Super-resolution Image Reconstruction Research Articles

Edge-enhanced infrared image super-resolution reconstruction model under transformer

Infrared images have important applications in military, security and surveillance fields. However, limited by technical factors, the resolution of infrared images is generally low, which seriously limits the application and development of infrared images in various fields. To address the problem of difficult recovery of edge information and easy ringing effect in the super-resolution reconstruction process of infrared images, an edge-enhanced infrared image super-resolution reconstruction model TESR under transformer is proposed. The main structure of this model is transformer. First, in view of the problem of difficult recovery of edge information of infrared images, an edge detection auxiliary network is designed, which can obtain more accurate edge information from the input low-resolution images and enhance the edge details during image reconstruction; then, the CSWin Transformer is introduced to compute the self-attention of horizontal and vertical stripes in parallel, so as to increase the receptive field of the model and enable it to utilize features with higher semantic levels. The super-resolution reconstruction model proposed in this paper can extract more comprehensive image information, and at the same time, it can obtain more accurate edge information to enhance the texture details of super-resolution images, and achieve better reconstruction results.

Image Super-Resolution Reconstruction Method based on Improved Cyclic Generative Adversarial Network with Twin Attention Mechanism

Image Super-Resolution Reconstruction Method based on Improved Cyclic Generative Adversarial Network with Twin Attention Mechanism

Deep Residual-in-Residual Model-Based PET Image Super-Resolution with Motion Blur

Positron emission tomography (PET) is a non-invasive molecular imaging technique. The limited spatial resolution of PET images, due to technological and physical imaging constraints, directly affects the precise localization and interpretation of small lesions and biological processes. The super-resolution (SR) technique aims to enhance image quality by improving spatial resolution, thereby aiding clinicians in achieving more accurate diagnoses. However, most conventional SR methods rely on idealized degradation models and fail to effectively capture both low- and high-frequency information present in medical images. For the challenging SR reconstruction of PET images exhibiting motion-induced artefacts, a degradation model that better aligns with practical scanning scenarios was designed by us. Furthermore, we proposed a PET image SR method based on the deep residual-in-residual network (DRRN), focusing on the recovery of both low- and high-frequency information. By incorporating multi-level residual connections, our approach facilitates direct feature propagation across different network levels. This design effectively mitigates the lack of feature correlation between adjacent convolutional layers in deep networks. Our proposed method surpasses benchmark methods in both full-reference and no-reference metrics and subjective visual effects across small animal PET (SAPET), phantoms, and Alzheimer’s Disease Neuroimaging Initiative (ADNI) datasets. The experimental findings confirm the remarkable efficacy of DRRN in enhancing spatial resolution and mitigating blurring in PET images. In comparison to conventional SR techniques, this method demonstrates superior proficiency in restoring low-frequency structural texture information while simultaneously maintaining high-frequency details, thus showcasing exceptional multi-frequency information fusion capabilities.

A Novel Electrical Equipment Status Diagnosis Method Based on Super-Resolution Reconstruction and Logical Reasoning.

The accurate detection of electrical equipment states and faults is crucial for the reliable operation of such equipment and for maintaining the health of the overall power system. The state of power equipment can be effectively monitored through deep learning-based visual inspection methods, which provide essential information for diagnosing and predicting equipment failures. However, there are significant challenges: on the one hand, electrical equipment typically operates in complex environments, thus resulting in captured images that contain environmental noise, which significantly reduces the accuracy of state recognition based on visual perception. This, in turn, affects the comprehensiveness of the power system's situational awareness. On the other hand, visual perception is limited to obtaining the appearance characteristics of the equipment. The lack of logical reasoning makes it difficult for purely visual analysis to conduct a deeper analysis and diagnosis of the complex equipment state. Therefore, to address these two issues, we first designed an image super-resolution reconstruction method based on the Generative Adversarial Network (GAN) to filter environmental noise. Then, the pixel information is analyzed using a deep learning-based method to obtain the spatial feature of the equipment. Finally, by constructing the logic diagram for electrical equipment clusters, we propose an interpretable fault diagnosis method that integrates the spatial features and temporal states of the electrical equipment. To verify the effectiveness of the proposed algorithm, extensive experiments are conducted on six datasets. The results demonstrate that the proposed method can achieve high accuracy in diagnosing electrical equipment faults.

