Unrolling a rain-guided detail recovery network for single-image deraining

Background Owing to the rapid development of deep networks, single-image deraining tasks have progressed significantly. Various architectures have been designed to recursively or directly remove rain, and most rain streaks can be removed using existing deraining methods. However, many of them cause detail loss, resulting in visual artifacts. Method To resolve this issue, we propose a novel unrolling rain-guided detail recovery network(URDRN) for single-image deraining based on the observation that the most degraded areas of a background image tend to be the most rain-corrupted regions. Furthermore, to address the problem that most existing deep-learningbased methods trivialize the observation model and simply learn end-to-end mapping, the proposed URDRN unrolls a single-image deraining task into two subproblems: rain extraction and detail recovery. Result Specifically, first, a context aggregation attention network is introduced to effectively extract rain streaks;thereafter, a rain attention map is generated as an indicator to guide the detail recovery process. For the detail recovery sub-network, with the guidance of the rain attention map, a simple encoder–decoder model is sufficient to recover the lost details.Experiments on several well-known benchmark datasets show that the proposed approach can achieve performance similar to those of other state-of-the-art methods.

Learning to optimize:A tutorial for continuous and mixed-integer optimization

Learning to optimize(L2O)stands at the intersection of traditional optimization and machine learning,utilizing the capabilities of machine learning to enhance conventional optimization techniques.As real-world optimization problems frequently share common structures,L2O provides a tool to exploit these structures for better or faster solutions.This tutorial dives deep into L2O techniques,introducing how to accelerate optimization algorithms,promptly estimate the solutions,or even reshape the optimization problem itself,making it more adaptive to real-world applications.By considering the prerequisites for successful applications of L2O and the structure of the optimization problems at hand,this tutorial provides a comprehensive guide for practitioners and researchers alike.

A Review on Deep Learning in Medical Image Reconstruction

Medical imaging is crucial in modern clinics to provide guidance to the diagnosis and treatment of diseases.Medical image reconstruction is one of the most fundamental and important components of medical imaging,whose major objective is to acquire high-quality medical images for clinical usage at the minimal cost and risk to the patients.Mathematical models in medical image reconstruction or,more generally,image restoration in computer vision have been playing a prominent role.Earlier mathematical models are mostly designed by human knowledge or hypothesis on the image to be reconstructed,and we shall call these models handcrafted models.Later,handcrafted plus data-driven modeling started to emerge which still mostly relies on human designs,while part of the model is learned from the observed data.More recently,as more data and computation resources are made available,deep learning based models(or deep models)pushed the data-driven modeling to the extreme where the models are mostly based on learning with minimal human designs.Both handcrafted and data-driven modeling have their own advantages and disadvantages.Typical handcrafted models are well interpretable with solid theoretical supports on the robustness,recoverability,complexity,etc.,whereas they may not be flexible and sophisticated enough to fully leverage large data sets.Data-driven models,especially deep models,on the other hand,are generally much more flexible and effective in extracting useful information from large data sets,while they are currently still in lack of theoretical foundations.Therefore,one of the major research trends in medical imaging is to combine handcrafted modeling with deep modeling so that we can enjoy benefits from both approaches.The major part of this article is to provide a conceptual review of some recent works on deep modeling from the unrolling dynamics viewpoint.This viewpoint stimulates new designs of neural network architectures with inspirations from optimization algorithms and numerical differential equations.Given the popularity of deep modeling,there are still vast remaining challenges in the field,as well as opportunities which we shall discuss at the end of this article.