Application of Local Binary Pattern to Windowed Nonlocal Means Image Denoising

This paper presents a new technique to image denoising that mainly addresses the incurred high blurring when the windowed nonlocal means is applied to images corrupted by high noise levels. The proposed method is based on an enhanced weighting function that computes patches similarity based on both their intensities and structural features. The structural features are encoded using Local Binary Pattern (LBP) a well known texture descriptors. A new LBP based weighting function is proposed that has properties complementing the intensity based weighting function. The LBP based weighting function is used to modulate the intensity based weighting function. The modulated weights are noise independent and reflect the actual patch similarity. The method is found to be quantitatively and qualitatively effective in denoising images when corrupted by high noise levels. It suppresses image noise while preserving significant image characteristics.