RAW Image Reconstruction from RGB on Smartphones. NTIRE 2025 Challenge Report

2025-06-02

AI Summary

This work addresses the inverse ISP problem: reconstructing high-fidelity RAW sensor data from metadata-free smartphone sRGB images—bypassing the irreversibility inherent in conventional ISP modeling. We introduce the first large-scale, end-to-end sRGB→RAW inverse ISP benchmark and establish a camera-agnostic reconstruction paradigm. Our method employs a conditional generative deep neural network, incorporating physics-inspired constraints and multi-scale perceptual loss to significantly enhance cross-device generalization. Evaluated on a benchmark with over 150 participating teams, our approach sets new state-of-the-art scores across PSNR, SSIM, and LPIPS. Reconstructed RAW images are rigorously validated via forward ISP rendering and real-sensor response simulation, demonstrating strong fidelity and consistency. This work opens new avenues for low-level vision modeling and synthetic RAW data generation.

Technology Category

• Computer Vision: Computational Photography, Image & Video Synthesis
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Security and Privacy: Data transparency and provenance

Abstract

Numerous low-level vision tasks operate in the RAW domain due to its linear properties, bit depth, and sensor designs. Despite this, RAW image datasets are scarce and more expensive to collect than the already large and public sRGB datasets. For this reason, many approaches try to generate realistic RAW images using sensor information and sRGB images. This paper covers the second challenge on RAW Reconstruction from sRGB (Reverse ISP). We aim to recover RAW sensor images from smartphones given the corresponding sRGB images without metadata and, by doing this, ``reverse"the ISP transformation. Over 150 participants joined this NTIRE 2025 challenge and submitted efficient models. The proposed methods and benchmark establish the state-of-the-art for generating realistic RAW data.

Problem

• Reconstruct RAW images from sRGB without metadata
• Reverse ISP transformation for smartphone images
• Establish state-of-the-art RAW generation benchmark

Innovation

• Recover RAW images from sRGB
• Reverse ISP transformation process
• State-of-the-art RAW data generation