22‐3: <i>Distinguished Student Paper:</i> BDLUT: Blind Image Denoising with Hardware‐Optimized Look‐Up Tables

We propose BDLUT(‐D) that novelly combines blind denoising with hardware‐optimized lookup tables (LUTs) for resource‐efficient edge computing. While BDLUT describes the LUT‐based network architecture, BDLUT‐D represents BDLUT trained with a specialized noise degradation model. BDLUT‐D achieves superior denoising, outperforming state‐of‐the‐art (SOTA) LUT methods by up to 2.42 dB on mixed‐noise‐intensity benchmark datasets using only 66KB storage. Our FPGA implementation demonstrates significant hardware efficiency, reducing logic resource consumption by over 10x and storage requirements by 75% compared to DNN accelerators, while achieving 57% higher speed than bilateral filtering methods.