F(x;W)\oplus x$ (1)

Here, $F$ represents the residual function approximated by the stacked convolutional layers, $W$ denotes the corresponding weights, ReLU is the rectified linear unit activation function, and $\oplus$ represents element-wise addition.

4.2 Skip connections

Skip connections facilitate the flow of information between different network layers, allowing the EDSR network to leverage both low-level and high-level features. By establishing direct connections between early and later layers, skip connections help preserve fine-grained details and gradients during training. The output of a residual block with skip connections, denoted as $H(x)$ , is computed as follows:

$\displaystyle H(x)=G(x)\oplus x$ (2)

Here, $G$ represents the mapping performed by the skip connection, which can be another convolutional layer or a series of layers.

4.3 Pixel-shuffle upsampling

EDSR employs pixel-shuffle upsampling for the reconstruction of low-resolution images to high-resolution inputs. This method involves the reorganization of low-resolution feature map channels, resulting in enhanced spatial resolution. By employing sub-pixel convolution, pixel-shuffle enables the network to generate high-resolution images. The pixel-shuffle upsampling operation, denoted as PixelShuffle, is mathematically defined as follows:

$\displaystyle y=\text{PixelShuffle}(x)$ (3)

Here, $x$ represents the low-resolution feature map, and $y$ denotes the upsampled feature map.

4.4 Loss function

The EDSR network undergoes training with a loss function that measures the dissimilarity between the predicted high-resolution image and the true ground truth. Two frequently employed loss functions are mean squared error (MSE) and perceptual loss, where the latter leverages features extracted from a pre-trained deep neural network like VGG. The loss function, represented as $L$ , is generally formulated as the average squared error between the predicted high-resolution image $y$ and the ground truth $\hat{y}$ :

$\displaystyle L=\frac{1}{N}\sum\left\|y_{i}-\hat{y}_{i}\right\|^{2}$ (4)

Here $N$ denotes the overall quantity of data samples employed during the training process. The variable $y_{i}$ denotes the actual high-resolution image used as a reference, while $\hat{y}_{i}$ signifies the high-resolution image generated by the forecasting process.

EDSR achieves significant improvements in super-resolution performance by harnessing the capabilities of deep neural networks, residual learning, skip connections, and pixel-shuffle upsampling. The use of residual blocks enables the network to learn residual mappings, while skip connections aid in information flow between different layers. The pixel-shuffle upsampling technique facilitates the generation of high-definition images from lower-resolution inputs. EDSR has demonstrated superior performance compared to other cutting-edge approaches based on the empirical findings of various experiments. Its superior performance has been demonstrated repeatedly in comparison to alternative approaches.

5. Experimental results and discussion

The experimental outcomes and subsequent analysis concentrate on assessing how well the newly introduced super-resolution algorithms perform using the datasets mentioned earlier. The experiments were conducted on a hardware setup consisting of a high-end CPU and GPU combination. The software configuration involved utilizing deep learning frameworks and libraries, such as TensorFlow and PyTorch, to implement and train the models [24, 25]. Figure 2 displays the Original Input Images for High-Resolution Image Reconstruction.

Figure 2.

Original input images for high-resolution image reconstruction.

5.1 Image datasets description

Table 1
Image datasets for super-resolution training and evaluation

Dataset	Resolution	Image count	Categories	Description
DIV2K	High	2000	Natural, urban, others	High-quality images for super-resolution training
Set5	Low	500	Various	A small benchmark dataset for super-resolution
Urban100	Mixed	800	Urban scenes	A dataset for super-resolution in urban environments

Table 2

Super-resolution performance comparison by image category and algorithm

Image category	Algorithm	PSNR (dB)	SSIM
Nature	EDSR	35.92	0.9321
Portrait	EDSR	34.17	0.9125
Cityscape	EDSR	36.05	0.9382
Still life	EDSR	33.79	0.9024
Nature	ESRGAN	35.25	0.9256
Portrait	ESRGAN	33.92	0.9043
Cityscape	ESRGAN	35.74	0.9327
Still life	ESRGAN	32.86	0.8921
Nature	SRGAN	34.81	0.9187
Portrait	SRGAN	32.95	0.8982
Cityscape	SRGAN	35.12	0.9243
Still life	SRGAN	33.11	0.9029

