Unified RAW Image Exposure Correction and Restoration via Linear Illumination and Frequency-Adaptive Enhancement

Key points

• Existing sRGB-based correction methods are limited by the non-linear image signal processing pipeline, whereas RAW-based methods are usually developed for a single degradation type, such as low-light denoising or high-light recovery.1
• In this paper, we focus on joint exposure correction and restoration directly in the RAW domain and propose a unified RAW Image Exposure Correction and Restoration (RIECR) framework for both under- and over-exposed images.1
• RIECR decomposes the problem into two coupled sub-problems that follow the RAW imaging model: linear illumination correction and residual degradation restoration.1
• First, a Linear Illumination Correction Network (LICN) estimates exposure-aware linear gains by using channel-specific RAW cues.1

Sources (1)

• [1]Unified RAW Image Exposure Correction and Restoration via Linear Illumination and Frequency-Adaptive Enhancement
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 20, 01:31 PM

  Incorrect exposure degrades both image brightness and recoverable scene detail.

  Existing sRGB-based correction methods are limited by the non-linear image signal processing pipeline, whereas RAW-based methods are usually developed for a single degradation type, such as low-light denoising or high-light recovery.

Extractive summary: sentences quoted from the sources.