Network For Single Image Super-resolution Research Articles

Enhanced pyramidal residual networks for single image super-resolution

Enhanced pyramidal residual networks for single image super-resolution

Dual-space high-frequency learning for transformer-based MRI super-resolution

Background and ObjectiveMagnetic resonance imaging (MRI) can provide rich and detailed high-contrast information of soft tissues, while the scanning of MRI is time-consuming. To accelerate MR imaging, a variety of Transformer-based single image super-resolution methods are proposed in recent years, achieving promising results thanks to their superior capability of capturing long-range dependencies. Nevertheless, most existing works prioritize the design of transformer attention blocks to capture global information. The local high-frequency details, which are pivotal to faithful MRI restoration, are unfortunately neglected. MethodsIn this work, we propose a high-frequency enhanced learning scheme to effectively improve the awareness of high frequency information in current Transformer-based MRI single image super-resolution methods. Specifically, we present two entirely plug-and-play modules designed to equip Transformer-based networks with the ability to recover high-frequency details from dual spaces: 1) in the feature space, we design a high-frequency block (Hi-Fe block) paralleled with Transformer-based attention layers to extract rich high-frequency features; while 2) in the image intensity space, we tailor a high-frequency amplification module (HFA) to further refine the high-frequency details. By fully exploiting the merits of the two modules, our framework can recover abundant and diverse high-frequency information, rendering faithful MRI super-resolved results with fine details. ResultsWe integrated our modules with six Transformer-based models and conducted experiments across three datasets. The results indicate that our plug-and-play modules can enhance the super-resolution performance of all foundational models to varying degrees, surpassing the capabilities of existing state-of-the-art single image super-resolution networks. ConclusionComprehensive comparison of super-resolution images and high-frequency maps from various methods, clearly demonstrating that our module possesses the capability to restore high-frequency information, showing huge potential in clinical practice for accelerated MRI reconstruction.

Lightweight blueprint residual network for single image super-resolution

The application of deep convolutional neural networks (CNNs) makes the lightweight single image super-resolution (SISR) task develop rapidly in recent years. However, existing lightweight SISR networks for the terminals with limited computing resources are still not efficient enough. In order to further alleviate the model complexity while keeping remarkable reconstruction performance, in this paper, we propose a lightweight blueprint residual network (LBRN), which is a powerful CNN-based SR model while simultaneously maintaining extremely lightweight. Specifically, we design a novel lightweight blueprint residual block (LBRB) to learn the high-frequency information efficiently for lightweight SISR. The LBRB with fewer parameters consists of more efficient convolution operations to extract high-frequency information which helps recover more visual details. In addition, we propose an effective feature fusion residual block (EFFRB) which is composed of three LBRBs with convolutional kernels of different sizes. The EFFRB, which is designed with effective multi-scale receptive fields, can availably extract and fuse multi-scale high-frequency information to obtain discriminative feature, and it further improves model performance. Extensive experiments show that our LBRN achieves superior reconstruction performance quantitatively and visually compared with other lightweight state-of-the-art SR methods.

RTEN-SR: A reference-based texture enhancement network for single image super-resolution

Most current super-resolution (SR) reconstruction methods suffer from edge blurring and insufficient detail reconstruction. To avoid these problems, this paper proposes a reference-based texture enhancement network for single image SR (RTEN-SR). Firstly, a preliminary reconstruction module (PRM) is constructed to learn the initial reconstructed high-resolution (HR) image features. Then, a multi-scale texture enhancement module (MTEM) based on the idea of texture transfer is designed to further supplement and enhance the texture details for the initial reconstructed HR images. To improve the feature learning ability of network, an efficient mixed attention module (EMAM) is constructed by combining channel attention and spatial attention. Meanwhile, the EMAM is integrated into the above two modules to enhance the important image features in the channel and spatial dimensions. Experimental results demonstrate that the proposed method can achieve better performance than state-of-the-art methods, effectively improving the objective indexes and visual results of reference-based image super-resolution (RefSR). For example, the proposed method outperforms the best RefSR method among the comparison methods by 0.38 dB in PSNR metric and 0.128 in SSIM metric.

