Super-resolution Reconstruction Research Articles

Super-resolution reconstruction of underwater polarized images with a fused attention mechanism.

The polarization imaging technique leverages the disparity between target and background polarization information to mitigate the impact of backward scattered light, thereby enhancing image quality. However, the imaging model of this method exhibits limitations in extracting inter-image features, resulting in less-than-optimal outcomes in turbid underwater environments. In recent years, machine learning methodologies, particularly neural networks, have gained traction. These networks, renowned for their superior fitting capabilities, can effectively extract information from multiple images. The incorporation of an attention mechanism significantly augments the capacity of neural networks to extract inter-image correlation attributes, thereby mitigating the constraints of polarization imaging methods to a certain degree. To enhance the efficacy of polarization imaging in complex underwater environments, this paper introduces a super-resolution network with an integrated attention mechanism, termed as SRGAN-DP. This network is a fusion of an enhanced SRGAN network and the high-performance deep pyramidal split attention (DPSA) module, also proposed in this paper. SRGAN-DP is employed to perform high-resolution reconstruction of the underwater polarimetric image dataset, constructed specifically for this study. A comparative analysis with existing algorithms demonstrates that our proposed algorithm not only produces superior images but also exhibits robust performance in real-world environments.

Multi-level feature extraction and reconstruction for 3D MRI image super-resolution

Magnetic resonance imaging (MRI) is an essential radiology technique in clinical diagnosis, but its spatial resolution may not suffice to meet the growing need for precise diagnosis due to hardware limitations and thicker slice thickness. Therefore, it is crucial to explore suitable methods to increase the resolution of MRI images. Recently, deep learning has yielded many impressive results in MRI image super-resolution (SR) reconstruction. However, current SR networks mainly use convolutions to extract relatively single image features, which may not be optimal for further enhancing the quality of image reconstruction. In this work, we propose a multi-level feature extraction and reconstruction (MFER) method to restore the degraded high-resolution details of MRI images. Specifically, to comprehensively extract different types of features, we design the triple-mixed convolution by leveraging the strengths and uniqueness of different filter operations. For the features of each level, we then apply deconvolutions to upsample them separately at the tail of the network, followed by the feature calibration of spatial and channel attention. Besides, we also use a soft cross-scale residual operation to improve the effectiveness of parameter optimization. Experiments on lesion-free and glioma datasets indicate that our method obtains superior quantitative performance and visual effects when compared with state-of-the-art MRI image SR methods.

A Contraband Detection Scheme in X-ray Security Images Based on Improved YOLOv8s Network Model.

X-ray inspections of contraband are widely used to maintain public transportation safety and protect life and property when people travel. To improve detection accuracy and reduce the probability of missed and false detection, a contraband detection algorithm YOLOv8s-DCN-EMA-IPIO* based on YOLOv8s is proposed. Firstly, the super-resolution reconstruction method based on the SRGAN network enhances the original data set, which is more conducive to model training. Secondly, DCNv2 (deformable convolution net v2) is introduced in the backbone network and merged with the C2f layer to improve the ability of the feature extraction and robustness of the model. Then, an EMA (efficient multi-scale attention) mechanism is proposed to suppress the interference of complex background noise and occlusion overlap in the detection process. Finally, the IPIO (improved pigeon-inspired optimization), which is based on the cross-mutation strategy, is employed to maximize the convolutional neural network's learning rate to derive the optimal group's weight information and ultimately improve the model's detection and recognition accuracy. The experimental results show that on the self-built data set, the mAP (mean average precision) of the improved model YOLOv8s-DCN-EMA-IPIO* is 73.43%, 3.98% higher than that of the original model YOLOv8s, and the FPS is 95, meeting the deployment requirements of both high precision and real-time.

A Framework for Reconstructing Super-Resolution Magnetic Resonance Images from Sparse Raw Data Using Multilevel Generative Methods

