Image Denoising using Hybrid Method

"Image Denoising" is a topic under image processing. Historically the field of image processing grew from electrical engineering as an extension of the signal processing branch. Image Denoising is an important pre-processing task before further processing of image like segmentation, feature extraction, texture analysis etc. The purpose of denoising is to remove the noise while retaining the edges and other detailed features as much as possible. This noise gets introduced during acquisition, transmission & reception and storage & retrieval processes. As a result, there is degradation in visual quality of an image. The prime focus of this thesis is related to the denoising of the image to maximize the peak signal to noise ratio (PSNR), minimize the mean square error (MSE) and bit error rate (BER) using wavelet transform. Denoising of image using wavelet technique is very effective because of its multiresolution and sparsity characteristics. This book is useful for those students who pursue their study and thesis in digital image processing specially in image enhancement.

Autorentext

Rimpy Garg, completed her M-Tech degree in CSE from Punjab Technical University, Jalandhar, Punjab India in 2013. During the period of her thesis in M-Tech she opted for the field of Image Denoising. She has also published 2 research papers on this topic. Since then she is working as a software engineer in one of the leading company of India.

Klappentext

"Image Denoising" is a topic under image processing. Historically the field of image processing grew from electrical engineering as an extension of the signal processing branch. Image Denoising is an important pre-processing task before further processing of image like segmentation, feature extraction, texture analysis etc. The purpose of denoising is to remove the noise while retaining the edges and other detailed features as much as possible. This noise gets introduced during acquisition, transmission & reception and storage & retrieval processes. As a result, there is degradation in visual quality of an image. The prime focus of this thesis is related to the denoising of the image to maximize the peak signal to noise ratio (PSNR), minimize the mean square error (MSE) and bit error rate (BER) using wavelet transform. Denoising of image using wavelet technique is very effective because of its multiresolution and sparsity characteristics. This book is useful for those students who pursue their study and thesis in digital image processing specially in image enhancement.