As ground-based all-sky astronomical surveys will gather millions of images
in the coming years, a critical requirement emerges for the development of fast
deconvolution algorithms capable of efficiently improving the spatial
resolution of these images. By successfully recovering clean and
high-resolution images from these surveys, our objective is to help deepen our
understanding of galaxy formation and evolution through accurate photometric
measurements. We introduce a two-step deconvolution framework using a Swin
Transformer architecture. Our study reveals that the deep learning-based
solution introduces a bias, constraining the scope of scientific analysis. To
address this limitation, we propose a novel third step relying on the active
coefficients in the sparsity wavelet framework. By conducting a performance
comparison between our deep learning-based method and Firedec, a classical
deconvolution algorithm, we analyze a subset of the EDisCS cluster samples. We
demonstrate the advantage of our method in terms of resolution recovery,
generalization to different noise properties, and computational efficiency. Not
only does the analysis of this cluster sample assess the efficiency of our
method, but it also enables us to quantify the number of clumps within these
galaxies in relation to their disc colour. This robust technique holds promise
for identifying structures in the distant universe from ground-based images.

借助基于 Swin Transformer 结构的 deep learning 方法进行恢复图像分辨率的研究，提出了一种具有先进的稀疏小波框架的三步解卷积框架，与传统的 Firedec 算法相比，我们的方法在恢复分辨率、适应不同噪声特性和计算效率方面展示出巨大优势，对于从地面图像中识别远程宇宙中的结构具有很大的潜力。

基于 Swin Transformer UNet 的地面图像去模糊

Ground-based Image Deconvolution with Swin Transformer UNet

To overcome hardware limitations in commercially available depth sensors
which result in low-resolution depth maps, depth map super-resolution (DMSR) is
a practical and valuable computer vision task. DMSR requires upscaling a
low-resolution (LR) depth map into a high-resolution (HR) space. Joint image
filtering for DMSR has been applied using spatially-invariant and
spatially-variant convolutional neural network (CNN) approaches. In this
project, we propose a novel joint image filtering DMSR algorithm using a Swin
transformer architecture. Furthermore, we introduce a Nonlinear Activation Free
(NAF) network based on a conventional CNN model used in cutting-edge image
restoration applications and compare the performance of the techniques. The
proposed algorithms are validated through numerical studies and visual examples
demonstrating improvements to state-of-the-art performance while maintaining
competitive computation time for noisy depth map super-resolution.

本研究提出了一种基于 Swin 变换器架构和非线性激活函数自由网络的联合图像滤波深度图超分辨率算法，并通过数值研究和视觉示例证明了其在保持竞争计算时间的同时提高了最先进性能。