Comprehensive Examination of Unrolled Networks for Solving Linear Inverse Problems.

Area of Science:

• Computer Vision
• Computational Imaging
• Deep Learning

Background:

• Unrolled networks are effective for computer vision and imaging but adapting them to new applications is complex.
• Numerous design choices (optimization, loss function, architecture) impact performance.
• Evaluating each choice requires extensive training and simulation, making optimization time-consuming.

Purpose of the Study:

• To unify methodologies for unrolled networks, reducing design complexity.
• To conduct a comprehensive ablation study on design choices.
• To offer practical recommendations for designing and diagnosing unrolled networks.

Main Methods:

• Unifying existing ideas and methodologies for unrolled network design.
• Performing a detailed ablation study to assess individual design choices.
• Analyzing simulation results for network training, fine-tuning, and performance optimization.

Main Results:

• Identification of key design decisions impacting unrolled network performance.
• Quantification of the effects of different optimization algorithms, loss functions, and architectures.
• Development of practical guidelines based on empirical findings.

Conclusions:

• Simplifying the design process for unrolled networks is achievable through methodological unification.
• Ablation studies are crucial for understanding and optimizing unrolled network configurations.
• The findings will aid scientists and engineers in efficiently designing and troubleshooting unrolled networks.