BDLUT: Blind image denoising with hardware-optimized look-up tables

Abstract

Denoising sensor-captured images on edge display devices remains challenging due to deep neural networks' (DNNs) high computational overhead and synthetic noise training limitations. This work proposes BDLUT(-D), a novel blind denoising method combining optimized lookup tables (LUTs) with hardware-centric design. While BDLUT describes the LUT-based network architecture, BDLUT-D represents BDLUT trained with a specialized noise degradation model. Designed for edge deployment, BDLUT(-D) eliminates neural processing units (NPUs) and functions as a standalone ASIC IP solution. Experimental results demonstrate BDLUT-D achieves up to 2.42 dB improvement over state-of-the-art LUT methods on mixed-noise-intensity benchmarks, requiring only 66 KB storage. FPGA implementation shows over 10 $\times$ reduction in logic resources, 75% less storage compared to DNN accelerators, while achieving 57% faster processing than traditional bilateral filtering methods. These optimizations enable practical integration into edge scenarios like low-cost webcam enhancement and real-time 4 K-to-4 K denoising without compromising resolution or latency. By enhancing silicon efficiency and removing external accelerator dependencies, BDLUT(-D) establishes a new standard for practical edge imaging denoising. Implementation is available at https://github.com/HKU-LiBoyu/BDLUT.