The paper introduces Colorist, a data‑augmentation method that uses classical statistical color matching to generate domain‑shifted medical images. By applying global mean‑standard‑deviation matching in RGB space, Colorist creates structurally intact variations without neural networks, outperforming deep generative models in fidelity and color alignment. Across multiple medical imaging datasets, it boosts balanced accuracy by up to 9% over state‑of‑the‑art domain‑generalization regularizers and 13% over no augmentation, while reducing computational cost and preserving anatomical structure.
By Sebastian Doerrich, Francesco Di Salvo, Shyam Nandan Rai, Marco Lents, Christian Ledig
Low-light image enhancement algorithms (LIEAs) aim to improve the visibility of images captured under poor illumination. However, the enhancement process often introduces artifacts such as noise amplification, color shift, structural damage, and over-exposure, which degrade the perceptual quality of the enhanced images.
arXiv:2609.25716v1 Announce Type: new
Abstract: Reference-based image quality assessment (IQA) metrics aim to reflect how humans perceive the perceptual distance between a pair of images. To learn ho...
By Jaihyun Lew, Mingi Jung, Minjun Park, Wooseok Song, Sungroh Yoon
arXiv:2610.32473v1 Announce Type: cross
Abstract: Dominant color extraction is a fundamental task in computer vision, with applications in design, image retrieval, and content recommendation. However...
By Uldana Shyndali, Nurdaulet Toleugaliyev, Kuat Zharykbayev, Zarina Abildasheva, Pakizar Shamoi
The paper presents a data‑driven method for estimating perceptual color differences by training regression models on human similarity judgments of 2,000 color pairs. Using COLIBRI fuzzy linguistic categories as features, linear regression achieves an R² of 0.595, outperforming RGB and HSI representations. The best result, an R² of 0.703, is obtained with LightGBM on a combined representation, showing that graded perceptual categories improve color‑difference prediction.
By Elnara Kadyrgali, Muragul Muratbekova, Adilet Yerkin, Nuray Toganas, Ayan Igali, Malika Ziyada, Aruzhan Burambekova, Jamaladdin Hasanov, Pakizar Shamoi
arXiv:2610.17661v2 Announce Type: replace
Abstract: The rapid development of mobile imaging and social platforms has led to explosive growth in user-generated content images, making their systematic...
By Shuhan Xia, Yu Chen, Xiang Wang, Shuai Li, Peipie Li, Zekun Li
arXiv:2607. 12375v1 Announce Type: cross Abstract: Image Quality Assessment (IQA) in open-world environments remains challenging due to limited generalization and interpretability.
By Jinjian Wu, Jiaqi Tang, Wei Wei, Yingying Yan, Jianmin Chen, Botong Geng, Lei Zhang, Qifeng Chen
VGIQA is a new image quality assessment model that provides both local and global quality evaluations. It uses a two-stage training process—initial cold-start followed by reinforcement learning—with local quality reward functions. The model outperforms existing large multimodal language models on the newly introduced VGSR-2K dataset and improves performance on degradation perception tasks.
By Weichang Wang, Liyu Chen, Shuyue Guan, Jie Hu, Fei Gao, Wei Li, Nannan Wang
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
VGIQA is a new image quality assessment model that provides both local and global quality evaluations. It uses a two‑stage training process—starting with a cold‑start phase and then reinforcement learning guided by local quality reward functions. The model demonstrates superior performance on the newly introduced VGSR‑2K dataset, outperforming existing open‑source multi‑modal large language models such as Qwen2.5‑VL‑72B‑Instruct and Qwen3‑VL‑32B‑Instruct, and it also improves degradation perception tasks.
arXiv:2608. 10798v1 Announce Type: cross Abstract: Most image colorization systems operate in $Lab$ space by predicting chroma ($ab$) while preserving an input-derived luminance channel ($L$).
By Swarnim Maheshwari, Syed Imam Ali, Vineeth N. Balasubramanian
LLaVA‑Assessor is a unified large multi‑modal model (LMM) designed for visual quality assessment, combining image and video inputs. It introduces a two‑task framework—quality interpretation and quality scoring—supported by an adaptive architecture, a rigorous human‑annotated dataset, and a machine‑synthesized data expansion pipeline. The model employs a prompt‑disentanglement strategy to stabilize multi‑task training and achieves strong performance across 11 quality scoring test sets and 4 interpretation benchmarks.
By Ziheng Jia, Zicheng Zhang, Jiaying Qian, Guangtao Zhai, Xiongkuo Min