# NTIRE 2026 The 3rd Restore Any Image Model (RAIM) Challenge: AI Flash Portrait (Track 3) arXiv: https://arxiv.org/abs/2604.11230 · HTML: https://mlsanity.com/paper/2604.11230/ ## Key facts - Cited by 12 papers in the map (data through 14 Oct 2026). - Builds on 13 of its 37 references that are in the map. - First posted on arXiv 13 Apr 2026. ## Builds on (most cited first, 13 of 13) - [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018) - [Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data](https://mlsanity.com/paper/2107.10833/) (2021) - [Deep Retinex Decomposition for Low-Light Enhancement](https://mlsanity.com/paper/1808.04560/) (2018) - [Kindling the Darkness: A Practical Low-light Image Enhancer](https://mlsanity.com/paper/1905.04161/) (2019) - [Towards Real-World Blind Face Restoration with Generative Facial Prior](https://mlsanity.com/paper/2101.04061/) (2021) - [Towards Robust Blind Face Restoration with Codebook Lookup Transformer](https://mlsanity.com/paper/2206.11253/) (2022) - [The Eleventh NTIRE 2026 Efficient Super-Resolution Challenge Report](https://mlsanity.com/paper/2604.03198/) (2026) - [NTIRE 2026 Challenge on Bitstream-Corrupted Video Restoration: Methods and Results](https://mlsanity.com/paper/2604.06945/) (2026) - [NTIRE 2026 The 3rd Restore Any Image Model (RAIM) Challenge: Multi-Exposure Image Fusion in Dynamic Scenes (Track 2)](https://mlsanity.com/paper/2604.09030/) (2026) - [NTIRE 2026 Challenge on Single Image Reflection Removal in the Wild: Datasets, Results, and Methods](https://mlsanity.com/paper/2604.10321/) (2026) - [NTIRE 2026 Challenge on Short-form UGC Video Restoration in the Wild with Generative Models: Datasets, Methods and Results](https://mlsanity.com/paper/2604.10551/) (2026) - [NTIRE 2026 The Second Challenge on Day and Night Raindrop Removal for Dual-Focused Images: Methods and Results](https://mlsanity.com/paper/2604.10634/) (2026) - [The Second Challenge on Real-World Face Restoration at NTIRE 2026: Methods and Results](https://mlsanity.com/paper/2604.10532/) (2026) ## Built on by (most cited first, 12 of 12) - [NTIRE 2026 The 3rd Restore Any Image Model (RAIM) Challenge: Professional Image Quality Assessment (Track 1)](https://mlsanity.com/paper/2604.12512/) (2026) - [The Fourth Challenge on Image Super-Resolution (×4) at NTIRE 2026: Benchmark Results and Method Overview](https://mlsanity.com/paper/2604.14558/) (2026) - [NTIRE 2026 Challenge on Video Saliency Prediction: Methods and Results](https://mlsanity.com/paper/2604.14816/) (2026) - [NTIRE 2026 Rip Current Detection and Segmentation (RipDetSeg) Challenge Report](https://mlsanity.com/paper/2604.17070/) (2026) - [Low Light Image Enhancement Challenge at NTIRE 2026](https://mlsanity.com/paper/2604.17669/) (2026) - [The First Challenge on Remote Sensing Infrared Image Super-Resolution at NTIRE 2026: Benchmark Results and Method Overview](https://mlsanity.com/paper/2604.21312/) (2026) - [The First Challenge on Mobile Real-World Image Super-Resolution at NTIRE 2026: Benchmark Results and Method Overview](https://mlsanity.com/paper/2604.17306/) (2026) - [Robust Deepfake Detection, NTIRE 2026 Challenge: Report](https://mlsanity.com/paper/2604.24163/) (2026) - [The First Controllable Bokeh Rendering Challenge at NTIRE 2026](https://mlsanity.com/paper/2605.05510/) (2026) - [The Third Challenge on Image Denoising at NTIRE 2026: Methods and Results](https://mlsanity.com/paper/2606.16031/) (2026) - [NTIRE 2026 Challenge on Efficient Low Light Image Enhancement: Methods and Results](https://mlsanity.com/paper/2605.02212/) (2026) - [NTIRE 2026 Low-light Enhancement: Twilight Cowboy Challenge](https://mlsanity.com/paper/2608.09782/) (2026) ## Abstract (arXiv) In this paper, we present a comprehensive overview of the NTIRE 2026 3rd Restore Any Image Model (RAIM) challenge, with a specific focus on Track 3: AI Flash Portrait. Despite significant advancements in deep learning for image restoration, existing models still encounter substantial challenges in real-world low-light portrait scenarios. Specifically, they struggle to achieve an optimal balance among noise suppression, detail preservation, and faithful illumination and color reproduction. To bridge this gap, this challenge aims to establish a novel benchmark for real-world low-light portrait restoration. We comprehensively evaluate the proposed algorithms utilizing a hybrid evaluation system that integrates objective quantitative metrics with rigorous subjective assessment protocols. For this competition, we provide a dataset containing 800 groups of real-captured low-light portrait data. Each group consists of a 1K-resolution low-light input image, a 1K ground truth (GT), and a 1K person mask. This challenge has garnered widespread attention from both academia and industry, attracting over 100 participating teams and receiving more than 3,000 valid submissions. This report details the motivation behind the challenge, the dataset construction process, the evaluation metrics, and the various phases of the competition. The released dataset and baseline code for this track are publicly available from the same hrefhttps://github.com/zsn1434/AIFlash-BaseLine/tree/mainGitHub repository, and the official challenge webpage is hosted on hrefhttps://www.codabench.org/competitions/12885/CodaBench.