Efficient noise reduction in images using directional modified sigma filter

Abstract

A noise reduction of images using a directional modified sigma filter is proposed. It is important that an image should include accurate values without noise for large-scale data processing of a cloud computing environment. A conventional sigma filter has been shown to be a good solution both in terms of filtering accuracy and computational complexity. However, the sigma filter does not preserve small edges well especially for the high level of additive noise. In this paper, we propose a new method using a modified sigma filter. In our proposed method, an input image is first decomposed into two components that have features of horizontal, vertical, and diagonal direction. Then two components are applied: high-pass filtering (HPF) and low-pass filtering (LPF). By applying the conventional sigma filter separately on each of them, an output image is reconstructed from the filtered components. Added noise is removed and our proposed method preserves the edges in the image. Comparative results from experiments show that the proposed algorithm achieves higher gains than the sigma filter and modified sigma filter, which are 2.6 dB PSNR on average and 0.5 dB PSNR, respectively. When relatively high levels of noise are added, the proposed algorithm shows better performance than the two conventional filters. The proposed method can be efficiently applied in digital cameras, digital TV, and smart phones.