Post-trained Convolution Networks for Single Image Super-resolution (Abstract Reprint)

A new method is proposed to increase the accuracy of the state-of-the-art single image super-resolution (SISR) using novel training procedure. The proposed method, named post-trained convolutional neural network (CNN), is carried out stochastic dual simplex algorithm (SDSA) in the last reconstruction layer. The method utilizes contextual information to update the last reconstruction layer of CNN. The extracted contextual information is projected to the last reconstructed layer by optimized weights and the bias is managed through SDSA. Post-trained CNN is applied to the very deep super-resolution (VDSR) method to show its performance. The quantitative and visual results demonstrate that the proposed post-trained VDSR (PTVDSR) exhibits excellent and competitive performance when compared with the VDSR and other super-resolution methods.

Dual contrastive attention-guided deformable convolutional network for single image super-resolution

With its powerful ability to model geometric transformations, the deformable convolutional network brings great improvements for single image super-resolution (SISR). Nevertheless, its location-variant sampling method leads to an escalation in spatial variance as the deformable convolutional layers are stacked, consequently resulting in limited performance. Hence, we propose a novel and effective approach called dual contrastive attention-guided deformable convolutional network (DCADCN) for SISR modeling. Specifically, we propose an attention-guided deformable convolutional module with joint inner and external attention mechanisms to fully exploit the correspondences between input and deformation features and preserve spatial characteristics to the extent possible. Additionally, we propose a dual mixed feature extractor consisting of two parallel sub-paths. This design allows for the learning of diverse and complementary spatial features. Furthermore, contrastive learning is applied to further amplify the role of key features and mitigate the interference of noisy features. Extensive experimental results demonstrate that DCADCN is capable of effectively handling classic SISR, SISR with blind noise, and real-world SISR tasks. Moreover, our method achieves comparable or even better performance with lower computational cost compared to state-of-the-art methods.

A Channel-Wise Multi-Scale Network for Single Image Super-Resolution

A Channel-Wise Multi-Scale Network for Single Image Super-Resolution

A Two-Branch Multiscale Residual Attention Network for Single Image Super-Resolution in Remote Sensing Imagery

A Two-Branch Multiscale Residual Attention Network for Single Image Super-Resolution in Remote Sensing Imagery

RefQSR: Reference-Based Quantization for Image Super-Resolution Networks.

Single image super-resolution (SISR) aims to reconstruct a high-resolution image from its low-resolution observation. Recent deep learning-based SISR models show high performance at the expense of increased computational costs, limiting their use in resource-constrained environments. As a promising solution for computationally efficient network design, network quantization has been extensively studied. However, existing quantization methods developed for SISR have yet to effectively exploit image self-similarity, which is a new direction for exploration in this study. We introduce a novel method called reference-based quantization for image super-resolution (RefQSR) that applies high-bit quantization to several representative patches and uses them as references for low-bit quantization of the rest of the patches in an image. To this end, we design dedicated patch clustering and reference-based quantization modules and integrate them into existing SISR network quantization methods. The experimental results demonstrate the effectiveness of RefQSR on various SISR networks and quantization methods.

Attention for Adaptive Convolution in Convolutional Neural Networks for Single Image Super-Resolution

Attention for Adaptive Convolution in Convolutional Neural Networks for Single Image Super-Resolution

CVANet: Cascaded visual attention network for single image super-resolution

Deep convolutional neural networks (DCNNs) have exhibited excellent feature extraction and detail reconstruction capabilities for single image super-resolution (SISR). Nevertheless, most previous DCNN-based methods do not fully utilize the complementary strengths between feature maps, channels, and pixels. Therefore, it hinders the ability of DCNNs to represent abundant features. To tackle the aforementioned issues, we present a Cascaded Visual Attention Network for SISR called CVANet, which simulates the visual attention mechanism of the human eyes to focus on the reconstruction process of details. Specifically, we first designed a trainable feature attention module (FAM) for feature-level attention learning. Afterward, we introduce a channel attention module (CAM) to reinforce feature maps under channel-level attention learning. Meanwhile, we propose a pixel attention module (PAM) that adaptively selects representative features from the previous layers, which are utilized to generate a high-resolution image. Satisfactory, our CVANet can effectively improve the resolution of images by exploring the feature representation capabilities of different modules and the visual perception properties of the human eyes. Extensive experiments with different methods on four benchmarks demonstrate that our CVANet outperforms the state-of-the-art (SOTA) methods in subjective visual perception, PSNR, and SSIM.The code will be made available https://github.com/WilyZhao8/CVANet.