A robust feature matching algorithm based on adaptive feature fusion combined with image superresolution reconstruction

A robust feature matching algorithm based on adaptive feature fusion combined with image superresolution reconstruction

SIGAN: A Multi-Scale Generative Adversarial Network for Underwater Sonar Image Super-Resolution

Super-resolution (SR) is a technique that restores image details based on existing information, enhancing the resolution of images to prevent quality degradation. Despite significant achievements in deep-learning-based SR models, their application in underwater sonar scenarios is limited due to the lack of underwater sonar datasets and the difficulty in recovering texture details. To address these challenges, we propose a multi-scale generative adversarial network (SIGAN) for super-resolution reconstruction of underwater sonar images. The generator is built on a residual dense network (RDN), which extracts rich local features through densely connected convolutional layers. Additionally, a Convolutional Block Attention Module (CBAM) is incorporated to capture detailed texture information by focusing on different scales and channels. The discriminator employs a multi-scale discriminative structure, enhancing the detail perception of both generated and high-resolution (HR) images. Considering the increased noise in super-resolved sonar images, our loss function emphasizes the PSNR metric and incorporates the L2 loss function to improve the quality of the output images. Meanwhile, we constructed a dataset for side-scan sonar experiments (DNASI-I). We compared our method with the current state-of-the-art super-resolution image reconstruction methods on the public dataset KLSG-II and our self-built dataset DNASI-I. The experimental results show that at a scale factor of 4, the average PSNR value of our method was 3.5 higher than that of other methods, and the accuracy of target detection using the super-resolution reconstructed images can be improved to 91.4%. Through subjective qualitative comparison and objective quantitative analysis, we demonstrated the effectiveness and superiority of the proposed SIGAN in the super-resolution reconstruction of side-scan sonar images.

A Novel Simulation Method for 3D Digital-Image Correlation: Combining Virtual Stereo Vision and Image Super-Resolution Reconstruction.

3D digital-image correlation (3D-DIC) is a non-contact optical technique for full-field shape, displacement, and deformation measurement. Given the high experimental hardware costs associated with 3D-DIC, the development of high-fidelity 3D-DIC simulations holds significant value. However, existing research on 3D-DIC simulation was mainly carried out through the generation of random speckle images. This study innovatively proposes a complete 3D-DIC simulation method involving optical simulation and mechanical simulation and integrating 3D-DIC, virtual stereo vision, and image super-resolution reconstruction technology. Virtual stereo vision can reduce hardware costs and eliminate camera-synchronization errors. Image super-resolution reconstruction can compensate for the decrease in precision caused by image-resolution loss. An array of software tools such as ANSYS SPEOS 2024R1, ZEMAX 2024R1, MECHANICAL 2024R1, and MULTIDIC v1.1.0 are used to implement this simulation. Measurement systems based on stereo vision and virtual stereo vision were built and tested for use in 3D-DIC. The results of the simulation experiment show that when the synchronization error of the basic stereo-vision system (BSS) is within 10-3 time steps, the reconstruction error is within 0.005 mm and the accuracy of the virtual stereo-vision system is between the BSS's synchronization error of 10-7 and 10-6 time steps. In addition, after image super-resolution reconstruction technology is applied, the reconstruction error will be reduced to within 0.002 mm. The simulation method proposed in this study can provide a novel research path for existing researchers in the field while also offering the opportunity for researchers without access to costly hardware to participate in related research.

