AIGS-Net builds an input-adaptive 2D Gaussian Splatting illumination field modulated by luminance statistics, rendered via alpha compositing, plus a zero-parameter multiscale encoder and regularizers to enhance low-light images on LOL and LSRW.
A lightweight real-time low-light enhancement network for embedded automotive vision systems,
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
GLFS represents illumination via anisotropic Gaussian basis functions, adds physics-guided biases to self-attention in a Vision Transformer, and introduces color-vector angular and luminance-edge losses to achieve SOTA unsupervised low-light enhancement.
citing papers explorer
-
AIGS-Net: Compact Illumination Field Modeling via 2D Gaussian Splatting for Fast Low-Light Image Enhancement
AIGS-Net builds an input-adaptive 2D Gaussian Splatting illumination field modulated by luminance statistics, rendered via alpha compositing, plus a zero-parameter multiscale encoder and regularizers to enhance low-light images on LOL and LSRW.
-
Gaussian Light Field Splatting: A Physical Prior-Driven Vision Transformer for Unsupervised Low-Light Image Enhancement
GLFS represents illumination via anisotropic Gaussian basis functions, adds physics-guided biases to self-attention in a Vision Transformer, and introduces color-vector angular and luminance-edge losses to achieve SOTA unsupervised low-light enhancement.