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The Coded Aperture Snapshot Spectral Imaging (CASSI) system captures the three-dimensional (3D) spatio-spectral information of a scene using a set of two-dimensional (2D) random-coded Focal Plane Array (FPA) measurements. A compressive sensing reconstruc-tion algorithm is then used to recover the underlying spatio-spectral 3D data cube. The quality of the reconstructed spectral images depends exclusively on the CASSI sensing matrix, which is determined by the structure of a set of random coded apertures. In this paper, the CASSI system is generalized by developing a multiple-aperture optical imaging system such that spectral resolution en-hancement is attainable. In the proposed system, a pair of high-resolution coded apertures is introduced into the CASSI system, allow-ing it to encode both spatial and spectral characteristics of the hyperspectral image. This approach allows the reconstruction of super-resolved hyperspectral data cubes, where the number of spectral bands is significantly increased and the quality in the spatial domain is greatly improved. Extensively simulated experiments show a gain in the peak-signal-to-noise ratio (PSNR), along with a better fit of the reconstructed spectral signatures to the original spectral data.

Super-resolution optical imaging is crucial to the study of cellular processes. Current super-resolution fluorescence microscopy is restricted by the need of special fluorophores or sophisticated optical systems, or long acquisition and computational times. In this work, we present a deep-learning-based super-resolution technique of confocal microscopy. We devise a two-channel attention network (TCAN), which takes advantage of both spatial representations and frequency contents to learn a more precise mapping from low-resolution images to high-resolution ones. This scheme is robust against changes in the pixel size and the imaging setup, enabling the optimal model to generalize to different fluorescence microscopy modalities unseen in the training set. Our algorithm is validated on diverse biological structures and dual-color confocal images of actin-microtubules, improving the resolution from ~ 230 nm to ~ 110 nm. Last but not least, we demonstrate live-cell super-resolution imaging by revealing the detailed structures and dynamic instability of microtubules.

Signal processing techniques play an important role in seismic data analysis. Variational mode decomposition (VMD), as a powerful signal processing method, has been extensively applied in seismic signal processing. A large number of papers on the application of VMD in seismic data analysis have appeared in various journals, conference proceedings, and technical communications. The paper aims to investigate and summarize the recent advancements of VMD and its application in seismic data analysis and give a comprehensive reference for scholars that may be interested in this topic so that researchers can select a more in-depth research direction. Firstly, the VMD principle is briefly introduced, and the advantage and limitations of this approach are illustrated in detail. Secondly, recent applications of the VMD in seismic data analysis are summarized in terms of specific scenarios, such as seismic time–frequency analysis (TFA), seismic denoising, and other applications. Finally, the key problems of VMD in seismic data analysis are discussed, and the potential research directions are listed. It is expected that the review would be constructive to the basic understanding of the VMD concept for beginners and insightful exploration of VMD’s applications in seismic data analysis for advanced researchers.Article HighlightsSeismic data analysis plays an important role in extracting valuable information from seismic recordsThis paper surveys the VMD and its applications in the field of seismic data analysis in a comprehensive wayPromising research prospects of VMD in seismic data analysis are proposed

The objective of image super-resolution is to reconstruct a high-resolution (HR) image with the prior knowledge from one or several low-resolution (LR) images. However, in the real world, due to the limited complementary information, the performance of both single-frame and multi-frame super-resolution reconstruction degrades rapidly as the magnification increases. In this paper, we propose a novel two-step image super resolution method concatenating multi-frame super-resolution (MFSR) with single-frame super-resolution (SFSR), to progressively upsample images to the desired resolution. The proposed method consisting of an L0-norm constrained reconstruction scheme and an enhanced residual back-projection network, integrating the flexibility of the variational model-based method and the feature learning capacity of the deep learning-based method. To verify the effectiveness of the proposed algorithm, extensive experiments with both simulated and real world sequences were implemented. The experimental results show that the proposed method yields superior performance in both objective and perceptual quality measurements. The average PSNRs of the cascade model in set5 and set14 are 33.413 dB and 29.658 dB respectively, which are 0.76 dB and 0.621 dB more than the baseline method. In addition, the experiment indicates that this cascade model can be robustly applied to different SFSR and MFSR methods.

Recent advances in 3D Gaussian Splatting (3DGS) have achieved remarkable performance in scene reconstruction and novel view synthesis on benchmark datasets. However, real-world images are frequently affected by degradations such as camera shake, object motion, and lens defocus, which not only compromise image quality but also severely hinder the accuracy of 3D reconstruction—particularly in fine details. While existing deblurring approaches have made progress, most are limited to addressing a single type of blur, rendering them inadequate for complex scenarios involving multiple blur sources and resolution degradation. To address these challenges, we propose Gaussian Splatting with Multi-Scale Deblurring and Resolution Enhancement (GS-MSDR), a novel framework that seamlessly integrates multi-scale deblurring and resolution enhancement. At its core, our Multi-scale Adaptive Attention Network (MAAN) fuses multi-scale features to enhance image information, while the Multi-modal Context Adapter (MCA) and adaptive spatial pooling modules further refine feature representation, facilitating the recovery of fine details in degraded regions. Additionally, our Hierarchical Progressive Kernel Optimization (HPKO) method mitigates ambiguity and ensures precise detail reconstruction through layer-wise optimization. Extensive experiments demonstrate that GS-MSDR consistently outperforms state-of-the-art methods under diverse degraded scenarios, achieving superior deblurring, accurate 3D reconstruction, and efficient rendering within the 3DGS framework.

