Comparing study using DWT / CT transforms in image denoising process
Abstract
Images may contain different types of noises; removing noise from image is often the first step in image processing, and remains a challenging problem in spite of the sophistication of recent research. This paper presents an efficient image denoising scheme based on two types of multi-resolution transforms, namely, the Discrete Wavelet Transform (DWT) and the Curvelet Transform(CT). each subbands components of an uses transform are denoising using two steps: 1) passed through principal component analysis (PCA) denoising procedure. 2) The image data obtain from PCA procedure can be denoising either by hard or soft thresholding techniques. The effectiveness of the methods was compared using parameters like MSE and PSNR. We find that using CT more efficient then using DWT and the qulity of denoising increase when we using PCA denoising procedure. Use the matlab version of 8th in the different treatment stages.
Keywords
image denoising, wavelet transform, curvelet transform, PCA, soft thresholding, hard thresholding.Metrics