Super-resolution Image Reconstruction Research Articles

Lightweight Super-Resolution Reconstruction Vision Transformers of Remote Sensing Image Based on Structural Re-Parameterization

In recent times, remote sensing image super-resolution reconstruction technology based on deep learning has experienced rapid development. However, most algorithms in this domain concentrate solely on enhancing the super-resolution network’s performance while neglecting the equally crucial aspect of inference speed. In this study, we propose a method for lightweight super-resolution reconstruction of remote sensing images, termed SRRepViT. This approach reduces model parameters and floating-point operations during inference through parameter equivalent transformation. Using the RSSOD remote sensing dataset as our benchmark dataset, we compared the reconstruction performance, inference time, and model size of SRRepViT with other classical methods. Compared to the lightweight model ECBSR, SRRepViT exhibits slightly improved reconstruction performance while reducing inference time by 16% and model parameters by 34%, respectively. Moreover, compared to other classical super-resolution reconstruction methods, the SRRepViT model achieves similar reconstruction performance while reducing model parameters by 98% and increasing inference speed by 90% for a single remote sensing image.

Retracted: Image Super-Resolution Reconstruction Method for Lung Cancer CT-Scanned Images Based on Neural Network.

[This retracts the article DOI: 10.1155/2022/3543531.].

Multi-scale dense spatially-adaptive residual distillation network for lightweight underwater image super-resolution

Underwater images are typically of poor quality, lacking texture and edge information, and are blurry and full of artifacts, which restricts the performance of subsequent tasks such as underwater object detection, and path planning for underwater unmanned submersible vehicles (UUVs). Additionally, the limitation of underwater equipment, most existing image enhancement and super-resolution methods cannot be implemented directly. Hence, developing a weightless technique for improving the resolution of submerged images while balancing performance and parameters is vital. In this paper, a multi-scale dense spatially-adaptive residual distillation network (MDSRDN) is proposed aiming at obtaining high-quality (HR) underwater images with odd parameters and fast running time. In particular, a multi-scale dense spatially-adaptive residual distillation module (MDSRD) is developed to facilitate the multi-scale global-to-local feature extraction like a multi-head transformer and enriching spatial attention maps. By introducing a spatial feature transformer layer (SFT layer) and residual spatial-adaptive feature attention (RSFA), an enhancing attention map for spatially-adaptive feature modulation is generated. Furthermore, to maintain the network lightweight enough, blue separable convolution (BS-Conv) and distillation module are applied. Extensive experimental results illustrate the superiority of MDSDRN in underwater image super-resolution reconstruction, which can achieve a great balance between parameters (only 0.32M), multi-adds (only 13G), and performance (26.38 dB on PSNR in USR-248) with the scale of ×4.

MICU: Image super-resolution via multi-level information compensation and U-net

Recently, Deep Convolutional Neural Networks have demonstrated high-quality reconstruction in image super-resolution procedure. In this paper, we propose improved image super-resolution reconstruction via multi-level information compensation and U-Net network to address the problem that the image super-resolution reconstruction algorithm based on deep neural networks tends to lose feature information in the feature extraction process, resulting in the lack of texture and edge details in the reconstructed image. Firstly, we design the U-net like network for image super-resolution reconstruction, which performs multi-level feature extraction and channel compression for the input features through the down-channel branch. It fuses the compressed features and extracts the correlation features of different channels through the bottom module, and performs multi-level feature extraction and channel recovery for the compressed correlation features through the up-channel branch. The multi-level information compensation model is then designed to compensate for the information lost in the channel compression process and the information that is difficult to recover in the channel recovery process of U-net like network. The experimental results can show that the proposed algorithm achieves a significant improvement in Peak Signal-to-Noise Ratio and Structure Similarity Index and visual effect compared with the state-of-arts algorithms. The average experimental results of PSNR from proposed method had improved by 1.63 dB, 1.53 dB, 0.97 dB and 0.94 dB compared to SRCNN, HAT, DAT and CARN, respectively.

Image super-resolution reconstruction based on deep dictionary learning and A+

Image super-resolution reconstruction based on deep dictionary learning and A+

Channel-Specific and Spatial Residual Attention Network for Medical Image Denoising.

Medical image quality is crucial for physicians to ensure accurate diagnosis and therapeutic strategies. However, due to the interference of noise, there are often various types of noise and artifacts in medical images. This not only damages the visual clarity of images, but also reduces the accuracy of information extraction. Considering that the edges of medical images are rich in high-frequency information, to enhance the quality of medical images, a dual attention mechanism, the channel-specific and spatial residual attention network (CSRAN) in the U-Net framework is proposed. The CSRAN seamlessly integrates the U-Net architecture with channel-wise and spatial feature attention (CSAR) modules, as well as low-frequency channel attention modules. Combined with the two modules, the ability of medical image processing to extract high-frequency features is improved, thereby significantly improving the edge effects and clarity of reconstructed images. This model can present better performance in capturing high-frequency information and spatial structures in medical image denoising and super-resolution reconstruction tasks. It cannot only enhance the ability to extract high-frequency features and strengthen its nonlinear representation capability, but also endow strong edge detection capabilities of the model. The experimental results further prove the superiority of CSRAN in medical image denoising and super-resolution reconstruction tasks.

