Image Denoising via 2-D FIR Filtering Approach

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. DOI: 10.4018/978-1-4666-3958-4.ch007 Image Denoising via 2-D FIR Filtering Approach 185 the original visual information as much as possible has become a major concern for decades. Generally, the deblurring issue can be viewed as a denoising problem and typical noise models include the Gaussian noise, the salt and pepper noise, the speckle noise and the Brownian noise (Lim, 1990). In this chapter, we focus on the lowpass filtering approach on denoising the image corrupted by the Gaussian noise but via a new perspective.