Optimization-Inspired Learning With Architecture Augmentations and Control Mechanisms for Low-Level Vision.

Area of Science:

• Computer Vision
• Machine Learning
• Numerical Optimization

Background:

• Growing interest in combining learnable modules with numerical optimization for low-level vision.
• Existing methods often lack a unified approach for designing propagative modules, theoretical analysis, and learning mechanisms.

Purpose of the Study:

• To propose a unified optimization-inspired learning framework (GDC) that integrates Generative, Discriminative, and Corrective principles.
• To address the limitations of specialized schemes by offering a generalized solution for diverse optimization models.

Main Methods:

• Introduced a general energy minimization model and formulated its descent direction from generative, discriminative, and corrective viewpoints.
• Constructed three propagative modules (Generative, Discriminative, Corrective) for flexible combination.
• Designed two control mechanisms providing theoretical guarantees for optimization formulations.

Main Results:

• Demonstrated the efficacy and adaptability of the GDC framework across various low-level vision tasks.
• Showcased stable propagation with convergence through architecture augmentation strategies like normalization and search.
• Validated the theoretical guarantees supporting the proposed methods.

Conclusions:

• The GDC framework provides a unified, theoretically grounded, and adaptable approach for optimization-inspired learning in low-level vision.
• The proposed methods effectively solve optimization models with flexible combinations of principles, enhancing image and feature propagation.