This paper proposes an integrated neural network for joint noise suppression and resolution enhancement of inverse synthetic aperture radar (ISAR) images. Unlike conventional methods that address both challenges separately, we present a unified framework that can address them simultaneously. To achieve this, we first generate a comprehensive dataset of ISAR images for various targets under different conditions using a simulation‐based method. Subsequently, we develop separate generative models for noise suppression and resolution enhancement, which are then combined sequentially. This combined network uses a joint optimization strategy in training process, simultaneously updating the weights of the two networks. The proposed integrated network achieved an average peak signal‐to‐noise ratio and structural similarity index measure of 34.69 dB and 0.95, respectively. It demonstrates that the proposed network effectively achieves both noise suppression and resolution enhancement within a single network. This study proposes an integrated neural network for simultaneous noise suppression and resolution enhancement in inverse synthetic aperture radar (ISAR) images. The proposed approach sequentially combines separate generative models for each task and optimizes them using a joint learning strategy. The integrated network achieves a peak signal‐to‐noise ratio of 34.69 dB and a structural similarity index of 0.95, demonstrating its effectiveness in enhancing ISAR image quality within a unified framework.

Low-light image enhancement based on deep learning has achieved breakthroughs recently. However, the current methods based on deep learning have problems with inadequate resolution enhancement or inadequate contrast. To address these problems, this paper proposes a resolution and contrast fusion network with ConvLSTM (RCFNC) for low-light image enhancement. The network is mainly constructed by four parts, including resolution enhancement branch, contrast enhancement branch, multi-scale feature fusion block (MFFB), and convolution long short-time memory block (ConvLSTM). Specifically, to improve the resolution of the low-light image, a resolution enhancement branch consisting of multi-scale differential feature blocks is proposed, using residual features at different scales to enhance the spatial details of image. To enhance the contrast of the image, a contrast enhancement branch consisting of adaptive convolution residual blocks is introduced to learn the mapping relationship between global and local features in the image. In addition, a weighted fusion is performed using MFFB to better balance the resolution and contrast features obtained from the above branches. Finally, to improve the learning capability of the model, ConvLSTM is added to filter redundant information. Experiments on the LOL, MIT5K, and five benchmark datasets show that RCFNC outperforms current state-of-the-art methods.

Hyperspectral images with hundreds of spectral bands have been proven to yield high performance in material classification. However, despite intensive advancement in hardware, the spatial resolution is still somewhat low, as compared to that of color and multispectral (MS) imagers. In this paper, we aim at presenting some ideas that may further enhance the performance of some remote sensing applications such as border monitoring and Mars exploration using hyperspectral images. One popular approach to enhancing the spatial resolution of hyperspectral images is pansharpening. We present a brief review of recent image resolution enhancement algorithms, including single super-resolution and multi-image fusion algorithms, for hyperspectral images. Advantages and limitations of the enhancement algorithms are highlighted. Some limitations in the pansharpening process include the availability of high resolution (HR) panchromatic (pan) and/or MS images, the registration of images from multiple sources, the availability of point spread function (PSF), and reliable and consistent image quality assessment. We suggest some proactive ideas to alleviate the above issues in practice. In the event where hyperspectral images are not available, we suggest the use of band synthesis techniques to generate HR hyperspectral images from low resolution (LR) MS images. Several recent interesting applications in border monitoring and Mars exploration using hyperspectral images are presented. Finally, some future directions in this research area are highlighted.

Abstract Improving the signal-to-noise ratio (SNR) and enhancing resolution are essential for accurately recovering weak signals in seismic signal processing. Block-Matching and 3D filtering (BM3D) is a widely used denoising algorithm in data processing that employs two key stages—hard thresholding and Wiener filtering—to achieve multidimensional noise attenuation and improve SNR. However, the traditional BM3D algorithm does not fully consider the complex stratum effects present in seismic data, which limits its effectiveness in weak signal recovery. To address this limitation, we propose a resolution-enhanced BM3D (RE-BM3D), which refines both key stages to improve the recovery of weak signals. In the hard thresholding stage, we introduce an inverse filtering operator with Tikhonov regularization to better recover weak signals attenuated by stratum filtering. Additionally, a dynamic threshold is applied based on the variance of spectral coefficients to further enhance the accuracy of noise attenuation. In the Wiener filtering stage, regularized inverse filtering is employed to enhance vertical resolution, while the shrinkage coefficient is calculated based on the variance of spectral coefficients to achieve optimal SNR improvement. Experimental results show that RE-BM3D successfully enhances vertical resolution and recovers weak seismic signals, effectively overcoming the limitations of traditional BM3D in seismic data processing.

The lack of high-resolution thermal images is a limiting factor in the fusion with other sensors with a higher resolution. Different families of algorithms have been designed in the field of remote sensors to fuse panchromatic images with multispectral images from satellite platforms, in a process known as pansharpening. Attempts have been made to transfer these pansharpening algorithms to thermal images in the case of satellite sensors. Our work analyses the potential of these algorithms when applied to thermal images from unmanned aerial vehicles (UAVs). We present a comparison, by means of a quantitative procedure, of these pansharpening methods in satellite images when they are applied to fuse high-resolution images with thermal images obtained from UAVs, in order to be able to choose the method that offers the best quantitative results. This analysis, which allows the objective selection of which method to use with this type of images, has not been done until now. This algorithm selection is used here to fuse images from thermal sensors on UAVs with other images from different sensors for the documentation of heritage, but it has applications in many other fields.