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Visual Mamba enhances low-light and underwater videos with state-space models

By PulseAugur Editorial · [1 sources] ·

Researchers have developed BVI-Mamba, a novel framework for enhancing videos captured in low-light and underwater conditions. This new method utilizes a Visual State Space (VSS) model to reduce computational demands and memory usage compared to existing AI-based tools. The framework includes a feature alignment module for registering frame displacements and an enhancement module employing VSS blocks for noise removal and brightness adjustment. Experiments indicate that BVI-Mamba surpasses Transformer and convolution-based models in video enhancement tasks. AI

IMPACT Offers a more computationally efficient approach to video enhancement, potentially improving performance in specialized environments.

RANK_REASON Academic paper introducing a new model architecture for video enhancement.

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Visual Mamba enhances low-light and underwater videos with state-space models

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Academic paper introducing a new model architecture for video enhancement.
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182 days old
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Guoxi Huang, Ruirui Lin, Yini Li, David R. Bull, Nantheera Anantrasirichai ·

    BVI-Mamba: Video Enhancement Using a Visual State-Space Model for Low-Light and Underwater Environments

    arXiv:2604.23655v1 Announce Type: new Abstract: Videos captured in low-light and underwater conditions often suffer from distortions such as noise, low contrast, color imbalance, and blur. These issues not only limit visibility but also degrade automatic tasks like detection. Pos…

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