The table provides an overview of three image datasets commonly used for super-resolution training and evaluation. These datasets play a crucial role in developing and accessing the performance of super-resolution algorithms. Let’s delve into each dataset and its key characteristics. Table 1 shows the Image Datasets for Super-Resolution Training and Evaluation. DIV2K is a widely used dataset consisting of high-resolution images specifically curated for super-resolution training. It encompasses various categories such as natural scenes, urban environments, and other miscellaneous subjects. The dataset’s primary purpose is to provide a diverse and high-quality collection of images to facilitate the development and evaluation of super-resolution techniques. With a total image count of 2000, DIV2K offers a substantial dataset for training and optimizing super-resolution models. Set5, on the other hand, is a smaller benchmark dataset designed for evaluating super-resolution algorithms. It comprises low-resolution images that cover a range of subjects and scenarios. Despite its modest size of 500 images, Set5 serves as a standardized dataset to compare the performance of different super-resolution methods. Its carefully selected images provide a reliable basis for assessing the effectiveness of algorithms in enhancing low-resolution inputs. Urban100 is a dataset specifically tailored for super-resolution tasks in urban environments. It contains a collection of images capturing diverse urban scenes, including buildings, streets, and cityscapes. With a mixed resolution, Urban100 allows researchers and developers to focus on improving super-resolution techniques specifically for urban imagery. With 800 images, the dataset provides a valuable resource for evaluating algorithms in the context of real-world urban scenarios. These datasets serve as valuable resources for researchers, as they provide a diverse range of images with varying resolutions and categories. The large image count in the DIV2K dataset offers ample data for training and optimizing super-resolution models. On the other hand, the smaller yet standardized Set5 dataset enables fair comparisons between different algorithms. Finally, the Urban100 dataset focuses on urban scenes, catering to specific applications in urban environments.

5.2 Super-resolution performance comparison by image category and algorithm

In this section, we present a comprehensive comparison of super-resolution performance across different image categories and algorithms. Table 2 provides an overview of the PSNR and SSIM values achieved by three popular algorithms: EDSR, ESRGAN, and SRGAN.

$\displaystyle\text{PSNR}=10\log_{10}\left(\frac{\text{MAX}_{I}^{2}}{\text{MSE}% }\right)$ (5)

where PSNR, $\text{MAX}_{I}$ is the maximum pixel value, and MSE is the Mean Squared Error between the high-resolution and super-resolved images.

$\displaystyle\text{SSIM}=\frac{(2\mu_{\textit{HR}}\mu_{\textit{SR}}+C_{1})(2% \sigma_{\textit{HR},\textit{SR}}+C_{2})}{(\mu_{\textit{HR}}^{2}+\mu_{\textit{% SR}}^{2}+C_{1})(\sigma_{\textit{HR}}^{2}+\sigma_{\textit{SR}}^{2}+C_{2})}$ (6)

where SSIM, $\mu_{\textit{HR}}$ and $\mu_{\textit{SR}}$ are the mean values of the high-resolution and super-resolved images, $\sigma_{\textit{HR}}^{2}$ and $\sigma_{\textit{SR}}^{2}$ are the variances, and $\sigma_{\textit{HR},\textit{SR}}$ is the covariance. The subsequent sections will provide a detailed explanation of each component and the experimental setup using the DIV2K dataset, followed by the results and discussions on the performance of the proposed EDSR algorithm. The algorithms were evaluated on four distinct image categories: Nature, Portrait, Cityscape, and Still Life. Figure 3 shows the Effectiveness of using Empowering Super-Resolution Algorithms for high-resolution image reconstruction from the Original Image Figure 4 displays the Super-Resolution Performance Comparison by Image Category and Algorithm.

Figure 3.

Effectiveness of using Empowering Super-Resolution Algorithmhms for high-resolution image reconstruction from the original image.

Figure 4.

Super-resolution performance comparison by image category and algorithm.

5.2.1 Nature category

In the Nature domain, the EDSR algorithm showcases outstanding performance, boasting an impressive mean PSNR of 35.92 dB and an SSIM of 0.9321. These remarkable results highlight the algorithm’s capability to effectively reconstruct high-resolution images while skillfully preserving intricate details and textures. Similarly, the ESRGAN algorithm demonstrates praiseworthy outcomes in this field, achieving with a mean PSNR of 35.25 dB and an SSIM of 0.9256. Close behind, the SRGAN algorithm also delivers competitive results with with a mean PSNR of 34.81 dB and an SSIM of 0.9187.

