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.
Vision-language alignment from compressed image representations using 2D Gaus- sian Splatting,
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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.
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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.
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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.