Rethinking the CSC Model for Natural Images

CRNet: Image Super-Resolution Using A Convolutional Sparse Coding Inspired Network

Convolutional Sparse Coding (CSC) has been attracting more and more attention in recent years, for making full use of image global correlation to improve performance on various computer vision applications. However, very few studies focus on solving CSC based image Super-Resolution (SR) problem. As a consequence, there is no significant progress in this area over a period of time. In this paper, we exploit the natural connection between CSC and Convolutional Neural Networks (CNN) to address CSC based image SR. Specifically, Convolutional Iterative Soft Thresholding Algorithm (CISTA) is introdu…

Representation Learning via Cauchy Convolutional Sparse Coding

In representation learning, Convolutional Sparse Coding (CSC) enables unsupervised learning of features by jointly optimising both an \(\ell_2\)-norm fidelity term and a sparsity enforcing penalty. This work investigates using a regularisation term derived from an assumed Cauchy prior for the coefficients of the feature maps of a CSC generative model. The sparsity penalty term resulting from this prior is solved via its proximal operator, which is then applied iteratively, element-wise, on the coefficients of the feature maps to optimise the CSC cost function. The performance of the proposed I…

深度均衡卷积稀疏编码用于超光谱图像去噪

Deep Equilibrium Convolutional Sparse Coding for Hyperspectral Image Denoising

本文提出深度均衡卷积稀疏编码框架，通过统一局部空间-光谱相关性、非局部自相似性和全局一致性，提升超光谱图像去噪性能。

Rethinking Machine Learning Model Evaluation in Pathology

Machine Learning has been applied to pathology images in research and clinical practice with promising outcomes. However, standard ML models often lack the rigorous evaluation required for clinical decisions. Machine learning techniques for natural images are ill-equipped to deal with pathology images that are significantly large and noisy, require expensive labeling, are hard to interpret, and are susceptible to spurious correlations. We propose a set of practical guidelines for ML evaluation in pathology that address the above concerns. The paper includes measures for setting up the evaluati…

关注纹理：重新思考用于医学图像分类的掩码自编码器中的预训练

Focus on Texture: Rethinking Pre-training in Masked Autoencoders for Medical Image Classification

针对医学图像分类中纹理线索重要但MSE预训练导致模糊的问题，提出GLCM-MAE框架，利用基于灰度共生矩阵的可微损失保留形态特征，在多项医学图像分类任务上超越现有方法。

Rethinking Irregular Scene Text Recognition

Reading text from natural images is challenging due to the great variety in text font, color, size, complex background and etc.. The perspective distortion and non-linear spatial arrangement of characters make it further difficult. While rectification based method is intuitively grounded and has pushed the envelope by far, its potential is far from being well exploited. In this paper, we present a bag of tricks that prove to significantly improve the performance of rectification based method. On curved text dataset, our method achieves an accuracy of 89.6% on CUTE-80 and 76.3% on Total-Text, a…