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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.

Guided image super-resolution (GISR) aims to obtain a high-resolution (HR) target image by enhancing the spatial resolution of a low-resolution (LR) target image under the guidance of a HR image. However, previous model-based methods mainly take the entire image as a whole, and assume the prior distribution between the HR target image and the HR guidance image, simply ignoring many non-local common characteristics between them. To alleviate this issue, we firstly propose a maximum a posteriori (MAP) estimation model for GISR with two types of priors on the HR target image, i.e., local implicit prior and global implicit prior. The local implicit prior aims to model the complex relationship between the HR target image and the HR guidance image from a local perspective, and the global implicit prior considers the non-local auto-regression property between the two images from a global perspective. Secondly, we design a novel alternating optimization algorithm to solve this model for GISR. The algorithm is in a concise framework that facilitates to be replicated into commonly used deep network structures. Thirdly, to reduce the information loss across iterative stages, the persistent memory mechanism is introduced to augment the information representation by exploiting the Long short-term memory unit (LSTM) in the image and feature spaces. In this way, a deep network with certain interpretation and high representation ability is built. Extensive experimental results validate the superiority of our method on a variety of GISR tasks, including Pan-sharpening, depth image super-resolution, and MR image super-resolution. Code will be released at https://github.com/manman1995/pansharpening.

Digital imaging and modeling are essential tools for characterizing rock structures and understanding fluid flow behavior. These efforts often rely on X‐ray micro‐computed tomography (micro‐CT), which faces an inherent trade‐off between resolution and field‐of‐view (FOV). Deep learning super‐resolution (SR) methods have been developed to overcome this limitation, but their application to carbonate rocks is challenged by complex micro‐nanometer features. Due to the resolution limits, micro‐CT fails to capture sub‐micrometer features such as micropores in carbonates, and using such data as high‐resolution (HR) training images limits the SR model's ability to accurately reconstruct the micropore structures. We introduce a cascading SR pipeline designed to address these challenges and reveal sub‐micrometer features in carbonate rocks. The approach integrates multi‐stage 2D SR networks to progressively enhance low‐resolution (LR) images toward the HR domain, followed by a third‐plane SR network for 3D reconstruction. We evaluate this method on a three‐stage SR task: starting from a 3 μ ${\\upmu }$m resolution micro‐CT image, super‐resolving to an intermediate 1 μ ${\\upmu }$m resolution, and ultimately reaching 0.1 μ ${\\upmu }$m resolution based on scanning electron microscopy (SEM), achieving a 30× ${\\times} $ scale factor. Validation with unseen SEM demonstrates that the reconstructed domains retain essential structural and physical properties. This approach provides a practical solution to current imaging limitations and enables the integration of multi‐resolution modalities for improved rock characterization.

Image super-resolution (SR) is one of the vital image processing methods that improve the resolution of an image in the field of computer vision. In the last two decades, significant progress has been made in the field of super-resolution, especially by utilizing deep learning methods. This survey is an effort to provide a detailed survey of recent progress in single-image super-resolution in the perspective of deep learning while also informing about the initial classical methods used for image super-resolution. The survey classifies the image SR methods into four categories, i.e., classical methods, supervised learning-based methods, unsupervised learning-based methods, and domain-specific SR methods. We also introduce the problem of SR to provide intuition about image quality metrics, available reference datasets, and SR challenges. Deep learning-based approaches of SR are evaluated using a reference dataset. Some of the reviewed state-of-the-art image SR methods include the enhanced deep SR network (EDSR), cycle-in-cycle GAN (CinCGAN), multiscale residual network (MSRN), meta residual dense network (Meta-RDN), recurrent back-projection network (RBPN), second-order attention network (SAN), SR feedback network (SRFBN) and the wavelet-based residual attention network (WRAN). Finally, this survey is concluded with future directions and trends in SR and open problems in SR to be addressed by the researchers.

We present a novel approach to leverage prior knowledge encapsulated in pre-trained text-to-image diffusion models for blind super-resolution. Specifically, by employing our time-aware encoder, we can achieve promising restoration results without altering the pre-trained synthesis model, thereby preserving the generative prior and minimizing training cost. To remedy the loss of fidelity caused by the inherent stochasticity of diffusion models, we employ a controllable feature wrapping module that allows users to balance quality and fidelity by simply adjusting a scalar value during the inference process. Moreover, we develop a progressive aggregation sampling strategy to overcome the fixed-size constraints of pre-trained diffusion models, enabling adaptation to resolutions of any size. A comprehensive evaluation of our method using both synthetic and real-world benchmarks demonstrates its superiority over current state-of-the-art approaches. Code and models are available at https://github.com/IceClear/StableSR.

