Hybrid-LUT: Channel-Aware Hybrid Lookup Table and Filtering for Efficient Image Restoration

Lookup table (LUT)-based image denoising methods haveattracted increasing attention due to their high efficiency and hardware-friendly properties. However, existing RGB-LUT approaches require threeidentical LUTs to process RGB channels in parallel, resulting in largeon-chip SRAM consumption. A simple alternative is to apply LUT pro-cessing only to the luminance (Y) channel in the YUV color space toreduce memory usage. However, this naive strategy leads to degradedrestoration quality, since ignoring the chrominance (UV) channels in-troduces color distortion and residual artifacts. In this work, we pro-pose Hybrid-LUT, a YUV-based asymmetric channel-processing frame-work that combines LUT and filtering in a unified design. Specifically, amulti-band LUT branch with pixel-level weight fusion is applied to theY channel to recover fine textures, while lightweight filtering is used forthe UV channels to maintain color consistency. This design reduces LUTstorage by two-thirds compared with RGB-LUT methods while main-taining the same runtime throughput. Extensive experiments show thatHybrid-LUT achieves state-of-the-art (SOTA) performance across mul-tiple benchmarks with only 421 KB of storage. In particular, our methodsurpasses existing LUT-based denoising approaches by at least 0.63 dBCPSNR on real-world datasets, demonstrating its effectiveness for imagedenoising on resource-constrained edge devices.