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The learning-based super-resolution reconstruction method inputs a low-resolution image into a network, and learns a non-linear mapping relationship between low-resolution and high-resolution through the network. In this study, the multi-scale super-resolution reconstruction network is used to fuse the effective features of different scale images, and the non-linear mapping between low resolution and high resolution is studied from coarse to fine to realise the end-to-end super-resolution reconstruction task. The loss of some features of the low-resolution image will negatively affect the quality of the reconstructed image. To solve the problem of incomplete image features in low-resolution, this study adopts the multi-scale super-resolution reconstruction method based on guided image filtering. The high-resolution image reconstructed by the multi-scale super-resolution network and the real high-resolution image are merged by the guide image filter to generate a new image, and the newly generated image is used for secondary training of the multi-scale super-resolution reconstruction network. The newly generated image effectively compensates for the details and texture information lost in the low-resolution image, thereby improving the effect of the super-resolution reconstructed image.Compared with the existing super-resolution reconstruction scheme, the accuracy and speed of super-resolution reconstruction are improved.

The reanalysis data set between 50 and 80 km altitude suffers from its low vertical resolution and high uncertainty errors. This paper introduces an extendable multi‐input operator neural network, designed to explore global‐scale temperature field to achieve super‐resolution reconstruction in this region. This architecture demonstrates the ability to flexibly address high‐dimensional problems of multi‐source data. Utilizing reanalysis data and observations, the super‐resolution operator elevates the vertical stratification of the temperature field from 13 to 31 layers, achieving a vertical resolution of 1 km, while correcting errors. The reconstructed data set demonstrated a reduction in root mean square error and mean absolute error metrics by 19.3% and 25.1%, respectively. These improvements are particularly pronounced between 55 and 70 km altitude. Notably, the super‐resolution operator model exhibits mediocre performance at heights above 70 km. Our research offers novel insights into generating high‐fidelity near‐space atmospheric data. Plain Language Summary The valuable and widely used European Centre for Medium‐Range Weather Forecasts Reanalysis v5 (ERA5) data set encounters some problems at the altitudes of 50–80 km, including low vertical resolution and uncertain errors. These limitations hinder the investigation of atmospheric processes in the near‐space. In this paper, we propose an extendable neural operator method for super‐resolution reconstruction of the global temperature fields at these altitudes. However, to date, there has been no such research. Our work not only corrects the errors in the ERA5 data but also enhances the vertical stratification of the height range from 13 layers to 31 layers. Consequently, the reconstructed data can be more effectively utilized for studying the atmospheric state in near‐space, which is crucial for understanding the atmospheric mechanisms and applications in this region. Key Points This study explores the super‐resolution reconstruction of the global‐scale temperature fields at heights of 50–80 km This study enhances the vertical resolution of the ERA5 data set(50–80 km) to 1 km The proposed super‐resolution operator can flexibly handle multi‐source high‐dimensional data

Remote-sensing images constitute an important means of obtaining geographic information. Image super-resolution reconstruction techniques are effective methods of improving the spatial resolution of remote-sensing images. Super-resolution reconstruction networks mainly improve the model performance by increasing the network depth. However, blindly increasing the network depth can easily lead to gradient disappearance or gradient explosion, increasing the difficulty of training. This report proposes a new pyramidal multi-scale residual network (PMSRN) that uses hierarchical residual-like connections and dilation convolution to form a multi-scale dilation residual block (MSDRB). The MSDRB enhances the ability to detect context information and fuses hierarchical features through the hierarchical feature fusion structure. Finally, a complementary block of global and local features is added to the reconstruction structure to alleviate the problem that useful original information is ignored. The experimental results showed that, compared with a basic multi-scale residual network, the PMSRN increased the peak signal-to-noise ratio by up to 0.44 dB and the structural similarity to 0.9776.

Visible to short‐wave infrared multispectral imaging is gaining significant attention across various fields, including agriculture, security, and medical diagnostics. Traditional multispectral imaging systems often rely on separate sensors for different spectral bands, leading to complex optical alignment and irreversible resolution loss. Here, we present hardware‐algorithm co‐designed architecture to achieve multispectral super‐resolution imaging. Specifically, we demonstrate a monolithic quad‐spectral photovoltaic imaging platform featuring a resolution of 640 × 512 pixels with <1% dead pixels per channel. The system achieves broadband spectral integration from visible to short‐wave infrared (350–2350 nm) by combining an all‐polymer bulk heterojunction with colloidal quantum dots within a single CMOS‐compatible architecture. The compatibility of all‐polymer bulk heterojunction with direct photopatterning allows for precise patterning and high‐density integration, enabling the devices to operate efficiently in photovoltage mode. To address resolution degradation inherent in planar‐integrated spectral sensing architectures, we applied a super‐resolution reconstruction method, restoring images to a resolution of 640 × 512. The demonstrated capability to simultaneously capture and process multispectral data paves the way for CMOS integration, multispectral Imagers, organic photodetector, super‐resolution reconstruction applications in diverse fields, from precision agriculture to medical diagnostics and beyond. We demonstrate a monolithic quad‐spectral imager that seamlessly integrates visible and short‐wave infrared detection on a single chip. Through direct photopatterning of an all‐polymer bulk heterojunction and colloidal quantum dots, the device achieves high‐resolution (640 × 512) imaging across 350–2400 nm, enabling multispectral capture for applications from precision agriculture to medical diagnostics.