Super-resolution reconstruction of images based on residual dual-path interactive fusion combined with attention

Super-resolution reconstruction of images based on residual dual-path interactive fusion combined with attention

AFFNet: adversarial feature fusion network for super-resolution image reconstruction in remote sensing images

AFFNet: adversarial feature fusion network for super-resolution image reconstruction in remote sensing images

Super-resolution reconstruction of ultrasound image using a modified diffusion model

Objective. This study aims to perform super-resolution (SR) reconstruction of ultrasound images using a modified diffusion model, designated as the diffusion model for ultrasound image super-resolution (DMUISR). SR involves converting low-resolution images to high-resolution ones, and the proposed model is designed to enhance the suitability of diffusion models for this task in the context of ultrasound imaging. Approach. DMUISR incorporates a multi-layer self-attention (MLSA) mechanism and a wavelet-transform based low-resolution image (WTLR) encoder to enhance its suitability for ultrasound image SR tasks. The model takes interpolated and magnified images as input and outputs high-quality, detailed SR images. The study utilized 1,334 ultrasound images from the public fetal head-circumference dataset (HC18) for evaluation. Main results. Experiments were conducted at 2× , 4× , and 8× magnification factors. DMUISR outperformed mainstream ultrasound SR methods (Bicubic, VDSR, DECUSR, DRCN, REDNet, SRGAN) across all scales, providing high-quality images with clear structures and rich detailed textures in both hard and soft tissue regions. DMUISR successfully accomplished multiscale SR reconstruction while suppressing over-smoothing and mode collapse problems. Quantitative results showed that DMUISR achieved the best performance in terms of learned perceptual image patch similarity, with a significant decrease of over 50% at all three magnification factors (2× , 4× , and 8× ), as well as improvements in peak signal-to-noise ratio and structural similarity index measure. Ablation experiments validated the effectiveness of the MLSA mechanism and WTLR encoder in improving DMUISR’s SR performance. Furthermore, by reducing the number of diffusion steps, the computational time of DMUISR was shortened to nearly one-tenth of its original while maintaining image quality without significant degradation. Significance. This study demonstrates that the modified diffusion model, DMUISR, provides superior performance for SR reconstruction of ultrasound images and has potential in improving imaging quality in the medical ultrasound field.

SR-RDFAN-LOG: Arbitrary-scale logging image super-resolution reconstruction based on residual dense feature aggregation

In the process of oil and gas exploration and development, ultrasonic logging imaging technology visually displays the lithology, structure, fractures, pores, and other characteristics of the wellbore. This technology has been widely applied in the exploration and development of oil and gas resources. However, the low circumferential sampling rate negatively affects the imaging quality and resolution during drilling logging. To address the issues of low resolution and blurriness in ultrasonic logging images caused by these factors, as well as the limitation of existing super-resolution reconstruction algorithms that can only reconstruct fixed scales, this paper presents an Image Super-Resolution Reconstruction Network called Residual Dense Feature Aggregation (SR-RDFAN-LOG), which is specifically designed to improve ultrasonic logging image resolution. The proposed network consists of two stages, including encoding and decoding. In the encoding stage, a local feature extraction module is initially designed to integrate contextual features. Subsequently, a residual dense module is introduced for extracting deep features. Finally, a spatial attention module is constructed to focus on high-frequency details. In the decoding stage, a multilayer perceptron is used to decode the feature maps and achieve super-resolution reconstruction at arbitrary scales. The method is tested on an ultrasonic logging images test set with in-distribution scale factors of × 2 and × 4, and out-of-distribution scale factors of × 8 and × 16. When the scaling factors are × 2 and × 4, compared to the best-performing method, the peak signal-to-noise ratio (PSNR) increased by 0.4892 dB and 0.3849 dB, respectively, the structural similarity (SSIM) values increased by 0.0002 and 0.0012, respectively, and the learned perceptual image patch similarity (LPIPS) decreased by 0.0001 and 0.0008, respectively. Compared to the bicubic interpolation method, the LPIPS decreased by 0.0001 and 0.0008 at scale factors of × 8 and × 16, PSNR increased by 1.9614 dB and 2.1002 dB, respectively, and SSIM values increased by 0.0476 and 0.033, respectively. The LPIPS values decreased by 0.2233 and 0.1070, respectively. The experimental results demonstrate that our method outperforms other mainstream methods, achieves super-resolution reconstruction at arbitrary scale, and provides support for subsequent fracture-cave evaluation and oil and gas exploration.

