用查表法实现超低功耗彩色图像去噪，速度比传统模型快20倍
DnLUT: Ultra-Efficient Color Image Denoising via Channel-Aware Lookup Tables
- 通过通道混合与L型卷积设计，提取图像跨通道关联和空间特征
- 仅需500KB存储，能耗为DnCNN的0.1%，推理速度提升20倍
- 适合移动端或嵌入式设备部署，尤其对算力受限场景友好
深度神经网络虽显著提升了图像去噪性能，但在边缘设备上的部署仍受计算与内存开销制约。为此，我们提出DnLUT，一种基于查找表的超高效框架，可在极低资源消耗下实现高质量彩色图像去噪。核心创新包括：并行捕捉通道间相关性与空间依赖的成对通道混合器（PCM），以及最大化感受野覆盖且最小化存储开销的L型卷积设计。训练后将这些组件转换为优化的查找表，DnLUT仅需500KB存储空间，能耗仅为DnCNN的0.1%，推理速度提升20倍。大量实验表明，DnLUT在PSNR上优于所有现有查表方法超过1dB，确立了资源高效彩色去噪的新基准。项目代码见https://github.com/Stephen0808/DnLUT。
原文摘要 · Abstract (English)
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 at https://github.com/Stephen0808/DnLUT.
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