“Performance Analysis of Spatial and Transform Filter For Efficient Image Noise Reduction”

Abstract

During acquisition of an image, from its source, noise becomes integral part of it, which is very difficult to remove. Various algorithms have been used in past to denoise images. Image denoising still has scope for improvement. Visual information transmitted in the form of digital images is becoming a major method of communication in the modern age, but the image obtained after transmission is often corrupted with noise.. This thesis reviews the existing denoising algorithms, such as filtering approach, wavelet based approach, and multiracial approach, and performs their comparative study. Different noise models including additive and multiplicative types are used. They include Gaussian noise, salt and pepper noise, speckle noise and Brownian noise. Selection of the denoising algorithm is application dependent. Hence, it is necessary to have knowledge about the noise present in the image so as to select the appropriate denoising algorithm.. The wavelet based approach finds applications in denoising images corrupted with Gaussian noise. In the case where the noise characteristics are complex, the multiracial approach can be used.. Based on using samples of degraded pixel neighborhoods as inputs, the output of efficient filtering approach provides a good image denoising performance which exhibited promising qualitative and quantitative results of the degraded noisy images in terms of PSNR, MSE and visual tests.