SIMI is an unsupervised low-light image enhancement network using bit-plane decomposition to mine self-information, reported to reach state-of-the-art performance on standard benchmarks.
Self-reference deep adaptive curve estimation for low-light image enhancement
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
fields
cs.CV 3years
2026 3verdicts
UNVERDICTED 3representative citing papers
Introduces LSTR and ESTR low-light text datasets and shows joint LLIE-OCR training outperforms standalone models.
Self-DACE++ enhances low-light images more effectively than prior methods via efficient adaptive adjustment curves, randomized-order training with network fusion, and a Retinex-grounded denoising module while achieving real-time speed.
citing papers explorer
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SIMI: Self-information Mining Network for Low-light Image Enhancement
SIMI is an unsupervised low-light image enhancement network using bit-plane decomposition to mine self-information, reported to reach state-of-the-art performance on standard benchmarks.
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Reading in the Dark: Low-light Scene Text Recognition
Introduces LSTR and ESTR low-light text datasets and shows joint LLIE-OCR training outperforms standalone models.
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Self-DACE++: Robust Low-Light Enhancement via Efficient Adaptive Curve Estimation
Self-DACE++ enhances low-light images more effectively than prior methods via efficient adaptive adjustment curves, randomized-order training with network fusion, and a Retinex-grounded denoising module while achieving real-time speed.