CRUN: a super lightweight and efficient network for single-image super resolution

CRUN: a super lightweight and efficient network for single-image super resolution

MFFN: Multi‐path feedback fusion network for lightweight image super resolution

AbstractRecently, convolutional neural network (CNN) has shown great power in single image super resolution (SISR) reconstruction, and achieving significant improvements over traditional methods. Despite the great success of these CNN‐based methods, direct application of these methods to some edge devices is impractical due to the large computational overhead required. To address this problem, a novel, lightweight SISR network focusing on speed and accuracy, called the multi‐path feedback fusion network (MFFN), has been designed in this paper. Specifically, in order to extract features more effectively, the authors propose a novel fusion attention feedback block (FAFB) as the main building block of MFFN. The FAFB consists of a backbone branch and several hierarchical branches. The backbone branch is composed of stacked enhanced pixel attentional blocks (EPAB), which are responsible for incremental deep feature learning on the feature map. And the hierarchical branches are responsible for extracting feature maps with different sizes of receptive fields and fusing these feature maps with the features extracted from the trunk branches to achieve multi‐scale feature learning, which the authors refer to this design as the multi‐scale fusion block (MSFB). Extra enhancement information (EIE) is added to each EPAB input, which enables the backbone branch to learn more effectively. On the other hand, the outputs of the cascade branches are further complemented by an additional feedback fusion enhancement block (FFEB) before being fused with the output of the trunk branches to achieve more comprehensive and accurate feature learning. Numerous experiments have shown that MFFN achieves higher accuracy than other state‐of‐the‐art methods on benchmark test sets.

Deep recurrent residual channel attention network for single image super-resolution

Deep recurrent residual channel attention network for single image super-resolution

Recurrent Multi-scale Approximation-Guided Network for Single Image Super-Resolution

Single-image super-resolution (SISR)is an essential topic in computer vision applications. However, most CNN-based SISR approaches directly learn the relationship between low- and high-resolution images while ignoring the contextual texture and detail fidelity to explore super-resolution; thus, they hinder the representational power of CNNs and lead to the unrealistic, distorted reconstruction of edges and textures in the images. In this study, we propose a novel recurrent structure preservation mechanism with the integration and innovative use of multi-scale wavelet transform,Recurrent Multiscale Approximation-guided Network (RMANet), to recursively process the low-frequency and high-frequency sub-networks at each level separately. Unlike traditional wavelet transform, we propose a novelApproximation Level Preservation (ALP)architecture to import and learn the low-frequency sub-networks at each level. Through proposedApproximation level fusion (ALF)and inverse wavelet transform, rich image structures of low frequency at each level can be recursively restored and greatly preserved with the combination of ALP at each level. In addition, a novel low-frequency to high-frequencydetail enhancement (DE)mechanism is also proposed to solve the problem of detail distortion in high-frequency networks by transmitting low-frequency information to the high-frequency network. Finally, a joint loss function is used to balance low-frequency and high-frequency information with different degrees of fusion. In addition to correct restoration, image details are further enhanced by tuning different hyperparameters during training. Compared with the state-of-the-art approaches, the experimental results on synthetic and real datasets demonstrate that the proposed RMANet achieves better performance in visual presentation, especially in image edges and texture details.

Multi-scale convolutional attention network for lightweight image super-resolution

Convolutional neural network (CNN) based methods have recently achieved extraordinary performance in single image super-resolution (SISR) tasks. However, most existing CNN-based approaches increase the model’s depth by stacking massive kernel convolutions, bringing expensive computational costs and limiting their application in mobile devices with limited resources. Furthermore, large kernel convolutions are rarely used in lightweight super-resolution designs. To alleviate the above problems, we propose a multi-scale convolutional attention network (MCAN), a lightweight and efficient network for SISR. Specifically, a multi-scale convolutional attention (MCA) is designed to aggregate the spatial information of different large receptive fields. Since the contextual information of the image has a strong local correlation, we design a local feature enhancement unit (LFEU) to further enhance the local feature extraction. Extensive experimental results illustrate that our proposed MCAN can achieve better performance with lower model complexity compared with other state-of-the-art lightweight methods.

