QISTA-ImageNet: A Deep Compressive Image Sensing Framework Solving Lq-Norm Optimization Problem
Abstract
In this paper, we study how to reconstruct the original images from the given sensed samples/measurements by proposing a so-called deep compressive image sensing framework. This framework, dubbed QISTA-ImageNet, is built upon a deep neural network to realize our optimization algorithm QISTA (Lq-ISTA) in solving image recovery problem. The unique characteristics of QISTA-ImageNet are that we (1) introduce a generalized proximal operator and present learning-based proximal gradient descent (PGD) together with an iterative algorithm in reconstructing images, (2) analyze how QISTA-ImageNet can exhibit better solutions compared to state-of-the-art methods and interpret clearly the insight of proposed method, and (3) conduct empirical comparisons with state-of-the-art methods to demonstrate that QISTA-ImageNet exhibits the best performance in terms of image reconstruction quality to solve the Lq-norm optimization problem.
Cite
Text
Lin et al. "QISTA-ImageNet: A Deep Compressive Image Sensing Framework Solving Lq-Norm Optimization Problem." Proceedings of the European Conference on Computer Vision (ECCV), 2022. doi:10.1007/978-3-031-20050-2_24Markdown
[Lin et al. "QISTA-ImageNet: A Deep Compressive Image Sensing Framework Solving Lq-Norm Optimization Problem." Proceedings of the European Conference on Computer Vision (ECCV), 2022.](https://mlanthology.org/eccv/2022/lin2022eccv-qistaimagenet/) doi:10.1007/978-3-031-20050-2_24BibTeX
@inproceedings{lin2022eccv-qistaimagenet,
title = {{QISTA-ImageNet: A Deep Compressive Image Sensing Framework Solving Lq-Norm Optimization Problem}},
author = {Lin, Gang-Xuan and Hu, Shih-Wei and Lu, Chun-Shien},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
year = {2022},
doi = {10.1007/978-3-031-20050-2_24},
url = {https://mlanthology.org/eccv/2022/lin2022eccv-qistaimagenet/}
}