Restoration of Noisy Microarray Images using Filtering Techniques

Abstract

Gene expression in large scale analysis is performed by microarray imaging and its accuracy is based on the experiments performed and processing the image further. It is known well that the noise produced during the gene expression analysis will affect the accuracy significantly. The quality of microarray image is affected by several errors particularly noise. Various noise types are present in an image that generates different influence on image processing as well as it is not essential to eliminate every noise, this noise elimination of noise effects establishes difficult issue in the analysis of microarray images. Conventionally several mathematical approaches are utilized for the noise estimation when processing the microarray images. The restoration model was developed in this paper. Noise image is provided as an input and the noise type is estimated by the probability density function (PDF) utilizing appropriate filter for image denoising and restored microarray images are produced. Image sharpening is performed by Blind deconvolution and the image with noise mixture are restored by bilateral filter. Therefore, good restored images are produced from the simulation results with increased Peak Signal to Noise Ratio (PSNR) values and decreased Mean Squared Error (MSE) values.