NTIRE 2026 The 3rd Restore Any Image Model (RAIM) Challenge: Professional Image Quality Assessment (Track 1)

Guanyi Qin; Jie Liang; Bingbing Zhang; Lishen Qu; Ya-nan Guan; Hui Zeng; Lei Zhang; Radu Timofte; Jianhui Sun; Xinli Yue; Tao Shao; Huan Hou; Wenjie Liao; Shuhao Han; Jieyu Yuan; Chunle Guo; Chongyi Li; Zewen Chen; Yunze Liu; Jian Guo; Juan Wang; Yun Zeng; Bing Li; Weiming Hu; Hesong Li; Dehua Liu; Xinjie Zhang; Qiang Li; Li Yan; Wei Dong; Qingsen Yan; Xingcan Li; Shenglong Zhou; Manjiang Yin; Yinxiang Zhang; Hongbo Wang; Jikai Xu; Zhaohui Fan; Dandan Zhu; Wei Sun; Weixia Zhang; Kun Zhu; Nana Zhang; Kaiwei Zhang; Qianqian Zhang; Zhihan Zhang; William Gordon; Linwei Wu; Jiachen Tu; Guoyi Xu; Yaoxin Jiang; Cici Liu; Yaokun Shi

arXiv · 2604.12512

NTIRE 2026 The 3rd Restore Any Image Model (RAIM) Challenge: Professional Image Quality Assessment (Track 1)

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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/.

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Guanyi Qin, Jie Liang, Bingbing Zhang, Lishen Qu, Ya-nan Guan, Hui Zeng, Lei Zhang, Radu Timofte, Jianhui Sun, Xinli Yue, Tao Shao, Huan Hou, Wenjie Liao, Shuhao Han, Jieyu Yuan, Chunle Guo, Chongyi Li, Zewen Chen, Yunze Liu, Jian Guo, Juan Wang, Yun Zeng, Bing Li, Weiming Hu, Hesong Li, Dehua Liu, Xinjie Zhang, Qiang Li, Li Yan, Wei Dong, Qingsen Yan, Xingcan Li, Shenglong Zhou, Manjiang Yin, Yinxiang Zhang, Hongbo Wang, Jikai Xu, Zhaohui Fan, Dandan Zhu, Wei Sun, Weixia Zhang, Kun Zhu, Nana Zhang, Kaiwei Zhang, Qianqian Zhang, Zhihan Zhang, William Gordon, Linwei Wu, Jiachen Tu, Guoyi Xu, Yaoxin Jiang, Cici Liu, Yaokun Shi. 2026-04-14. NTIRE 2026 The 3rd Restore Any Image Model (RAIM) Challenge: Professional Image Quality Assessment (Track 1). https://arxiv.org/abs/2604.12512

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