NTIRE 2026 The 3rd Restore Any Image Model (RAIM) Challenge: Professional Image Quality Assessment (Track 1)
Abstract
In this paper, we present an overview of the NTIRE 2026 challenge on the 3rd Restore Any Image Model in the Wild, specifically focusing on Track 1: Professional Image Quality Assessment. Conventional Image Quality Assessment (IQA) typically relies on scalar scores. By compressing complex visual characteristics into a single number, these methods fundamentally struggle to distinguish subtle differences among uniformly high-quality images. Furthermore, they fail to articulate why one image is superior, lacking the reasoning capabilities required to provide guidance for vision tasks. To bridge this gap, recent advancements in Multimodal Large Language Models (MLLMs) offer a promising paradigm. Inspired by this potential, our challenge establishes a novel benchmark exploring the ability of MLLMs to mimic human expert cognition in evaluating high-quality image pairs. Participants were tasked with overcoming critical bottlenecks in professional scenarios, centering on two primary objectives: (1) Comparative Quality Selection: reliably identifying the visually superior image within a high-quality pair; and (2) Interpretative Reasoning: generating grounded, expert-level explanations that detail the rationale behind the selection. In total, the challenge attracted nearly 200 registrations and over 2,500 submissions. The top-performing methods significantly advanced the state of the art in professional IQA. The challenge dataset is available at https://github.com/narthchin/RAIM-PIQA, and the official homepage is accessible at https://www.codabench.org/competitions/12789/.
Keywords
Cite
@article{arxiv.2604.12512,
title = {NTIRE 2026 The 3rd Restore Any Image Model (RAIM) Challenge: Professional Image Quality Assessment (Track 1)},
author = {Guanyi Qin and Jie Liang and Bingbing Zhang and Lishen Qu and Ya-nan Guan and Hui Zeng and Lei Zhang and Radu Timofte and Jianhui Sun and Xinli Yue and Tao Shao and Huan Hou and Wenjie Liao and Shuhao Han and Jieyu Yuan and Chunle Guo and Chongyi Li and Zewen Chen and Yunze Liu and Jian Guo and Juan Wang and Yun Zeng and Bing Li and Weiming Hu and Hesong Li and Dehua Liu and Xinjie Zhang and Qiang Li and Li Yan and Wei Dong and Qingsen Yan and Xingcan Li and Shenglong Zhou and Manjiang Yin and Yinxiang Zhang and Hongbo Wang and Jikai Xu and Zhaohui Fan and Dandan Zhu and Wei Sun and Weixia Zhang and Kun Zhu and Nana Zhang and Kaiwei Zhang and Qianqian Zhang and Zhihan Zhang and William Gordon and Linwei Wu and Jiachen Tu and Guoyi Xu and Yaoxin Jiang and Cici Liu and Yaokun Shi},
journal= {arXiv preprint arXiv:2604.12512},
year = {2026}
}
Comments
NTIRE Challenge Report. Accepted by CVPRW 2026