Improving Non-local Video Denoising with Local Binary Patterns and Image Quantization
Welinton A. Contato, Tiago Santana de Nazaré, Gabriel B. Paranhos da Costa, Moacir Antonelli Ponti, Joao E. S. Batista Neto · 2016
The most challenging aspect of video and image denoising is to preserve texture and small details, while filtering out noise. To tackle such problem, we present two novel variants of the 3D Non-Local Means (NLM3D) which are suitable for videos and 3D images. The first proposed algorithm computes texture patterns for each pixel by using the LBP-TOP descriptor to modify the NLM3D weighting function. It also uses MSB (Most Significant Bits) quantization to improve robustness to noise. The second proposed algorithm filters homogeneous and textured regions differently. It analyses the percentage of non-uniform LBP patterns of a region to determine whether or not the region exhibits textures and/or small details. Quantitative and qualitative experiments indicate that the proposed approaches outperform well known methods for the video denoising task, especially in the presence of textures and small details.