Denoising Images Under Multiplicative Noise

Abstract

Generally the speckle noise occurred in images of different modalities due to random variation of pixel values. To denoise these images, it is necessary to apply various filtering techniques. So far there are lots of filtering methods proposed in literature which includes the Wiener filtering and Wavelet based thresholding approach to denoise such type of noisy images. This thesis analyse exiting Wiener filtering for image restoration with variable window size. However this restoration may not exhibit satisfactory performances with respect to standard indices like Structural Similarity Index Measure (SSIM), Signal-to-Noise Ratio (SNR), Peak Signal to Noise Ratio (PSNR), and Mean Square Error (MSE). Literature indicates that Curvelet transform represents natural image better than any other transformations. Therefore, curvelet coefficient can be used to segment true image and noise. The aim of the thesis to characterize the multiplicative noise in Curvelet transform domain. Subsequently a threshold based denoising algorithm has been developed using hard and MCET thresholding techniques. Finally, the denoised image was compared with original image using some quantifying statistical indices such as SSIM, MSE, SNR and PSNR for different noise variance which The experimental results demonstrate its efficacy over Wiener filtering method.