4 papers
MG-SpaIR: Multi-grade Sparse-guided Implicit Representation for Training-Data-Free Image Restoration
Jianmin Liao, Lei Huang, Ronglong Fang +3
MG-SpaIR is a training-data-free framework for restoring a clean image from a single observation corrupted by a mixture of blur, downsampling, noise, and missing pixels. Building o…
Computational Advantages of Multi-Grade Deep Learning: Convergence Analysis and Performance Insights
Ronglong Fang, Yuesheng Xu
Multi-grade deep learning (MGDL) has been shown to significantly outperform the standard single-grade deep learning (SGDL) across various applications. This work aims to investigat…
Addressing Spectral Bias of Deep Neural Networks by Multi-Grade Deep Learning
Ronglong Fang, Yuesheng Xu
Deep neural networks (DNNs) suffer from the spectral bias, wherein DNNs typically exhibit a tendency to prioritize the learning of lower-frequency components of a function, struggl…
Inexact FPPA for the Sparse Regularization Problem
Ronglong Fang, Yuesheng Xu, Mingsong Yan
We study inexact fixed-point proximity algorithms for solving a class of sparse regularization problems involving the norm. Specifically, the model has an objecti…