题目: Learnable Descent Algorithm for Inverse Problems and Applications in Image Reconstruction

报告人: 叶筱倞教授（佐治亚州立大学数学与统计系）

报告摘要：

We propose a general learning based framework for solving nonsmooth and nonconvex image reconstruction problems. We model the regularization function as the composition of the $l_{2,1}$ norm and a smooth but nonconvex feature mapping parametrized as a deep convolutional neural network. We develop a provably convergent descent-type algorithm to solve the nonsmooth nonconvex minimization problem by leveraging the Nesterov's smoothing technique and the idea of residual learning, and learn the network parameters such that the outputs of the algorithm match the references in training data. Our method is versatile as one can employ various modern network structures into the regularization, and the resulting network inherits the guaranteed convergence of the algorithm. We also show that the proposed network is parameter-efficient and its performance compares favorably to the state-of-the-art methods in a variety of image reconstruction problems in practice.

报告时间：2023年5月29日（星期一），15:00-16:00

报告地点: 第六教学楼南528 （3200威尼斯vip学术报告厅）

联系人：徐洪坤（手机：15356199736）