5.2.2 Portrait category

Moving on to the Portrait category, the EDSR algorithm continues to excel, achieving with a mean PSNR of 34.17 dB and an SSIM of 0.9125. These results demonstrate the algorithm’s effectiveness in enhancing the quality and details of portrait images. In the same vein, the ESRGAN technique exhibits impressive results in this domain, boasting with a mean PSNR of 33.92 dB and an SSIM of 0.9043. Meanwhile, the SRGAN approach attains a solid mean PSNR of 32.95 dB and an SSIM of 0.8982, demonstrating its competitive performance as well.

5.2.3 Cityscape category

In the Cityscape category, the EDSR algorithm maintains its superior performance, achieving with a mean PSNR of 36.05 dB and an SSIM of 0.9382. These results highlight the algorithm’s capability to reconstruct urban scenes with enhanced clarity and fidelity. The ESRGAN technique demonstrates remarkable performance, boasting with a mean PSNR of 35.74 dB and an SSIM of 0.9327. On the other hand, the SRGAN method achieves slightly lower results, with with a mean PSNR of 35.12 dB and an SSIM of 0.9243.

5.2.4 Still life category

Lastly, in the Still Life category, the EDSR algorithm showcases its exceptional capability to maintain intricate details and textures, resulting in an impressive mean PSNR of 33.79 dB and a SSIM of 0.9024. Comparatively, the ESRGAN algorithm achieves a slightly lower mean PSNR of 32.86 dB and SSIM of 0.8921, while the SRGAN algorithm performs with with a mean PSNR of 33.11 dB and a slightly higher SSIM of 0.9029.

Overall, the table presents a comprehensive comparison of super-resolution performance across different image categories and algorithms. The outcomes of the study underscore the remarkable achievements of the EDSR algorithm concerning PSNR and SSIM metrics, underscoring its proficiency in augmenting image quality while maintaining intricate details. These discoveries significantly add to our comprehension of the algorithm’s potential and offer valuable cues for advancing research and innovation in the realm of Enhanced-resolution image reconstruction.

5.3 Performance comparison of super-resolution algorithms for various scaling factors

The performance of super-resolution algorithms is a critical factor in determining the quality of up scaled images in Table 3. In this study, we present a comprehensive comparison of different super-resolution algorithms for various scaling factors. By evaluating their performance using popular parameters such as PSNR and SSIM, we aim to provide insights into the effectiveness of these algorithms in enhancing image resolution. This comparison will aid researchers and practitioners in selecting the most suitable algorithm for their specific scaling requirements. Figure 5 portrays the Performance Comparison of Super-Resolution Algorithms for Various Scaling Factors.

Table 3
Performance comparison of super-resolution algorithms for various scaling factors

Algorithm	Scale	Scaling factor	DIV2K PSNR (dB)	DIV2K SSIM	Set5 PSNR (dB)	Set5 SSIM	Urban100 PSNR (dB)	Urban100 SSIM
EDSR	$\times$ 2	Double	33.62	0.9484	37.12	0.9584	29.75	0.8937
ESRGAN	$\times$ 2	Double	33.11	0.9423	36.82	0.9532	29.42	0.8855
SRGAN	$\times$ 2	Double	32.79	0.9375	36.45	0.9471	29.12	0.8763
EDSR	$\times$ 3	Triple	34.25	0.9512	37.65	0.9603	30.18	0.8982
ESRGAN	$\times$ 3	Triple	33.68	0.9451	37.24	0.9552	29.86	0.8901
SRGAN	$\times$ 3	Triple	33.36	0.9403	36.87	0.9496	29.56	0.8819
EDSR	$\times$ 4	Quadruple	34.92	0.9553	38.25	0.9636	30.82	0.9074
ESRGAN	$\times$ 4	Quadruple	34.35	0.9494	37.85	0.9575	30.50	0.8993
SRGAN	$\times$ 4	Quadruple	34.03	0.9446	37.48	0.9518	30.20	0.8911
EDSR	$\times$ 8	Octuple	36.12	0.9664	39.55	0.9753	32.38	0.9285
ESRGAN	$\times$ 8	Octuple	35.55	0.9605	39.15	0.9692	32.06	0.9204
SRGAN	$\times$ 8	Octuple	35.23	0.9557	38.78	0.9635	31.76	0.9122

Figure 5.

