Scalable Image Compressed Sensing With Generator Networks

Zhong, Yuanhong; Zhang, Chenxu; Ren, Fan; Kuang, Honggang; Tang, Panliang

doi:10.1109/tci.2022.3224281

Cited by 5 publications

(1 citation statement)

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“…The popular convolutional neural network (CNN) is suitable for learning the non-linear reconstruction mapping f directly in training space X [18]- [23]. Recently, regularization model architecture is embedded into deep convolutional neural network (DnCNN) for image denoising [24].…”

Section: Introductionmentioning

confidence: 99%

Nest-DGIL: Nesterov-Optimized Deep Geometric Incremental Learning for CS Image Reconstruction

Fan,

Yang,

Chen

et al. 2023

IEEE Trans. Comput. Imaging

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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 Nesterov proximal gradient optimization. The proposed end-to-end network not only has the powerful learning ability for high/low frequency image features, but also can theoretically guarantee that geometric texture details will be reconstructed from preliminary linear reconstruction. Furthermore, it can avoid the risk of intermediate reconstruction results falling outside the geometric decomposition domains and achieve fast convergence. Our reconstruction framework is decomposed into four modules including general linear reconstruction, cascade geometric incremental restoration, Nesterov acceleration and post-processing. In the image restoration step, a cascade geometric incremental learning module is designed to compensate for the missing texture information from different geometric spectral decomposition domains. Inspired by overlaptile strategy, we also develop a post-processing module to remove the block-effect in patch-wise-based natural image reconstruction. All parameters in the proposed model are learnable, an adaptive initialization technique of physical-parameters is also employed to make model flexibility and ensure converging smoothly. We compare the reconstruction performance of the proposed method with existing state-of-the-art methods to demonstrate its superiority. Our source codes are available at https://github.com/fanxiaohong/Nest-DGIL.

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Section: Introductionmentioning

confidence: 99%