BC-IHV: Conditioning the Color Space for Stable Rectified-Flow Low-Light Enhancement
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
The Flow has not summarised this story yet — read it at arXiv Computer Vision.
Low-light imaging often introduces color bias caused by the low signal-to-noise ratio and the image formation process. Although recent low-light image enhancement methods have achieved strong brightness recovery, faithful color restoration remains challenging, manifesting as overall color bias together with local under- and over-saturation.
EventSeeColor is a method that reconstructs color images from monochrome event camera data. It combines event‑reconstructed grayscale images with aligned events to guide a diffusion colorizer, using semantic context and region‑balanced chroma supervision to improve color fidelity. Evaluations on two real event datasets show that EventSeeColor outperforms existing methods in structural similarity, perceptual scores, and chromatic‑region color difference.
The paper investigates how knowledge distillation from event cameras to RGB images can alter the inductive biases of convolutional neural networks. By transferring learning from the event domain, the authors find that models gain color invariance, a shape bias, and improved robustness to high‑frequency noise, largely due to reduced reliance on texture and increased emphasis on edge‑based object shape. These changes are evidenced by early‑layer processing differences and a spectral trade‑off between robustness to missing high‑frequency content and vulnerability to its contamination or geometric disruption.
IDM-Net is a lightweight Illumination-Decoupled Modulation Network designed for low-light image enhancement. It uses a dual-encoder architecture: a structure encoder extracts multi-scale appearance features from the RGB image, while a lightweight illumination encoder learns illumination priors from the decoupled luminance channel. An Illumination-Guided Modulation module injects these priors into the decoder via spatially adaptive affine modulation, and a Feature Refinement Block progressively suppresses artifacts and recovers fine details, achieving competitive performance on standard benchmarks with good balance between quality and efficiency.
The paper introduces PRIN, a Perceptual-Structural Retinex-Inspired Network designed for low‑light image enhancement. PRIN uses a shared encoder with asymmetric decoders to separately refine reflectance and illumination components, applying attention‑guided wavelet refinement for textures and edge preservation in the reflectance branch and spatially coherent brightness adjustment in the illumination branch. A curve‑based post‑composition head further corrects tone and exposure, achieving state‑of‑the‑art SSIM, LPIPS, and PSNR results on multiple LOL datasets.
arXiv:2604. 10359v3 Announce Type: replace-cross Abstract: Low-light image enhancement (LLIE) aims to restore natural visibility, color fidelity, and structural detail under severe illumination degradation.