Nest-DGIL: Nesterov-Optimized Deep Geometric Incremental Learning for CS Image Reconstruction
Xiaohong Fan,
Yin Yang,
Ke Chen
et al.
Abstract:Proximal gradient-based optimization is one of the most common strategies for solving image inverse problems as well as easy to implement. However, these techniques often generate heavy artifacts in image reconstruction. One of the most popular refinement methods is to fine-tune the regularization parameter to alleviate such artifacts, but it may not always be sufficient or applicable due to increased computational costs. In this work, we propose a deep geometric incremental learning framework based on second … Show more
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