Image Super-Resolution Reconstruction by Huber Regularization and Tailored Finite Point Method

Image Super-Resolution Reconstruction by Huber Regularization and Tailored Finite Point Method

Fractional-Order Super-Resolution Reconstruction Algorithm for GM-APD Lidar Distance Images Based on Convex Set Projection

The main objective of this paper is to solve the problem of low resolution of target distance images obtained by GM-APD lidar. Towards overcoming this problem, this paper proposes a fractional-order super-resolution reconstruction algorithm for GM-APD lidar distance images based on convex set projection. Firstly, the first low-resolution image is upsampled three times to obtain a high-resolution reference image. Second, starting from the image degradation model, the transformation model between the low-resolution image and the high-resolution reference image is constructed. Then the high-resolution reference image is convolved using the G-L fractional order differential operator, and the estimated low-resolution image is obtained by combining the point spread function. Finally, the difference between the remaining low-resolution images and the estimated low-resolution image is calculated, and the high-resolution reference image is corrected to obtain the final high-resolution distance image. In addition to solving the edge blurring problem of the original convex set projection algorithm, the edge structure of the image is enhanced and the image quality is improved. Simulation and experimental results show that the proposed algorithm improves the information entropy by at least 24.190% and the average gradient by at least 10.812%, and achieves the super-resolution reconstruction of GM-APD LIDAR target distance images.

Super-Resolution Reconstruction of Cell Images Based on Generative Adversarial Networks

Super-Resolution Reconstruction of Cell Images Based on Generative Adversarial Networks

Super-Resolution Reconstruction of Seismic Images Based on Deep Residual Channel Attention Mechanism

Super-Resolution Reconstruction of Seismic Images Based on Deep Residual Channel Attention Mechanism

A Lightweight Pyramid Feature Fusion Network for Single Image Super-Resolution Reconstruction

A Lightweight Pyramid Feature Fusion Network for Single Image Super-Resolution Reconstruction

Images super-resolution reconstruction of transposed self-attention with local feature enhancement

Images super-resolution reconstruction of transposed self-attention with local feature enhancement

Single image super-resolution reconstruction based on split-attention networks

Single image super-resolution reconstruction based on split-attention networks

Cross-scale transformer image super-resolution reconstruction with fusion channel attention

Cross-scale transformer image super-resolution reconstruction with fusion channel attention

The super-resolution reconstruction algorithm of multi-scale dilated convolution residual network.

Aiming at the problems of traditional image super-resolution reconstruction algorithms in the image reconstruction process, such as small receptive field, insufficient multi-scale feature extraction, and easy loss of image feature information, a super-resolution reconstruction algorithm of multi-scale dilated convolution network based on dilated convolution is proposed in this paper. First, the algorithm extracts features from the same input image through the dilated convolution kernels of different receptive fields to obtain feature maps with different scales; then, through the residual attention dense block, further obtain the features of the original low resolution images, local residual connections are added to fuse multi-scale feature information between multiple channels, and residual nested networks and jump connections are used at the same time to speed up deep network convergence and avoid network degradation problems. Finally, deep network extraction features, and it is fused with input features to increase the nonlinear expression ability of the network to enhance the super-resolution reconstruction effect. Experimental results show that compared with Bicubic, SRCNN, ESPCN, VDSR, DRCN, LapSRN, MemNet, and DSRNet algorithms on the Set5, Set14, BSDS100, and Urban100 test sets, the proposed algorithm has improved peak signal-to-noise ratio and structural similarity, and reconstructed images. The visual effect is better.

Image Super-Resolution Reconstruction Based on Enhanced Attention Mechanism and Gradient Correlation Loss

Image Super-Resolution Reconstruction Based on Enhanced Attention Mechanism and Gradient Correlation Loss

A DEM Image Superresolution Reconstruction Method Based on the Texture Transfer of High-Resolution Remote Sensing Images

A DEM Image Superresolution Reconstruction Method Based on the Texture Transfer of High-Resolution Remote Sensing Images

MCFPN: Multi-Path Cross Fusion Pyramid-Like Network for Image Super-Resolution Reconstruction

MCFPN: Multi-Path Cross Fusion Pyramid-Like Network for Image Super-Resolution Reconstruction

A Recurrent Feedback Hyperspectral Image Super-Resolution Reconstruction Method by Using Self-Attention-Based Pixel Awareness

A Recurrent Feedback Hyperspectral Image Super-Resolution Reconstruction Method by Using Self-Attention-Based Pixel Awareness

Image Super-Resolution Reconstruction Algorithm Based on Adaptive Two-Branch Block

Image Super-Resolution Reconstruction Algorithm Based on Adaptive Two-Branch Block