Super-resolution magnetic resonance (MR) scans give anatomical data for quantitative analysis and treatment. The use of convolutional neural networks (CNNs) in image processing and deep learning research have led to super-resolution reconstruction methods based on deep learning. The study offers a G-guided generative multilevel network for training 3D neural networks with poorly sampled MR input data. The author suggest using super-resolution reconstruction (SRR) and modified sparse sampling to address these issues. Image-based Wasserstein GANs retain k-space data sparsity. Wasserstein Generative Adversarial Networks (WGANs) store and represent picture space knowledge. The method obtains null-valued k-space data and repairs fill gaps in the dataset to preserve data integrity. The proposed reconstruction method processes raw data samples and is able to perform subspace synchronization, deblurring, denoising, motion estimation, and super-resolution image production. The suggested algorithm uses different preprocessing methods to deblur and denoise datasets. Preliminary trials contextualize and speed up assessments. Results indicate that reconstructed pictures have better high-frequency features than sophisticated multi-frame techniques. This is supported by rising PSNR, MAE, and IEM measurements. A k-space correction block improves GAN network refinement learning in the suggested method. This block improves the network’s ability to avoid unnecessary data, speeding reconstruction. A k-space correction module can limit the generator’s output to critical lines, allowing the reconstruction of only missing lines. This improves convergence and speeds rebuilding. This study shows that this strategy reduces aliasing artifacts better than contemporaneous and noniterative methods.

A Lightweight Image Super-Resolution Reconstruction Algorithm Based on the Residual Feature Distillation Mechanism.

In recent years, the development of image super-resolution (SR) has explored the capabilities of convolutional neural networks (CNNs). The current research tends to use deeper CNNs to improve performance. However, blindly increasing the depth of the network does not effectively enhance its performance. Moreover, as the network depth increases, more issues arise during the training process, requiring additional training techniques. In this paper, we propose a lightweight image super-resolution reconstruction algorithm (SISR-RFDM) based on the residual feature distillation mechanism (RFDM). Building upon residual blocks, we introduce spatial attention (SA) modules to provide more informative cues for recovering high-frequency details such as image edges and textures. Additionally, the output of each residual block is utilized as hierarchical features for global feature fusion (GFF), enhancing inter-layer information flow and feature reuse. Finally, all these features are fed into the reconstruction module to restore high-quality images. Experimental results demonstrate that our proposed algorithm outperforms other comparative algorithms in terms of both subjective visual effects and objective evaluation quality. The peak signal-to-noise ratio (PSNR) is improved by 0.23 dB, and the structural similarity index (SSIM) reaches 0.9607.

Heat Transfer-Inspired Network for Image Super-Resolution Reconstruction.

Image super-resolution (SR) is a critical image preprocessing task for many applications. How to recover features as accurately as possible is the focus of SR algorithms. Most existing SR methods tend to guide the image reconstruction process with gradient maps, frequency perception modules, etc. and improve the quality of recovered images from the perspective of enhancing edges, but rarely optimize the neural network structure from the system level. In this article, we conduct an in- depth exploration for the inner nature of the SR network structure. In light of the consistency between thermal particles in the thermal field and pixels in the image domain, we propose a novel heat-transfer-inspired network (HTI-Net) for image SR reconstruction based on the theoretical basis of heat transfer. With the finite difference theory, we use a second-order mixed-difference equation to redesign the residual network (ResNet), which can fully integrate multiple information to achieve better feature reuse. In addition, according to the thermal conduction differential equation (TCDE) in the thermal field, the pixel value flow equation (PVFE) in the image domain is derived to mine deep potential feature information. The experimental results on multiple standard databases demonstrate that the proposed HTI-Net has superior edge detail reconstruction effect and parameter performance compared with the existing SR methods. The experimental results on the microscope chip image (MCI) database consisting of realistic low-resolution (LR) and high-resolution (HR) images show that the proposed HTI-Net for image SR reconstruction can improve the effectiveness of the hardware Trojan detection system.

Transformer and GAN-Based Super-Resolution Reconstruction Network for Medical Images

Transformer and GAN-Based Super-Resolution Reconstruction Network for Medical Images

UFSRNet: U-shaped face super-resolution reconstruction network based on wavelet transform

UFSRNet: U-shaped face super-resolution reconstruction network based on wavelet transform

Super-resolution image reconstruction of a neutron thick pinhole imaging system using image denoiser prior based on half quadratic splitting method

Thick pinhole imaging system is a powerful tool to diagnose intense pulsed radiation sources. However, high spatial resolution of a thick pinhole imaging system cannot be achieved due to the limitation of field of view (FOV) and detection efficiency. In this paper, we propose a novel super-resolution image reconstruction algorithm to improve the spatial resolution of a practical thick pinhole imaging system designed for 14.1 MeV neutrons with 100 mm FOV. Point spread function (PSF) of the imaging system is obtained through complete simulation methods, indicating that the spatial resolution of the imaging system is about 500 μ m. A 4 × super-resolution image reconstruction algorithm using image denoiser as implicit image prior, named image denoiser prior, based on half quadratic splitting (HQS) method is firstly applied to restore more details of neutron sources. The results show that the proposed algorithm performs better than the Richardson-Lucy (RL) algorithm with fewer artifacts and sharper boundaries in reconstructed images. Notably, at a low noise level, the proposed algorithm can surpass the Nyquist sampling limit of 200 μ m, which is determined by the equivalent camera pixel size, to achieve a spatial resolution of 150 μ m.

