DiffLLFace: Learning Alternate Illumination-Diffusion Adaptation for Low-Light Face Super-Resolution and Beyond.

Area of Science:

• Computer Vision
• Image Processing
• Artificial Intelligence

Background:

• Facial image acquisition often suffers from coupled photometric and geometric degradations under low-light and low-resolution (LLR) conditions.
• Existing methods treat low-light image enhancement (LLIE) and face super-resolution (FSR) as separate tasks, limiting practical applicability.

Purpose of the Study:

• To present DiffLLFace, a unified framework for robust face super-resolution from LLR images.
• To address the limitations of fragmented optimization by considering the compound nature of LLR degradations.

Main Methods:

• Utilizes diffusive generative capabilities with illumination-aware trajectories for face super-resolution.
• Employs an alternate illumination-diffusion adaptation mechanism throughout the generation process.
• Incorporates a non-parametric Fourier enhancement strategy for texture and color consistency.

Main Results:

• DiffLLFace effectively harmonizes latent representations by capturing brightness and structural degradation patterns.
• The framework achieves precise control over conditional adaptation and illumination rectification.
• Demonstrates superior performance over existing methods on LLR facial images and generalizability on natural scenes.

Conclusions:

• DiffLLFace offers a unified and robust solution for face super-resolution under challenging LLR conditions.
• The proposed method outperforms traditional approaches by effectively handling coupled degradations.
• The framework shows significant potential for real-world applications requiring high-quality facial image reconstruction.