Impulse noise removal by median-type noise detectors and edge-preserving regularization.

Ho Chung Wa.Thesis (M.Phil.)--Chinese University of Hong Kong, 2004.Includes bibliographical references.Abstracts in English and Chinese.Introduction --- p.6Paper I --- p.13Paper II --- p.34Concluding Remark --- p.5

Machine Learning And Image Processing For Noise Removal And Robust Edge Detection In The Presence Of Mixed Noise

The central goal of this dissertation is to design and model a smoothing filter based on the random single and mixed noise distribution that would attenuate the effect of noise while preserving edge details. Only then could robust, integrated and resilient edge detection methods be deployed to overcome the ubiquitous presence of random noise in images. Random noise effects are modeled as those that could emanate from impulse noise, Gaussian noise and speckle noise. In the first step, evaluation of methods is performed based on an exhaustive review on the different types of denoising methods which focus on impulse noise, Gaussian noise and their related denoising filters. These include spatial filters (linear, non-linear and a combination of them), transform domain filters, neural network-based filters, numerical-based filters, fuzzy based filters, morphological filters, statistical filters, and supervised learning-based filters. In the second step, switching adaptive median and fixed weighted mean filter (SAMFWMF) which is a combination of linear and non-linear filters, is introduced in order to detect and remove impulse noise. Then, a robust edge detection method is applied which relies on an integrated process including non-maximum suppression, maximum sequence, thresholding and morphological operations. The results are obtained on MRI and natural images. In the third step, a combination of transform domain-based filter which is a combination of dual tree – complex wavelet transform (DT-CWT) and total variation, is introduced in order to detect and remove Gaussian noise as well as mixed Gaussian and Speckle noise. Then, a robust edge detection is applied in order to track the true edges. The results are obtained on medical ultrasound and natural images. In the fourth step, a smoothing filter, which is a feed-forward convolutional network (CNN) is introduced to assume a deep architecture, and supported through a specific learning algorithm, l2 loss function minimization, a regularization method, and batch normalization all integrated in order to detect and remove impulse noise as well as mixed impulse and Gaussian noise. Then, a robust edge detection is applied in order to track the true edges. The results are obtained on natural images for both specific and non-specific noise-level

Removal of Fixed-valued Impulse Noise based on Probability of Existence of the Image Pixel

This paper proposes a new approach for restoring images distorted by fixed-valued impulse noise. The detection process is based on finding the probability of existence of the image pixel. Extensive investigations indicate that the probability of existence of a pixel in an original image is bounded and has a maximum limit. The tested pixel is judged as original if it has probability of existence less than the threshold boundary. In many tested images, the proposed method indicates that the noisy pixels are detected efficiently. Moreover, this method is very fast, easy to implement and has an outstanding performance when compared with other well-known methods. Therefore, if the proposed filter is added as a preliminary stage to many filters, the final results will be improved

A Survey of Non-Linear Filtering Techniques For Image Noise Removal

Image is captured or noninheritable by any image capturing device like camera or scanner and then it is stored in the mass storage of the computer system. In many of these applications the existence of impulsive noise among the noninheritable pictures is one altogether common problems. This noise is characterized by spots on the image and is usually related to the innate image because of errors in image sensors and information transmission. Now-a-days there are numerous strategies that are offered to remove noise from digital images. Most of the novel methodology includes 2 stages: the primary stage is to find the noise within the image and the second stage is to eliminate the noise from the image. This paper explores the varied novel methods for the removal of noise from the digital images. The distinctive feature of the all the described filters is that offers well line, edge and detail preservation performance while, at the constant time, effectively removing noise from the input image. In later section, we present a short introduction for various strategies for noise reduction in digital images