Super-Resolution Image Reconstruction Method between Sentinel-2 and Gaofen-2 Based on Cascaded Generative Adversarial Networks

Due to the multi-scale and spectral features of remote sensing images compared to natural images, there are significant challenges in super-resolution reconstruction (SR) tasks. Networks trained on simulated data often exhibit poor reconstruction performance on real low-resolution (LR) images. Additionally, compared to natural images, remote sensing imagery involves fewer high-frequency components in network construction. To address the above issues, we introduce a new high–low-resolution dataset GF_Sen based on GaoFen-2 and Sentinel-2 images and propose a cascaded network CSWGAN combined with spatial–frequency features. Firstly, based on the proposed self-attention GAN (SGAN) and wavelet-based GAN (WGAN) in this study, the CSWGAN combines the strengths of both networks. It not only models long-range dependencies and better utilizes global feature information, but also extracts frequency content differences between different images, enhancing the learning of high-frequency information. Experiments have shown that the networks trained based on the GF_Sen can achieve better performance than those trained on simulated data. The reconstructed images from the CSWGAN demonstrate improvements in the PSNR and SSIM by 4.375 and 4.877, respectively, compared to the relatively optimal performance of the ESRGAN. The CSWGAN can reflect the reconstruction advantages of a high-frequency scene and provides a working foundation for fine-scale applications in remote sensing.

Reference-based super-resolution reconstruction of remote sensing images based on a coarse-to-fine feature matching transformer

Remote sensing image super-resolution reconstruction technology mines the deep details of remote sensing image, which has been widely used in the fields of intelligent and precise agriculture, intelligent transportation, earth surface object recognition, and so on. To obtain more detailed information,we design a reference-based super-resolution reconstruction network. Firstly, the coarse-to-fine feature matching strategy is adopted for the features of the input image. Global coarse matching is performed on the center patch of each block, and then pixel-level local fine matching is performed on the edge patches of the block. This both reduces the amount of computation and improves the matching accuracy. A threshold is set to determine whether the feature matching results meet the criteria for feature transfer. Finally, different scale features are fused through several convolutional layers and sampling operations, obtaining the reconstruction features after a fourfold increase in resolution. The ultimate super-resolution image is generated through a decoder. We have performed training and testing on remote sensing datasets. Compared to the current state-of-the-art methods, our proposed method is visually more details and outperforms other methods in terms of objective evaluation metrics.

Image super‐resolution reconstruction based on implicit image functions

AbstractImage super‐resolution (SR) reconstruction is a key technique for improving image quality and details. Conventional methods are frequently limited by interpolation, filtering, or statistical approaches; thus, they are unable to reconstruct high‐quality continuously enlarged images with detailed information. This study proposes an image SR reconstruction network model, called LALNet, based on implicit image functions and residual multilayered perceptron (RAMLP) with an attention mechanism. Through the implicit image function and RAMLP + attention, high‐quality SR reconstruction with continuous scale factors is achieved, and LALNets can run on embedded edge computing platforms. This method exhibits the following advantages: lightweight network structure reduces computing requirements, introduction of implicit image functions and RAMLP improves reconstruction quality, and attention mechanism suppresses artefacts and distortions. Experimental results show that LALNet outperforms traditional and other deep learning methods in terms of reconstruction performance and computational efficiency. This research provides new ideas and methods for the further development of the field of image SR reconstruction.

Super-resolution reconstruction of single image for latent features

Single-image super-resolution (SISR) typically focuses on restoring various degraded low-resolution (LR) images to a single high-resolution (HR) image. However, during SISR tasks, it is often challenging for models to simultaneously maintain high quality and rapid sampling while preserving diversity in details and texture features. This challenge can lead to issues such as model collapse, lack of rich details and texture features in the reconstructed HR images, and excessive time consumption for model sampling. To address these problems, this paper proposes a Latent Feature-oriented Diffusion Probability Model (LDDPM). First, we designed a conditional encoder capable of effectively encoding LR images, reducing the solution space for model image reconstruction and thereby improving the quality of the reconstructed images. We then employed a normalized flow and multimodal adversarial training, learning from complex multimodal distributions, to model the denoising distribution. Doing so boosts the generative modeling capabilities within a minimal number of sampling steps. Experimental comparisons of our proposed model with existing SISR methods on mainstream datasets demonstrate that our model reconstructs more realistic HR images and achieves better performance on multiple evaluation metrics, providing a fresh perspective for tackling SISR tasks.

