BVI-Mamba enhances low-light and underwater videos by combining feature alignment with a UNet architecture built from Visual State Space blocks, claiming better quality and efficiency than prior Transformer or convolution methods.
RetinexMamba: Retinex-based mamba for low-light image enhancement
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.CV 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Lightweight LLIE framework pairs frozen distribution-normalizing preprocessing with a depthwise U-Net and reports 3rd place in the 2026 NTIRE Efficient Low-Light challenge.
citing papers explorer
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BVI-Mamba: Video Enhancement Using a Visual State-Space Model for Low-Light and Underwater Environments
BVI-Mamba enhances low-light and underwater videos by combining feature alignment with a UNet architecture built from Visual State Space blocks, claiming better quality and efficiency than prior Transformer or convolution methods.
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Lightweight Low-Light Image Enhancement via Distribution-Normalizing Preprocessing and Depthwise U-Net
Lightweight LLIE framework pairs frozen distribution-normalizing preprocessing with a depthwise U-Net and reports 3rd place in the 2026 NTIRE Efficient Low-Light challenge.