Multinex: Ultra-Lightweight Low-Light Image Enhancement Framework
A new research paper on arXiv proposes Multinex, an ultra-lightweight structured framework for low-light image enhancement (LLIE). The framework integrates multiple fine-grained representations within a Retinex residual formulation, decomposing images into illumination and color prior stacks from distinct analytic representations. It learns to fuse these into luminance and reflectance adjustments for exposure correction. By prioritizing enhancement over reconstruction and using lightweight neural operations, Multinex significantly reduces computational demands, addressing the limitations of state-of-the-art techniques that rely on large models and multi-stage training, which hinder edge deployment. The paper also notes that dependence on a single color space in existing methods introduces instability and artifacts.
Key facts
- Multinex is an ultra-lightweight structured framework for low-light image enhancement.
- It integrates multiple fine-grained representations within a Retinex residual formulation.
- The framework decomposes images into illumination and color prior stacks.
- It learns to fuse representations into luminance and reflectance adjustments.
- Multinex prioritizes enhancement over reconstruction.
- It uses lightweight neural operations to reduce computational demands.
- State-of-the-art LLIE techniques often rely on large models and multi-stage training.
- Existing methods' dependence on a single color space introduces instability and artifacts.
Entities
Institutions
- arXiv