Lightweight interactive feature inference network for single-image super-resolution

The emergence of convolutional neural network (CNN) and transformer has recently facilitated significant advances in image super-resolution (SR) tasks. However, these networks commonly construct complex structures, having huge model parameters and high computational costs, to boost reconstruction performance. In addition, they do not consider the structural prior well, which is not conducive to high-quality image reconstruction. In this work, we devise a lightweight interactive feature inference network (IFIN), complementing the strengths of CNN and Transformer, for effective image SR reconstruction. Specifically, the interactive feature aggregation module (IFAM), implemented by structure-aware attention block (SAAB), Swin Transformer block (SWTB), and enhanced spatial adaptive block (ESAB), serves as the network backbone, progressively extracts more dedicated features to facilitate the reconstruction of high-frequency details in the image. SAAB adaptively recalibrates local salient structural information, and SWTB effectively captures rich global information. Further, ESAB synergetically complements local and global priors to ensure the consistent fusion of diverse features, achieving high-quality reconstruction of images. Comprehensive experiments reveal that our proposed networks attain state-of-the-art reconstruction accuracy on benchmark datasets while maintaining low computational demands. Our code and results are available at: https://github.com/wwaannggllii/IFIN.

Lightweight Super-Resolution Generative Adversarial Network for SAR Images

Due to a unique imaging mechanism, Synthetic Aperture Radar (SAR) images typically exhibit degradation phenomena. To enhance image quality and support real-time on-board processing capabilities, we propose a lightweight deep generative network framework, namely, the Lightweight Super-Resolution Generative Adversarial Network (LSRGAN). This method introduces Depthwise Separable Convolution (DSConv) in residual blocks to compress the original Generative Adversarial Network (GAN) and uses the SeLU activation function to construct a lightweight residual module (LRM) suitable for SAR image characteristics. Furthermore, we combine the LRM with an optimized Coordinated Attention (CA) module, enhancing the lightweight network’s capability to learn feature representations. Experimental results on spaceborne SAR images demonstrate that compared to other deep generative networks focused on SAR image super-resolution reconstruction, LSRGAN achieves compression ratios of 74.68% in model storage requirements and 55.93% in computational resource demands. In this work, we significantly reduce the model complexity, improve the quality of spaceborne SAR images, and validate the effectiveness of the SAR image super-resolution algorithm as well as the feasibility of real-time on-board processing technology.

Image Super-Resolution Reconstruction in Sports Scenarios and Its Application in Motion Analysis

Image Super-Resolution Reconstruction in Sports Scenarios and Its Application in Motion Analysis

Dual-branch feature encoding framework for infrared images super-resolution reconstruction

Infrared thermal imaging is a passive non-contact detection and identification technology, which is not subject to electromagnetic infection and good concealment, is widely used in military and commercial fields. However, due to the limitations of the existing infrared imaging system mechanisms, the spatial resolution of the acquired infrared images is low and the edge details are blurred, which in turn leads to poor performance in downstream missions based on infrared images. In this paper, in order to better solve the above problems, we propose a new super-resolution reconstruction framework for infrared images, called DBFE, which extracts and retains abundant structure and textual information for robust infrared image high-resolution reconstruction with a novel structure-textual encoder module. Extensive experiment demonstrates that our proposed method achieves significantly superior contraband high-resolution reconstruction results on the multiple dataset compared to progressive methods for high resolution infrared image reconstruction, effectively proving the practicability of the method proposed in this paper.

Infrared image super-resolution reconstruction based on residual fast fourier transform

Infrared image super-resolution reconstruction based on residual fast fourier transform