NTIRE 2026 The Second Challenge on Day and Night Raindrop Removal for Dual-Focused Images: Methods and Results

2026-04-12

AI Summary

This work addresses the challenge of raindrop removal from real-world images under complex conditions involving diurnal lighting variations and dual-focus settings. To this end, it establishes the first systematic benchmark tailored to such realistic scenarios. Built upon the large-scale Raindrop Clarity dataset—comprising 14,139 training images—the study organized an international challenge that provided unified training, validation, and test benchmarks to advance deep learning–based image restoration methods. The competition attracted 168 participating teams, with 17 submitting valid solutions that significantly improved deraining performance on the test set, thereby setting a new state-of-the-art benchmark and substantially enhancing algorithmic robustness under real-world, complex imaging conditions.

Technology Category

• Computer Vision: Adversarial Attacks & Robustness
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Responsible Web: Algorithmic accountability and transparency on the web

Abstract

This paper presents an overview of the NTIRE 2026 Second Challenge on Day and Night Raindrop Removal for Dual-Focused Images. Building upon the success of the first edition, this challenge attracted a wide range of impressive solutions, all developed and evaluated on our real-world Raindrop Clarity dataset~\cite{jin2024raindrop}. For this edition, we adjust the dataset with 14,139 images for training, 407 images for validation, and 593 images for testing. The primary goal of this challenge is to establish a strong and practical benchmark for the removal of raindrops under various illumination and focus conditions. In total, 168 teams have registered for the competition, and 17 teams submitted valid final solutions and fact sheets for the testing phase. The submitted methods achieved strong performance on the Raindrop Clarity dataset, demonstrating the growing progress in this challenging task.

Problem

• raindrop removal
• day and night
• dual-focused images
• image restoration
• real-world dataset

Innovation

• raindrop removal
• dual-focused images
• day and night imaging
• real-world dataset
• image restoration