Image Super-Resolution Reconstruction Model Based on Multi-Feature Fusion

Due to the limitations of imaging equipment and image transmission conditions on daily image acquisition, the images acquired are usually low-resolution images, and it will cost a lot of time and economic costs to increase image resolution by upgrading hardware equipment. In this paper, we propose an image super-resolution reconstruction algorithm based on spatio-temporal-dependent residual network MSRN, which fuses multiple features. The algorithm uses the surface feature extraction module to extract the input features of the image, and then uses the deep residual aggregation module to adaptively learn the deep features, and then fuses multiple features and learns the global residual. Finally, the high-resolution image is obtained through the up-sampling module and the reconstruction module. In the model structure, different convolution kernels and jump connections are used to extract more high-frequency information, and spatio-temporal attention mechanism is introduced to focus on more image details. The experimental results show that compared with SRGAN, VDSR and Laplacian Pyramid SRN, the proposed algorithm finally achieves better reconstruction effect, and the image texture details are clearer under different scaling factors. In objective evaluation, the peak signal-to-noise ratio (PSNR) and structure similarity (SSIM) of the proposed algorithm are improved compared with SRGAN.

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.

A proposed video super-resolution reconstruction strategy using wavelet multi-scale convolutional neural networks

A proposed video super-resolution reconstruction strategy using wavelet multi-scale convolutional neural networks

A Super-Resolution Reconstruction Method for Infrared Polarization Images with Sparse Representation of Over-Complete Basis Sets

The spatial resolution of an infrared focal plane polarization detection system is limited by the structure of the detector, resulting in lower resolution than the actual array size. To overcome this limitation and improve imaging resolution, we propose an infrared polarization super-resolution reconstruction model based on sparse representation, optimized using Stokes vector images. This model forms the basis for our method aimed at achieving super-resolution reconstruction of infrared polarization images. In this method, we utilize the proposed model to initially reconstruct low-resolution images in blocks. Subsequently, we perform a division by weight, followed by iterative back projection to enhance details and achieve high-resolution reconstruction results. As a supplement, we establish a near-real-time short-wave infrared time-sharing polarization system for data collection. The dataset was acquired to gather prior knowledge of the over-complete basis set and to generate a series of simulated focal plane images. Simulation experimental results demonstrate the superiority of our method over several advanced methods in objective evaluation indexes, exhibiting strong noise robustness in quantitative experiments. Finally, to validate the practical application of our method, we establish a split-focal plane polarization short-wave infrared system for scene testing. Experimental results confirm the effective processing of actual captured data by our method.

Deep learning enables contrast-robust super-resolution reconstruction in structured illumination microscopy.

Structured illumination microscopy (SIM) is a powerful technique for super-resolution (SR) image reconstruction. However, conventional SIM methods require high-contrast illumination patterns, which necessitate precision optics and highly stable light sources. To overcome these challenges, we propose a new method called contrast-robust structured illumination microscopy (CR-SIM). CR-SIM employs a deep residual neural network to enhance the quality of SIM imaging, particularly in scenarios involving low-contrast illumination stripes. The key contribution of this study is the achievement of reliable SR image reconstruction even in suboptimal illumination contrast conditions. The results of our study will benefit various scientific disciplines.

Channel estimation for underwater acoustic OFDM based on super‐resolution network

AbstractIn this letter, we propose a method for underwater acoustic channel estimation that combines image super‐resolution (SR) and is named FCDnNet. FCDnNet consists of two parts: Fast Super Resolution Convolutional Neural Network (FSRCNN) and Complex Denoising Convolutional Neural Network (C‐DnCNN). FSRCNN extracts effective features of pilot channels, uses deconvolution to achieve SR reconstruction, and generates a pre‐estimation channel matrix. C‐DnCNN preserves the relative positions of the real and imaginary parts of the channel, fully utilizing amplitude and phase information, and can more effectively recover the channel matrix from the pre‐estimation matrix. Experimental results show that the normalized mean square error (NMSE) of FCDnNet is at least 13.1–65.2 lower than other channel estimation methods based on deep learning.

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.].

