A Fast Two-Stage Bilateral Filter Using Constant Time O(1) Histogram Generation

Bilateral Filtering (BF) is an effective edge-preserving smoothing technique in image processing. However, an inherent problem of BF for image denoising is that it is challenging to differentiate image noise and details with the range kernel, thus often preserving both noise and edges in denoising. This letter proposes a novel Dual-Histogram BF (DHBF) method that exploits an edge-preserving noise-reduced guidance image to compute the range kernel, removing isolated noisy pixels for better denoising results. Furthermore, we approximate the spatial kernel using mean filtering based on column histogram construction to achieve constant-time filtering regardless of the kernel radius' size and achieve better smoothing. Experimental results on multiple benchmark datasets for denoising show that the proposed DHBF outperforms other state-of-the-art BF methods.

Automated summary

Bilateral Filtering (BF) is an effective technique for edge-preserving smoothing in image processing, but it struggles with differentiating noise and details in image denoising. This letter proposes a novel Dual-Histogram BF (DHBF) method that utilizes a noise-reduced guidance image to compute the range kernel, removing noisy pixels for improved denoising results. Experimental results show that DHBF outperforms other state-of-the-art BF methods.