Image Denoising via 2-D FIR Filtering Approach

Image Denoising via 2-D FIR Filtering Approach

Jingyu Hua (Zhejiang University of Technology, China) and Wankun Kuang (Zhejiang University of Technology, China)
DOI: 10.4018/978-1-4666-3958-4.ch007
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Abstract

Image denoising has received much concern for decades. One of the simplest methods for image denoising is the 2-D FIR lowpass filtering approach. Firstly, the authors make a comparative study of the conventional lowpass filtering approach, including the classical mean filter and three 2-D FIR LowPass Filters (LPF) designed by McClellan transform. Then an improved method based on learning method is presented, where pixels are filtered by five edge-oriented filters, respectively, facilitated to their edge details. Differential Evolution Particle Swarm Optimization (DEPSO) algorithm is exploited to refine those filters. Computer simulation demonstrates that the proposed method can be superior to the conventional filtering method, as well as the modern Bilateral Filtering (BF) and the Stochastic Denoising (SD) method.
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Main Focus Of The Chapter

Firstly, we made a brief introduction of a popular and effective method to evaluate the image quality, i.e., the Structural SIMilarity Index (SSIM), which laid the foundation of all the evaluation of the subsequent sections. Then, conventional 2-D FIR lowpass filtering approaches, both the mean filter and other LPFs designed by McClellan transform are investigated. However, conventional approaches do not take into consideration the local details of the corrupted image. To tackle this issue, we present a method to design five edge-oriented 2-D FIR LPFs, where the pixels are grouped into five classes according to edge details before they have been filtered and DEPSO algorithm is exploited to refine those filters.

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