Denoising Digital Images using Order Statistic Filtering Technique
G. Sivarajde Pushpavalli · 2013
A new filtering technique is proposed to denoising process on digital images. This filter is a combination of statistics and average. It is very useful for denoising if image is corrupted with impulse noise and gaussian noise. This filtering scheme offers edge and fine detail preservation performance while, at the same time, effectively denoising digital images. Extensive simulation results were realized for the proposed filter and different filters are compared. Results show that the proposed filter is superior performance in terms of image denoising and edges and fine details preservation properties. igital images are often contaminated by impulse noise and gaussian noise during image acquisition and/or transmission over communication channel. Majority of the existing filtering methods comprise order statistic filters utilizing the rank order information of an appropriate set of noisy input pixels. These filters are usually developed in the general framework of rank selection filters, which are nonlinear operators, constrained to output an order statistic from a set of input samples. The difference between these filters is in the information used to decide which order statistic to output. The standard median filter (MF) (1)-(3) is a simple rank selection filter and attempts to remove impulse noise from the center pixel of the analysis window by changing the luminance value of the center pixel with the median of the luminance values of the pixels contained within the window. This approach pro- vides a reasonable noise removal performance with the cost of introducing undesirable blurring effects into image details even at low noise densities (4-27). In order to address this issue, a new Decision Based Algo- rithm (DBA) is presented. This filter is a combination of statis- tics and average. It is very useful for denoising if image is cor- rupted with impulse noise and gaussian noise. Pixel inside the window is separated as impulse noise and remaining pixels. The remaining pixels (without impulse noise) inside the filter- ing window are arranged in ascending order and average val- ue is calculated for filtering. The rest of the paper is organized as follows. Section II ex- plains the structure of the proposed operator and its building blocks. Section III discusses the application of the proposed operator to the test images. Results of the experiments con- ducted to evaluate the performance of the proposed operator and comparative discussion of these results are also presented in this Section IV, which is the final section, presents the con- clusions.