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Watching physical scenes change over time turns out to be an ideal supervisory signal for static visual features, allowing an independently encoded frame supervised across signed temporal intervals to excel at both single-image and multi-frame downstream tasks.
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 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.
