[2603.20836] MERIT: Multi-domain Efficient RAW Image Translation

Computer Science > Computer Vision and Pattern Recognition arXiv:2603.20836 (cs) [Submitted on 21 Mar 2026] Title:MERIT: Multi-domain Efficient RAW Image Translation Authors:Wenjun Huang, Shenghao Fu, Yian Jin, Yang Ni, Ziteng Cui, Hanning Chen, Yirui He, Yezi Liu, Sanggeon Yun, SungHeon Jeong, Ryozo Masukawa, William Youngwoo Chung, Mohsen Imani View a PDF of the paper titled MERIT: Multi-domain Efficient RAW Image Translation, by Wenjun Huang and 12 other authors View PDF HTML (experimental) Abstract:RAW images captured by different camera sensors exhibit substantial domain shifts due to varying spectral responses, noise characteristics, and tone behaviors, complicating their direct use in downstream computer vision tasks. Prior methods address this problem by training domain-specific RAW-to-RAW translators for each source-target pair, but such approaches do not scale to real-world scenarios involving multiple types of commercial cameras. In this work, we introduce MERIT, the first unified framework for multi-domain RAW image translation, which leverages a single model to perform translations across arbitrary camera domains. To address domain-specific noise discrepancies, we propose a sensor-aware noise modeling loss that explicitly aligns the signal-dependent noise statistics of the generated images with those of the target domain. We further enhance the generator with a conditional multi-scale large kernel attention module for improved context and sensor-aware feature ...