Impulse Noise Reduction for Texture Images Using Real Word Spelling Correction Algorithm and Local Binary Patterns

Noise Reduction is one of the most important steps in very broad domain of image processing applications such as face identification, motion tracking, visual pattern recognition and etc. Texture images are covered a huge number of images where are collected as database in these applications. In this paper an approach is proposed for noise reduction in texture images which is based on real word spelling correction theory in natural language processing. The proposed approach is included two main steps. In the first step, most similar pixels to noisy desired pixel in terms of textural features are generated using local binary pattern. Next, best one of the candidates is selected based on two-gram algorithm. The quality of the proposed approach is compared with some of state of the art noise reduction filters in the result part. High accuracy, Low blurring effect, and low computational complexity are some advantages of the proposed approach.