While deep neural networks have revolutionized image denoising capabilities, their deployment on edge devices remains challenging due to substantial computational and memory requirements. To this end, we present DnLUT, an ultra-efficient lookup table-based framework that achieves high-quality color image denoising with minimal resource consumption. Our key innovation lies in two complementary components: a Pairwise Channel Mixer (PCM) that effectively captures inter-channel correlations and spatial dependencies in parallel, and a novel L-shaped convolution design that maximizes receptive field coverage while minimizing storage overhead. By converting these components into optimized lookup tables post-training, DnLUT achieves remarkable efficiency - requiring only 500KB storage and 0.1% energy consumption compared to its CNN contestant DnCNN, while delivering 20X faster inference. Extensive experiments demonstrate that DnLUT outperforms all existing LUT-based methods by over 1dB in PSNR, establishing a new state-of-the-art in resource-efficient color image denoising. The project is available atthis https URL.
View on arXiv@article{yang2025_2503.15931,
title={ DnLUT: Ultra-Efficient Color Image Denoising via Channel-Aware Lookup Tables },
author={ Sidi Yang and Binxiao Huang and Yulun Zhang and Dahai Yu and Yujiu Yang and Ngai Wong },
journal={arXiv preprint arXiv:2503.15931},
year={ 2025 }
}