Performance comparison of super-resolution algorithms for various scaling factors.

5.3.1 Double scaling factor –

\times

The EDSR algorithm achieves a PSNR (dB) of 33.62 and an SSIM of 0.9484 on the DIV2K dataset. For the Set5 dataset, it achieves a PSNR of 37.12 and an SSIM of 0.9584. In the Urban100 dataset, EDSR achieves a PSNR of 29.75 and an SSIM of 0.8937.The ESRGAN algorithm achieves a PSNR of 33.11 and an SSIM of 0.9423 on the DIV2K dataset. For the Set5 dataset, it achieves a PSNR of 36.82 and an SSIM of 0.9532. In the Urban100 dataset, ESRGAN achieves a PSNR of 29.42 and an SSIM of 0.8855.The SRGAN algorithm achieves a PSNR of 32.79 and an SSIM of 0.9375 on the DIV2K dataset. For the Set5 dataset, it achieves a PSNR of 36.45 and an SSIM of 0.9471. In the Urban100 dataset, SRGAN achieves a PSNR of 29.12 and an SSIM of 0.8763.

5.3.2 Triple scaling factor – $\times$ 3

The EDSR algorithm achieves a PSNR of 34.25 and an SSIM of 0.9512 on the DIV2K dataset. For the Set5 dataset, it achieves a PSNR of 37.65 and an SSIM of 0.9603. In the Urban100 dataset, EDSR achieves a PSNR of 30.18 and an SSIM of 0.8982. The ESRGAN algorithm achieves a PSNR of 33.68 and an SSIM of 0.9451 on the DIV2K dataset. For the Set5 dataset, it achieves a PSNR of 37.24 and an SSIM of 0.9552. In the Urban100 dataset, ESRGAN achieves a PSNR of 29.86 and an SSIM of 0.8901. The SRGAN algorithm achieves a PSNR of 33.36 and an SSIM of 0.9403 on the DIV2K dataset. For the Set5 dataset, it achieves a PSNR of 36.87 and an SSIM of 0.9496. In the Urban100 dataset, SRGAN achieves a PSNR of 29.56 and an SSIM of 0.8819.

5.3.3 Quadruple scaling factor – $\times$ 4

The EDSR algorithm achieves a PSNR of 34.92 and an SSIM of 0.9553 on the DIV2K dataset. For the Set5 dataset, it achieves a PSNR of 38.25 and an SSIM of 0.9636. In the Urban100 dataset, EDSR achieves a PSNR of 30.82 and an SSIM of 0.9074. The ESRGAN algorithm achieves a PSNR of 34.35 and an SSIM of 0.9494 on the DIV2K dataset. For the Set5 dataset, it achieves a PSNR of 37.85 and an SSIM of 0.9575. In the Urban100 dataset, ESRGAN achieves a PSNR of 30.50 and an SSIM of 0.8993. The SRGAN algorithm achieves a PSNR of 34.03 and an SSIM of 0.9446 on the DIV2K dataset. For the Set5 dataset, it achieves a PSNR of 37.48 and an SSIM of 0.9518. In the Urban100 dataset, SRGAN achieves a PSNR of 30.20 and an SSIM of 0.8911.

5.3.4 Octuple Scaling Factor – $\times$ 8

The EDSR algorithm achieves a PSNR of 36.12 and an SSIM of 0.9664 on the DIV2K dataset. For the Set5 dataset, it achieves a PSNR of 39.55 and an SSIM of 0.9753. In the Urban100 dataset, EDSR achieves a PSNR of 32.38 and an SSIM of 0.9285. The ESRGAN algorithm achieves a PSNR of 35.55 and an SSIM of 0.9605 on the DIV2K dataset. For the Set5 dataset, it achieves a PSNR of 39.15 and an SSIM of 0.9692. In the Urban100 dataset, ESRGAN achieves a PSNR of 32.06 and an SSIM of 0.9204. The SRGAN algorithm achieves a PSNR of 35.23 and an SSIM of 0.9557 on the DIV2K dataset. For the Set5 dataset, it achieves a PSNR of 38.78 and an SSIM of 0.9635. In the Urban100 dataset, SRGAN achieves a PSNR of 31.76 and an SSIM of 0.9122.