It is essential for cells to control which genes are transcribed into RNA. In eukaryotes, two major control points are recruitment of RNA polymerase II (Pol II) into a paused state, and subsequent pause release toward transcription. Pol II recruitment and pause release occur in association with macromolecular clusters, which were proposed to be formed by a liquid–liquid phase separation mechanism. How such a phase separation mechanism relates to the interaction of Pol II with DNA during recruitment and transcription, however, remains poorly understood. Here, we use live and super‐resolution microscopy in zebrafish embryos to reveal Pol II clusters with a large variety of shapes, which can be explained by a theoretical model in which regulatory chromatin regions provide surfaces for liquid‐phase condensation at concentrations that are too low for canonical liquid–liquid phase separation. Model simulations and chemical perturbation experiments indicate that recruited Pol II contributes to the formation of these surface‐associated condensates, whereas elongating Pol II is excluded from these condensates and thereby drives their unfolding. Synopsis Recruited RNA polymerase II forms clusters via surface condensation on regulatory chromatin. These clusters unfold due to the exclusion of elongating polymerase from condensates. Pluripotent zebrafish embryos exhibit prominent and long‐lived clusters enriched in recruited RNA polymerase II. Clusters form similar to a liquid film that coats condensation surfaces provided by regulatory genomic regions. Genomic regions that undergo transcription elongation are excluded from the liquid film, resulting in the unfolding of the clusters. Graphical Abstract Recruited RNA polymerase II forms clusters via surface condensation on regulatory chromatin. These clusters unfold due to the exclusion of elongating polymerase from condensates.

Dendritic spines arise as small protrusions from the dendritic shaft of various types of neuron and receive inputs from excitatory axons. Ever since dendritic spines were first described in the nineteenth century, questions about their function have spawned many hypotheses. In this review, we introduce understanding of the structural and biochemical properties of dendritic spines with emphasis on components studied with imaging methods. We then explore advances in in vivo imaging methods that are allowing spine activity to be studied in living tissue, from super‐resolution techniques to calcium imaging. Finally, we review studies on spine structure and function in vivo . These new results shed light on the development, integration properties and plasticity of spines. Dendritic spines receive inputs from excitatory axons, but questions about their function remain. This review tackles our understanding of their structural and biochemical properties, and the imaging methods that allow spine activity to be studied in living tissue. These new results shed light on the development, integration properties and plasticity of dendritic spines.

Due to increasingly complex factors of image degradation, inferring high-frequency details of remote sensing imagery is more difficult compared to ordinary digital photos. This paper proposes an adaptive multi-scale feature fusion network (AMFFN) for remote sensing image super-resolution. Firstly, the features are extracted from the original low-resolution image. Then several adaptive multi-scale feature extraction (AMFE) modules, the squeeze-and-excited and adaptive gating mechanisms are adopted for feature extraction and fusion. Finally, the sub-pixel convolution method is used to reconstruct the high-resolution image. Experiments are performed on three datasets, the key characteristics, such as the number of AMFEs and the gating connection way are studied, and super-resolution of remote sensing imagery of different scale factors are qualitatively and quantitatively analyzed. The results show that our method outperforms the classic methods, such as Super-Resolution Convolutional Neural Network(SRCNN), Efficient Sub-Pixel Convolutional Network (ESPCN), and multi-scale residual CNN(MSRN).

INTRODUCTION The monoclonal antibodies Aducanumab, Lecanemab, Gantenerumab, and Donanemab were developed for the treatment of Alzheimer's disease (AD). METHODS We used single‐molecule detection and super‐resolution imaging to characterize the binding of these antibodies to diffusible amyloid beta (Aβ) aggregates generated in‐vitro and harvested from human brains. RESULTS Lecanemab showed the best performance in terms of binding to the small‐diffusible Aβ aggregates, affinity, aggregate coating, and the ability to bind to post‐translationally modified species, providing an explanation for its therapeutic success. We observed a Braak stage–dependent increase in small‐diffusible aggregate quantity and size, which was detectable with Aducanumab and Gantenerumab, but not Lecanemab, showing that the diffusible Aβ aggregates change with disease progression and the smaller aggregates to which Lecanemab preferably binds exist at higher quantities during earlier stages. DISCUSSION These findings provide an explanation for the success of Lecanemab in clinical trials and suggests that Lecanemab will be more effective when used in early‐stage AD. Highlights Anti amyloid beta therapeutics are compared by their diffusible aggregate binding characteristics. In‐vitro and brain‐derived aggregates are tested using single‐molecule detection. Lecanemab shows therapeutic success by binding to aggregates formed in early disease. Lecanemab binds to these aggregates with high affinity and coats them better.