Paint-Anything introduces a unified hex-prompt interface that allows users to specify any 24‑bit hex color for both image generation and editing. The method trains on a new Paint‑500K dataset created from real images with object grounding, perceptual color labeling, and editing‑pair synthesis, and supplements this with pure‑color anchors to address shadow‑induced color inaccuracies. Evaluated on the newly proposed Any Color Benchmark (ACBench), Paint‑Anything achieves significant improvements over the base FLUX.2‑4B model, boosting T2I and editing scores by 85.3 % and 28.3 % respectively, and outperforms competing methods on the CompColor metric.
By Ji Xie, Dewei Zhou, Xinyu Huang, Zhennan Chen, Xun Wang
arXiv:2606. 00188v1 Announce Type: cross Abstract: While current multimodal models are proficient at open-ended visual editing, executing precise single-answer edits remains an important obstacle.
By Kai Xu, Ellis Brown, Shrikar Madhu, Rob Fergus, He He, Saining Xie
arXiv:2608.29925v1 Announce Type: new
Abstract: Controllable image relighting is an important problem in image editing, and hand-drawn scribbles provide an intuitive interface for specifying the desi...
By Xuanpu Zhang, Xuesong Niu, Haoxiang Cao, Ruidong Chen, Jianhao Zeng, Changqian Yu
arXiv:2607. 08185v1 Announce Type: cross Abstract: Enhancing images to make them visually appealing is a persistent challenge in computer vision.
By David Serrano-Lozano, Luis Herranz, Michael S. Brown, Javier Vazquez-Corral
arXiv:2609.14495v1 Announce Type: new
Abstract: Image colorization is an inherently ill-posed task, since a single grayscale image may correspond to multiple plausible colorized results. Consequently...
By Yunkai Zhuang, Qihang Yan, Zicheng Zhang, Guangtao Zhai
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.
By Alexandru Brateanu, Tingting Mu, Codruta Ancuti, Cosmin Ancuti
arXiv:2608.21847v1 Announce Type: new
Abstract: Low-light image enhancement (LLIE) must correct ambiguous exposure without overwriting structure already supported by the input. Generative transport c...
By Yi Ai, Zheng Chen, Yuanhao Cai, Yulun Zhang, Xiaokang Yang
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.
Diffusion Editing with Soft Mask: Pixel Level Redo of Image and Video with Adjustable Strength introduces SoftPaint, a zero‑shot sampling method that uses soft masks to provide continuous, pixel‑level control over edits in diffusion models. The approach employs a Langevin‑iteration sampler that respects per‑pixel mask strengths, enabling smooth edits from preserving to fully re‑synthesizing content across image and video backbones. SoftPaint is gradient‑free, memory‑efficient, and works universally with existing diffusion models.
By Candi Zheng, Yuan Lan
arXiv:2609.21169v1 Announce Type: cross
Abstract: Independent control of tonescale regions (e.g., shadows, highlights) is essential for painters, photographers and cinematographers to bring 2D images...
By Trevor D. Canham, Abhijith Punnappurath, Michael S. Brown
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
The paper proposes replacing traditional one-dimensional correlated color temperature (CCT) interpolation with a two-dimensional chromaticity approach for in-camera colorimetric mapping. By training a lightweight multi-layer perceptron (MLP) on a lightbox calibration that includes representative LED sources, the method achieves a 22% average reduction in angular reproduction error across diverse LED lighting. It remains compatible with conventional illuminants, handles multi-illuminant scenes, and can be deployed in real time with minimal computational overhead.
By SaiKiran Tedla, Joshua E. Little, Hakki Can Karaimer, Michael S. Brown