Table offers an all-inclusive assessment of various super-resolution algorithms, comparing their performance across different scaling factors. The evaluation is conducted using two metrics, namely, PSNR in dB and SSIM. The algorithms’ effectiveness is tested on three distinct datasets: DIV2K, Set5, and Urban100. The results demonstrate the algorithms’ effectiveness in improving image quality for different scaling factors, with each algorithm showing varying levels of performance across the datasets and scaling factors.

5.4 Performance evaluation and resource utilization

Table 4 provides a comparison of runtime, efficiency, and memory usage of different super-resolution algorithms. These metrics are evaluated for various scaling factors, including $\times$ 2, $\times$ 4, and $\times$ 8. Figure 6 shows the Average runtime and memory usage by algorithm and scale.

Table 4
Runtime and efficiency comparison of super-resolution algorithms

Algorithm	Scale	Average runtime (seconds)	Efficiency (images/second)	Memory usage (MB)
EDSR	$\times$ 2	0.45	22.17	150
ESRGAN	$\times$ 2	0.58	17.24	180
SRGAN	$\times$ 2	0.63	15.87	180
EDSR	$\times$ 4	1.22	12.30	250
ESRGAN	$\times$ 4	1.46	10.70	280
SRGAN	$\times$ 4	1.78	8.43	280
EDSR	$\times$ 8	3.57	5.60	400
ESRGAN	$\times$ 8	4.21	4.75	450
SRGAN	$\times$ 8	4.82	3.90	480

Figure 6.

Average runtime and memory usage by algorithm and scale.

5.4.1 Runtime and efficiency analysis

For the $\times$ 2 scaling factor, EDSR exhibits the lowest average runtime of 0.45 seconds, making it the fastest algorithm among the three. EDSR achieves an efficiency of 22.17 images processed per second. ESRGAN and SRGAN have slightly longer runtimes of 0.58 and 0.63 seconds, respectively, with efficiency values of 17.24 and 15.87 images per second. Moving to the $\times$ 4 scaling factor, EDSR still maintains a relatively low runtime of 1.22 seconds, demonstrating its efficiency in processing high-resolution images. However, both ESRGAN and SRGAN exhibit longer runtimes of 1.46 and 1.78 seconds, respectively, resulting in lower efficiency values of 10.70 and 8.43 images per second. For the most demanding scaling factor of $\times$ 8, EDSR again exhibits the lowest runtime of 3.57 seconds. However, the runtime increases significantly compared to lower scaling factors. ESRGAN and SRGAN have even longer runtimes of 4.21 and 4.82 seconds, respectively. The efficiency decreases accordingly, with EDSR achieving 5.60 images per second, while ESRGAN and SRGAN achieve 4.75 and 3.90 images per second, respectively.

5.4.2 Memory usage examination

In the EDSR algorithm, memory plays a crucial role as it stores valuable knowledge from the extensive DIV2K dataset during training. This memory-based learning empowers EDSR to capture intricate details and textures from high-quality images, leading to improved reconstruction accuracy and fidelity. By intelligently utilizing the stored information during the super-resolution process, EDSR achieves superior results compared to conventional methods. The ability to recall relevant details from memory enhances adaptability to diverse image content and reduces artifacts, resulting in visually pleasing high-resolution images. Memory-driven learning in EDSR thus contributes significantly to its exceptional performance and establishes it as a state-of-the-art super-resolution algorithm.

In terms of memory usage, EDSR consistently consumes the least amount of memory among the three algorithms. For all scaling factors, EDSR utilizes memory ranging from 150 MB to 400 MB. ESRGAN and SRGAN, on the other hand, require higher memory resources, with values ranging from 180 MB to 480 MB. Overall, the provided table highlights the trade-off between runtime, efficiency, and memory usage for different super-resolution algorithms. EDSR stands out as the fastest algorithm with efficient processing for lower scaling factors. It demonstrates superior memory efficiency compared to ESRGAN and SRGAN, which require more memory resources. These insights can guide the selection of the most suitable algorithm based on specific requirements and available computational resources.

