arXiv:2604. 23094v2 Announce Type: replace-cross Abstract: Portrait relighting is a low-level vision problem in which physically plausible illumination transfer, identity preservation, and compact real-time inference must be considered together.
By Qian Huang, Mayoore Selvarasa Jaiswal, Zhen Zhong, Rochelle Pereira, Jianyuan Min
Lumos3D introduces a pose‑free, single‑forward framework for restoring 3D scenes captured in low light. It employs a cross‑illumination distillation scheme where a frozen teacher network provides accurate geometric cues to a student model, and a specialized Lumos loss refines the 3D Gaussian reconstruction. Trained on a single dataset, Lumos3D can directly restore illumination and structure from unposed, low‑light multi‑view images without per‑scene optimization, achieving competitive results on real‑world datasets.
By Hanzhou Liu, Peng Jiang, Jia Huang, Mi Lu
arXiv:2607. 03013v1 Announce Type: cross Abstract: Images captured by consumer electronic devices, such as mobile phones and digital cameras, often suffer from low-light degradation due to sensor limitations and imaging pipelines, which degrades visual quality and affects downstream vision tasks.
By Wanshu Fan, Xiangyu Li, Cong Wang, Kin-man Lam, Xin Yang, Haiyan Zhang, Dongsheng Zhou
Consist‑Retinex introduces a one‑step noise‑emphasized consistency training framework for Retinex‑based low‑light image enhancement. It first decomposes images into reflectance and illumination maps using a Retinex Transformer Decomposition Network, then trains two conditional consistency models with a dual objective that blends trajectory consistency and ground‑truth alignment. The method employs adaptive noise‑emphasized fixed‑point sampling to focus supervision near the inference endpoint, achieving state‑of‑the‑art VE‑LOL‑L scores on paired and unpaired low‑light benchmarks while reducing sampling and training costs.
By Jian Xu, Wei Chen, Shigui Li, Delu Zeng, John Paisley, Qibin Zhao
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.
By Cheng-Yen Hsiao, Jing-Ming Guo
arXiv:2609.23442v1 Announce Type: new
Abstract: Diffusion models generate high-quality images, yet often violate the physical laws governing mirror reflections. Reflections often suffer from geometri...
By Shuheng Ge, Hongwei Ren, Li Zhang, Xiangqian Wu