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 × 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 .