DeepGSR: Deep Group-Based Sparse Representation Network for Solving Image Inverse Problems.

Area of Science:

• Computer Vision
• Image Processing
• Machine Learning

Background:

• Group-based sparse representation (GSR) offers model interpretability for image inverse problems.
• Traditional GSR methods are computationally expensive due to iterative processes.
• Deep learning (DL) methods are efficient but often lack model interpretability.

Purpose of the Study:

• To propose DeepGSR, a novel framework integrating GSR and DL for efficient and interpretable image inverse problem solving.
• To overcome the computational bottlenecks of conventional GSR while retaining its interpretability.
• To enhance representation ability by modeling complex intra-group relationships and exploiting frequency-specific structures.

Main Methods:

• Developed a deep group-based sparse representation (DeepGSR) framework.
• Integrated adaptive patch matching and aggregation mechanisms for latent space modeling.
• Introduced a learnable low-rank shrinkage module to reduce computational complexity and enhance adaptability.
• Incorporated a shifting wavelet-domain patch partitioning strategy for frequency-specific modeling.

Main Results:

• DeepGSR effectively addresses computational expense and interpretability issues in GSR.
• The framework demonstrates consistent and effective performance across diverse image inverse problems.
• Applications include image denoising, deraining, metal artifact reduction, CT reconstruction, phase retrieval, and all-in-one restoration.

Conclusions:

• DeepGSR provides a powerful and interpretable solution for image inverse problems.
• The framework's drop-in replacement capability validates its versatility and effectiveness.
• Publicly available source code and datasets facilitate further research and application.