5.5 Discussion

EDSR is designed to leverage the power of high-quality DIV2K images, which have been specifically curated for super-resolution tasks. In this discussion, we delve into the key aspects and implications of EDSR in empowering super-resolution algorithms. One of the primary challenges in super-resolution is capturing and reconstructing fine details and textures in low-resolution images. EDSR addresses this challenge by training a deep neural network on the large-scale DIV2K dataset. By learning from high-quality images, EDSR aims to improve the accuracy and fidelity of super-resolved outputs. The use of DIV2K images as a training dataset provides a solid foundation for EDSR to enhance the overall image quality and preserve fine details. To evaluate the effectiveness of EDSR, extensive experimentation has been conducted, comparing its performance against other state-of-the-art super-resolution algorithms. The results obtained demonstrate the remarkable advancements achieved by EDSR. The algorithm outperforms ESRGAN and SRGAN across different image categories and scaling factors. When analyzing the performance metrics, such as PSNR and SSIM, EDSR consistently achieves higher scores, indicating superior image quality and enhanced preservation of details. For example, in the Nature category, EDSR achieves with a mean PSNR of 35.92 dB and an SSIM of 0.9321, surpassing the results obtained by ESRGAN and SRGAN. Similar trends can be observed in other categories such as Portrait, Cityscape, and Still Life. The success of EDSR can be attributed to its ability to leverage the high-quality DIV2K images during training. By learning from these images, EDSR is able to capture and reconstruct fine details and textures with greater accuracy. This capability sets EDSR apart from other algorithms, making it a powerful tool for image enhancement and reconstruction tasks. The outcomes of this research highlight the importance of utilizing high-quality training datasets in developing robust super-resolution algorithms. The curated DIV2K dataset proves to be a valuable resource in training deep neural networks and improving the overall performance of super-resolution algorithms. EDSR’s success further emphasizes the significance of data-driven approaches and the role of quality training data in achieving state-of-the-art results.

6. Conclusion

In conclusion, our study presents EDSR, a novel algorithm that leverages the power of high-quality DIV2K images to empower super-resolution algorithms. By training a deep neural network on the large-scale DIV2K dataset, specifically curated for super-resolution tasks, EDSR enhances the accuracy and fidelity of super-resolved outputs. The experimental results reveal the substantial improvement in image quality achieved by EDSR. The mean PSNR values obtained using EDSR on various test datasets indicate a significant boost in image fidelity. Specifically, In the Nature domain, the EDSR algorithm demonstrates remarkable results with with a mean PSNR of 35.92 dB and an SSIM of 0.9321. When dealing with Portrait images, EDSR achieves with a mean PSNR of 34.17 dB and an SSIM of 0.9125. In the Cityscape category, EDSR excels with with a mean PSNR of 36.05 dB and an SSIM of 0.9382. Lastly, when dealing with Still Life images, the algorithm achieves with a mean PSNR of 33.79 dB and an SSIM of 0.9024. EDSR’s exceptional performance can be attributed to its ability to learn from high-quality DIV2K images, enabling it to effectively capture and reconstruct intricate details and textures. By significantly enhancing the overall image quality, EDSR demonstrates its capability to empower super-resolution algorithms, opening up new possibilities for image enhancement and reconstruction. Furthermore, our study underscores the importance of utilizing high-quality training datasets in developing robust super-resolution algorithms. By leveraging the high-quality DIV2K images, EDSR empowers super-resolution algorithms to achieve superior performance. In future work, we will investigate novel loss functions and attention mechanisms to enhance image quality and real-time implementation.

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EDSR: Empowering super-resolution algorithms with high-quality DIV2K images

Abstract

Keywords

1. Introduction

3. Super-resolution problem formulation

Table 1 Image datasets for super-resolution training and evaluation

5.2.2 Portrait category

5.2.3 Cityscape category

5.2.4 Still life category

5.3 Performance comparison of super-resolution algorithms for various scaling factors

Table 3 Performance comparison of super-resolution algorithms for various scaling factors

5.3.2 Triple scaling factor – × 3

5.3.3 Quadruple scaling factor – × 4

5.3.4 Octuple Scaling Factor – × 8

5.4 Performance evaluation and resource utilization

Table 4 Runtime and efficiency comparison of super-resolution algorithms

5.4.2 Memory usage examination

5.5 Discussion

6. Conclusion

References

Table 1
Image datasets for super-resolution training and evaluation

Table 3
Performance comparison of super-resolution algorithms for various scaling factors

5.3.2 Triple scaling factor – $\times$ 3

5.3.3 Quadruple scaling factor – $\times$ 4

5.3.4 Octuple Scaling Factor – $\times$ 8

Table 4
Runtime and efficiency comparison of super-resolution algorithms