Impulse Noise
Reduction for Texture Images Using Real Word Spelling Correction Algorithm and
Local Binary Patterns
Shervan
Fekri-Ershad1, Seyed Fakhrahmad2, and Farshad Tajeripour2
1Faculty of
Computer Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran
2Department of
Computer Science and Engineering, Shiraz University, Shiraz, Iran
Abstract: 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.
Keywords:
Image noise reduction, local binary pattern, real word spelling correction,
texture analysis.