Edge-guided Low-light Image Enhancement with Inertial Bregman Alternating Linearized Minimization: Analysis and Results
The author introduces an edge-guided Retinex model for enhancing low-light images using a novel inertial Bregman alternating linearized minimization algorithm.
LoLiSRFlow: Joint Single Image Low-light Enhancement and Super-resolution via Cross-scale Transformer-based Conditional Flow
Proposing LoLiSRFlow for joint low-light enhancement and super-resolution tasks using a transformer-based conditional flow network.
Zero-Reference Low-Light Enhancement Framework with Physical Quadruple Priors
Proposing a zero-reference low-light enhancement framework using physical quadruple priors to achieve superior performance in various scenarios.
Enhancing Neural ISP for Low Light with Latent Diffusion Models
Utilizing pre-trained latent diffusion models to enhance neural ISP for low-light images.
Effiziente Bildverbesserung bei schwachem Licht durch Troublemaker-Lernen
Effiziente Bildverbesserung bei schwachem Licht durch die Kombination von Troublemaker-Lernen (TML) und Global Dynamic Convolution (GDC).
LYT-Net: A Lightweight Transformer-Based Network for Efficient Low-Light Image Enhancement
LYT-Net, a lightweight transformer-based network, achieves state-of-the-art performance on low-light image enhancement tasks while maintaining high computational efficiency.
CLIP-Fourier Guided Wavelet Diffusion for Robust Low-light Image Enhancement
A novel and robust low-light image enhancement method using CLIP-Fourier Guided Wavelet Diffusion (CFWD) that leverages multimodal visual-language information in the frequency domain to effectively bridge the gap between degraded and normal domains.
An Efficient Network for Low-light Image Enhancement Using a Novel Trainable Color Space
A novel trainable color space called Horizontal/Vertical-Intensity (HVI) is proposed to decouple image brightness and color, enabling efficient low-light image enhancement.
Retinex-based Mamba Architecture for Efficient Low-light Image Enhancement
A novel Retinex-based Mamba architecture that leverages the computational efficiency of State Space Models and a Fused-Attention mechanism to effectively enhance low-light images while maintaining image quality.
Enhancing Low-Light Images with Atmospheric Scattering-Driven Attention and Gamma Correction Priors
The proposed CPGA-Net+ model achieves state-of-the-art performance in low-light image enhancement by incorporating an attention mechanism driven by the Atmospheric Scattering Model and integrating gamma correction into the local processing branch.
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