Introduction Preoperatively distinguishing pure ductal carcinoma in situ (DCIS) from upstaged DCIS is important for deciding optimal surgical strategies. However, it is hard to preoperatively predict the upstaging of biopsy‐proven DCIS. This study aims to develop an effective radiomics model for predicting the upstaging of DCIS based on super‐resolution (SR) ultrasound images. Methods In this multicentre retrospective study, patients with biopsy‐proven DCIS who underwent ultrasound examination were included. A super‐resolution reconstruction algorithm was used to enhance the resolution of original high resolution (HR) ultrasound images and obtain SR images. Pyradiomics was used for feature extraction. The selected HR radiomics features and SR radiomics features were combined with clinical features to construct the HR fusion model and SR fusion model, respectively. The area under the receiver operating characteristic curve (AUC) of the models and radiologists was analyzed and compared by the Delong test. Results A total of 681 women (median age, 47 years; interquartile range, 42–54) with 681 biopsy‐proven DCIS lesions were included, with 422 lesions in the training set, 106 lesions in the validation set, and 153 lesions in the external test set. The SR Fusion model achieved an AUC of 0.819 (0.732–0.887) in the validation set and 0.800 (95% CI 0.728–0.860) in the external test set. It outperformed the radiologists (AUC = 0.603–0.627; p < 0.001) in the validation set. Additionally, it surpassed the clinical model (AUC = 0.682, 95% CI 0.602–0.755; p = 0.02) and the HR Fusion model (AUC = 0.724, 95% CI 0.646–0.793; p = 0.03) in the external test set. Conclusion The SR Fusion model integrating SR features and clinical features can effectively predict the upstaging of DCIS.

In the reconstruction of security images, the current generative adversarial network super‐resolution reconstruction algorithm is prone to generate unrealistic artifacts under high noise and low contrast, and the details of small targets are blurred. This paper adopts an improved Real‐ESRGAN (Real‐Enhanced Super‐Resolution Generative Adversarial Network) and CBAM (Convolutional Block Attention Module) algorithm to perform super‐resolution reconstruction of security images and improve the quality of reconstructed images. The study applies multi‐scale convolution kernels in the RRDB (residual in residual dense block) module of the model and uses convolution kernels of different sizes to extract and fuse image features to comprehensively capture local and overall detail information in the image. Then, based on Real‐ESRGAN, the CBAM module is applied in the generator, and the channel attention and spatial attention mechanisms are used to adaptively focus on multi‐scale features to enhance the modeling ability of texture details in the target area. Finally, a multi‐loss fusion optimization strategy is adopted, and color consistency loss and total variation loss are applied to effectively suppress artifacts and color drift problems in the reconstruction process. The experiment takes the security image in the license plate recognition task as an example to perform super‐resolution reconstruction. The results show that the PSNR (Peak Signal‐to‐Noise Ratio) and SSIM (Structural Similarity) of the improved Real‐ESRGAN‐CBAM are the best, reaching 20.1 dB and 0.635, respectively, under extremely high noise of 5 dB, and still reaching 26.1 dB and SSIM of 0.765 under extremely low contrast of 0.2. The experimental results show that the improved Real‐ESRGAN and CBAM combined algorithm in this paper greatly improves the reconstruction quality of security images, effectively suppresses the generation of artifacts, and can better meet the dual needs of intelligent security systems in image quality and practicality. This paper proposes an improved Real‐ESRGAN algorithm that integrates multi‐scale convolution and CBAM attention mechanism, which significantly improves the super‐resolution reconstruction quality of security images under high noise and low contrast, suppresses artifact generation, and enhances detail recovery ability.

Objective Reliable airway patency diagnosis in fetal tracheolaryngeal obstruction is crucial to select and plan ex utero intrapartum treatment (EXIT) surgery. We compared the clinical utility of magnetic resonance imaging (MRI) super-resolution reconstruction (SRR) of the trachea, which can mitigate unpredictable fetal motion effects, with standard 2-dimensional (2D) MRI for airway patency diagnosis and assessment of fetal neck mass anatomy. Study Design A single-center case series of 7 consecutive singleton pregnancies with complex upper airway obstruction (2013-2019). Setting A tertiary fetal medicine unit performing EXIT surgery. Methods MRI SRR of the trachea was performed involving rigid motion correction of acquired 2D MRI slices combined with robust outlier detection to reconstruct an isotropic high-resolution volume. SRR, 2D MRI, and paired data were blindly assessed by 3 radiologists in 3 experimental rounds. Results Airway patency was correctly diagnosed in 4 of 7 cases (57%) with 2D MRI as compared with 2 of 7 cases (29%) with SRR alone or paired 2D MRI and SRR. Radiologists were more confident (P = .026) in airway patency diagnosis when using 2D MRI than SRR. Anatomic clarity was higher with SRR (P = .027) or paired data (P = .041) in comparison with 2D MRI alone. Radiologists detected further anatomic details by using paired images versus 2D MRI alone (P < .001). Cognitive load, as assessed by the NASA Task Load Index, was increased with paired or SRR data in comparison with 2D MRI. Conclusion The addition of SRR to 2D MRI does not increase fetal airway patency diagnostic accuracy but does provide improved anatomic information, which may benefit surgical planning of EXIT procedures.