Super-Resolution Semantic Segmentation of Droplet Deposition Image for Low-Cost Spraying Measurement

In-field in situ droplet deposition digitization is beneficial for obtaining feedback on spraying performance and precise spray control, the cost-effectiveness of the measurement system is crucial to its scalable application. However, the limitations of camera performance in low-cost imaging systems, coupled with dense spray droplets and a complex imaging environment, result in blurred and low-resolution images of the deposited droplets, which creates challenges in obtaining accurate measurements. This paper proposes a Droplet Super-Resolution Semantic Segmentation (DSRSS) model and a Multi-Adhesion Concave Segmentation (MACS) algorithm to address the accurate segmentation problem in low-quality droplet deposition images, and achieve a precise and efficient multi-parameter measurement of droplet deposition. Firstly, a droplet deposition image dataset (DDID) is constructed by capturing high-definition droplet images and using image reconstruction methods. Then, a lightweight DSRSS model combined with anti-blurring and super-resolution semantic segmentation is proposed to achieve semantic segmentation of deposited droplets and super-resolution reconstruction of segmentation masks. The weighted IoU (WIoU) loss function is used to improve the segmented independence of droplets, and a comprehensive evaluation criterion containing six sub-items is used for parameter optimization. Finally, the MACS algorithm continues to segment the remained adhesive droplets processed by the DSRSS model and corrects the bias of the individual droplet regions by regression. The experiments show that when the two weight parameters α and β in WIoU are 0.775 and 0.225, respectively, the droplet segmentation independence rate of DSRSS on the DDID reaches 0.998, and the IoU reaches 0.973. The MACS algorithm reduces the droplet adhesion rate in images with a coverage rate of more than 30% by 15.7%, and the correction function reduces the coverage error of model segmentation by 3.54%. The parameters of the DSRSS model are less than 1 M, making it possible to run it on embedded platforms. The proposed approach improves the accuracy of spray measurement using low-quality droplet deposition image and will help to scale-up of fast spray measurements in the field.

Detecting anteriorly displaced temporomandibular joint discs using super-resolution magnetic resonance imaging: a multi-center study.

Background: Magnetic resonance imaging (MRI) plays a crucial role in diagnosing anterior disc displacement (ADD) of the temporomandibular joint (TMJ). The primary objective of this study is to enhance diagnostic accuracy in two common disease subtypes of ADD of the TMJ on MRI, namely, ADD with reduction (ADDWR) and ADD without reduction (ADDWoR). To achieve this, we propose the development of transfer learning (TL) based on Convolutional Neural Network (CNN) models, which will aid in accurately identifying and distinguishing these subtypes. Methods: A total of 668 TMJ MRI scans were obtained from two medical centers. High-resolution (HR) MRI images were subjected to enhancement through a deep TL, generating super-resolution (SR) images. Naive Bayes (NB) and Logistic Regression (LR) models were applied, and performance was evaluated using receiver operating characteristic (ROC) curves. The model's outcomes in the test cohort were compared with diagnoses made by two clinicians. Results: The NB model utilizing SR reconstruction with 400 × 400 pixel images demonstrated superior performance in the validation cohort, exhibiting an area under the ROC curve (AUC) of 0.834 (95% CI: 0.763-0.904) and an accuracy rate of 0.768. Both LR and NB models, with 200 × 200 and 400 × 400 pixel images after SR reconstruction, outperformed the clinicians' diagnoses. Conclusion: The ResNet152 model's commendable AUC in detecting ADD highlights its potential application for pre-treatment assessment and improved diagnostic accuracy in clinical settings.

Model-based super-resolution reconstruction for pseudo-continuous Arterial Spin Labeling

Arterial spin labeling (ASL) is a promising, non-invasive perfusion magnetic resonance imaging technique for quantifying cerebral blood flow (CBF). Unfortunately, ASL suffers from an inherently low signal-to-noise ratio (SNR) and spatial resolution, undermining its potential. Increasing spatial resolution without significantly sacrificing SNR or scan time represents a critical challenge towards routine clinical use. In this work, we propose a model-based super-resolution reconstruction (SRR) method with joint motion estimation that breaks the traditional SNR/resolution/scan-time trade-off. From a set of differently oriented 2D multi-slice pseudo-continuous ASL images with a low through-plane resolution, 3D-isotropic, high resolution, quantitative CBF maps are estimated using a Bayesian approach. Experiments on both synthetic whole brain phantom data, and on in vivo brain data, show that the proposed SRR Bayesian estimation framework outperforms state-of-the-art ASL quantification.

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.