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CMIG-Net achieves up to a 0.619 dB gain in PSNR over existing methods by effectively leveraging conditional mutual information for low-light image enhancement.
Current image quality metrics struggle to articulate *why* one high-quality image is better than another, but this challenge shows MLLMs are closing the gap by providing expert-level explanations.
Current image restoration models still fail to strike the right balance between noise reduction, detail fidelity, and accurate color in real-world, low-light portrait scenarios, highlighting a critical gap this challenge aims to close.