To improve the issue of low-frequency and high-frequency components from feature maps being treated equally in existing image super-resolution reconstruction methods, the paper proposed an image super-resolution reconstruction method using attention mechanism with feature map to facilitate reconstruction from original low-resolution images to multi-scale super-resolution images. The proposed model consists of a feature extraction block, an information extraction block, and a reconstruction module. Firstly, the extraction block is used to extract useful features from low-resolution images, with multiple information extraction blocks being combined with the feature map attention mechanism and passed between feature channels. Secondly, the interdependence is used to adaptively adjust the channel characteristics to restore more details. Finally, the reconstruction module reforms different scales high-resolution images. The experimental results can demonstrate that the proposed method can effectively improve not only the visual effect of images but also the results on the Set5, Set14, Urban100, and Manga109. The results can demonstrate the proposed method has structurally similarity to the image reconstruction methods. Furthermore, the evaluating indicator of Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index (SSIM) has been improved to a certain degree, while the effectiveness of using feature map attention mechanism in image super-resolution reconstruction applications is useful and effective.

High-resolution images have a wide range of applications in image compression, remote sensing, medical imaging, public safety, and other fields. The primary objective of super-resolution reconstruction of images is to reconstruct a given low-resolution image into a corresponding high-resolution image by a specific algorithm. With the emergence and swift advancement of generative adversarial networks (GANs), image super-resolution reconstruction is experiencing a new era of progress. Unfortunately, there has been a lack of comprehensive efforts to bring together the advancements made in the field of super-resolution reconstruction using generative adversarial networks. Hence, this paper presents a comprehensive overview of the super-resolution image reconstruction technique that utilizes generative adversarial networks. Initially, we examine the operational principles of generative adversarial networks, followed by an overview of the relevant research and background information on reconstructing remote sensing images through super-resolution techniques. Next, we discuss significant research on generative adversarial networks in high-resolution image reconstruction. We cover various aspects, such as datasets, evaluation criteria, and conventional models used for image reconstruction. Subsequently, the super-resolution reconstruction models based on generative adversarial networks are categorized based on whether the kernel blurring function is recognized and utilized during training. We provide a brief overview of the utilization of generative adversarial network models in analyzing remote sensing imagery. In conclusion, we present a prospective analysis of forthcoming research directions pertaining to super-resolution reconstruction methods that rely on generative adversarial networks.

Purpose Deformable image registration (DIR) in low‐contrast tissues is often suboptimal because of low visibility of landmarks, low driving‐force to deform, and low penalty for misalignment. We aim to overcome the shortcomings for improved reconstruction of time‐resolved four‐dimensional magnetic resonance imaging (TR‐4DMRI). Methods and Materials Super‐resolution TR‐4DMRI reconstruction utilizes DIR to combine high‐resolution (highR:2x2x2mm3) breath‐hold (BH) and low‐resolution (lowR:5x5x5mm3) free‐breathing (FB) 3D cine (2Hz) images to achieve clinically acceptable spatiotemporal resolution. A 2‐step hybrid DIR approach was developed to segment low‐dynamic‐range (LDR) regions: low‐intensity lungs and high‐intensity “bodyshell” (=body‐lungs) for DIR refinement after conventional DIR. The intensity in LDR regions was renormalized to the full dynamic range (FDR) to enhance local tissue contrast. A T1‐mapped 4D XCAT digital phantom was created, and seven volunteers and five lung cancer patients were scanned with two BH and one 3D cine series per subject to compare the 1‐step conventional and 2‐step hybrid DIR using: (a) the ground truth in the phantom, (b) highR‐BH references, which were used to simulate 3D cine images by down‐sampling and Rayleigh‐noise‐adding, and (c) cross‐verification between two TR‐4DMRI images reconstructed from two BHs. To assess DIR improvement, 8‐17 blood vessel bifurcations were used in volunteers, and lung tumor position, size, and shape were used in phantom and patients, together with the voxel intensity correlation (VIC), structural similarity (SSIM), and cross‐consistency check (CCC). Results The 2‐step hybrid DIR improves contrast and DIR accuracy. In volunteers, it improves low‐contrast alignment from 6.5 ± 1.8 mm to 3.3 ± 1.0 mm. In phantom, it improves tumor center of mass alignment (COM = 1.3 ± 0.2 mm) and minimizes DIR directional difference. In patients, it produces almost‐identical tumor COM, size, and shape (dice> 0.85) as the reference. The VIC and SSIM are significantly increased and the number of CCC outliers are reduced by half. Conclusion The 2‐step hybrid DIR improves low‐contrast‐tissue alignment and increases lung tumor fidelity. It is recommended to adopt the 2‐step hybrid DIR for TR‐4DMRI reconstruction.