Single-Image Super-Resolution Challenges: A Brief Review

Single-image super-resolution (SISR) is an important task in image processing, aiming to achieve enhanced image resolution. With the development of deep learning, SISR based on convolutional neural networks has also gained great progress, but as the network deepens and the task of SISR becomes more complex, SISR networks become difficult to train, which hinders SISR from achieving greater success. Therefore, to further promote SISR, many challenges have emerged in recent years. In this review, we briefly review the SISR challenges organized from 2017 to 2022 and focus on the in-depth classification of these challenges, the datasets employed, the evaluation methods used, and the powerful network architectures proposed or accepted by the winners. First, depending on the tasks of the challenges, the SISR challenges can be broadly classified into four categories: classic SISR, efficient SISR, perceptual extreme SISR, and real-world SISR. Second, we introduce the datasets commonly used in the challenges in recent years and describe their characteristics. Third, we present the image evaluation methods commonly used in SISR challenges in recent years. Fourth, we introduce the network architectures used by the winners, mainly to explore in depth where the advantages of their network architectures lie and to compare the results of previous years’ winners. Finally, we summarize the methods that have been widely used in SISR in recent years and suggest several possible promising directions for future SISR.

Learnable Nonlocal Contrastive Network for Single Image Super-Resolution

Single image super-resolution (SISR) aims to recover a high-resolution image from a single low-resolution image. In recent years, SISR methods based on deep convolutional neural networks have achieved remarkable success, and some methods further improve the performance of the SISR model by introducing nonlocal attention into the model. However, most SISR methods that introduce nonlocal attention focus on more complex attention mechanisms and only use fixed functions for measurement when exploring image similarity. In addition, the model penalizes the algorithm in terms of loss when the output predicted by the model does not match the target data, even if this output is a potentially valid solution. To this end, we propose learnable nonlocal contrastive attention (LNLCA), which flexibly aggregates image features while maintaining linear computational complexity. Then, we introduce the adaptive target generator (ATG) model to address the problem of the single model training mode. Based on LNLCA, we construct a learnable nonlocal contrastive network (LNLCN). The experimental results demonstrate the effectiveness of the algorithm, which produces reconstructed images with more natural texture details.

Fast Non-Local Attention network for light super-resolution

Although convolutional neural network-based methods have achieved significant performance improvement for Single Image Super-Resolution (SISR), their vast computational cost hinders real-world environment application. Thus, the interest in light networks for SISR is rising. Since existing SISR light models mainly focus on extracting fine local features using convolution operation, they have a limitation in that networks hardly capture global information. To capture the long-range dependency, Non-Local (NL) attention and Transformers have been explored in the SISR task. However, they are still suffering from a balancing problem between performance and computational cost. In this paper, we propose Fast Non-Local attention NETwork (FNLNET) for a super light SISR, which can capture the global representation. To acquire global information, we propose The Fast Non-Local Attention (FNLA) module that has low computational complexity while capturing global representation that reflects long-distance relationships between patches. Then, FNLA requires only 16 times lower computational cost than conventional NL networks while improving performance. In addition, we propose a powerful module called Global Self-Intension Mining (GSIM) that fuses the multi-information resources such as local, and global representation. Our FNLNET shows outstanding performance with fewer parameters and computational costs in the experiments on the benchmark datasets against state-of-the-art light SISR models.

Single-image super-resolution via dynamic multi-task learning and multi-level mutual feature fusion

In recent years, deep neural networks have exhibited promising performance in the single-image super-resolution (SISR) task by designing complex network structures from the low-resolution (LR) images to the high-resolution (HR) images and learning the mapping function. However, the SISR task is a widely known ill-posed problem. Since it may map one LR image input to multiple HR images, there are always undesired structural distortions in the recovered SR images. We creatively propose a SISR network with dynamic multi-task learning and multi-level mutual feature fusion (DMMFSR), alleviating structural distortion problems while maintaining more edge structure. Specifically, we utilize a DML block (DMLB) to train low-frequency and high-frequency image information extraction networks so the two networks can mutually promote each other. In addition, we also use a multi-level mutual feature fusion block to fuse image and edge features at different levels. Extensive experiments demonstrate the effectiveness of our proposed DMMFSR. Meanwhile, our model achieves comparable peak signal-to-noise ratio and structural similarity scores on multiple SR benchmarks compared with state-of-the-art SR methods.