Solving Inverse Problems with Piecewise Linear Estimators: From Gaussian Mixture Models to Structured Sparsity

A general framework for solving image inverse problems is introduced in this paper. The approach is based on Gaussian mixture models, estimated via a computationally efficient MAP-EM algorithm. A dual mathematical interpretation of the proposed framework with structured sparse estimation is described, which shows that the resulting piecewise linear estimate stabilizes the estimation when compared to traditional sparse inverse problem techniques. This interpretation also suggests an effective dictionary motivated initialization for the MAP-EM algorithm. We demonstrate that in a number of image inverse problems, including inpainting, zooming, and deblurring, the same algorithm produces either equal, often significantly better, or very small margin worse results than the best published ones, at a lower computational cost.Comment: 30 page

Study on high Performance and Effective Watermarking Scheme using Hybrid Transform (DCT-DWT)

Nowadays healthcare infrastructure depends on Hospital Information Systems (HIS), Radiology Information Systems (RIS),Picture archiving and Communication Systems (PACS) as these provide new ways to store, access and distribute medical data . It eliminates the security risk. Conversely, these developments have introduced new risks for unsuitable deployment of medical information flowing in open networks, provided the effortlessness with which digital content can be manipulated. It is renowned that the integrity and confidentiality of medical data is a serious topic for ethical and legal reasons. Medical images need to be kept intact in any condition and prior to any operation as well need to be checked for integrity and verification. Watermarking is a budding technology that is capable of assisting this aim. In recent times, frequency domain watermarking algorithms have gained immense importance due to their widespread use. Subsequently, the watermark embedding and extraction are performed in frequency domain using the presented scheme. The proposed watermarking scheme, the watermark extraction compared with the original image for calculating SSIM.The effectiveness of the proposed watermarking scheme is demonstrated with the aid of experimental results

Non-linear echo cancellation - a Bayesian approach

Echo cancellation literature is reviewed, then a Bayesian model is introduced and it is shown how how it can be used to model and fit nonlinear channels. An algorithm for cancellation of echo over a nonlinear channel is developed and tested. It is shown that this nonlinear algorithm converges for both linear and nonlinear channels and is superior to linear echo cancellation for canceling an echo through a nonlinear echo-path channel

Directional edge and texture representations for image processing

An efficient representation for natural images is of fundamental importance in image processing and analysis. The commonly used separable transforms such as wavelets axe not best suited for images due to their inability to exploit directional regularities such as edges and oriented textural patterns; while most of the recently proposed directional schemes cannot represent these two types of features in a unified transform. This thesis focuses on the development of directional representations for images which can capture both edges and textures in a multiresolution manner. The thesis first considers the problem of extracting linear features with the multiresolution Fourier transform (MFT). Based on a previous MFT-based linear feature model, the work extends the extraction method into the situation when the image is corrupted by noise. The problem is tackled by the combination of a "Signal+Noise" frequency model, a refinement stage and a robust classification scheme. As a result, the MFT is able to perform linear feature analysis on noisy images on which previous methods failed. A new set of transforms called the multiscale polar cosine transforms (MPCT) are also proposed in order to represent textures. The MPCT can be regarded as real-valued MFT with similar basis functions of oriented sinusoids. It is shown that the transform can represent textural patches more efficiently than the conventional Fourier basis. With a directional best cosine basis, the MPCT packet (MPCPT) is shown to be an efficient representation for edges and textures, despite its high computational burden. The problem of representing edges and textures in a fixed transform with less complexity is then considered. This is achieved by applying a Gaussian frequency filter, which matches the disperson of the magnitude spectrum, on the local MFT coefficients. This is particularly effective in denoising natural images, due to its ability to preserve both types of feature. Further improvements can be made by employing the information given by the linear feature extraction process in the filter's configuration. The denoising results compare favourably against other state-of-the-art directional representations

Rate scalable image compression in the wavelet domain

This thesis explores image compression in the wavelet transform domain. This the- sis considers progressive compression based on bit plane coding. The rst part of the thesis investigates the scalar quantisation technique for multidimensional images such as colour and multispectral image. Embedded coders such as SPIHT and SPECK are known to be very simple and e cient algorithms for compression in the wavelet do- main. However, these algorithms require the use of lists to keep track of partitioning processes, and such lists involve high memory requirement during the encoding process. A listless approach has been proposed for multispectral image compression in order to reduce the working memory required. The earlier listless coders are extended into three dimensional coder so that redundancy in the spectral domain can be exploited. Listless implementation requires a xed memory of 4 bits per pixel to represent the state of each transformed coe cient. The state is updated during coding based on test of sig- ni cance. Spectral redundancies are exploited to improve the performance of the coder by modifying its scanning rules and the initial marker/state. For colour images, this is done by conducting a joint the signi cant test for the chrominance planes. In this way, the similarities between the chrominance planes can be exploited during the cod- ing process. Fixed memory listless methods that exploit spectral redundancies enable e cient coding while maintaining rate scalability and progressive transmission. The second part of the thesis addresses image compression using directional filters in the wavelet domain. A directional lter is expected to improve the retention of edge and curve information during compression. Current implementations of hybrid wavelet and directional (HWD) lters improve the contour representation of compressed images, but su er from the pseudo-Gibbs phenomenon in the smooth regions of the images. A di erent approach to directional lters in the wavelet transforms is proposed to remove such artifacts while maintaining the ability to preserve contours and texture. Imple- mentation with grayscale images shows improvements in terms of distortion rates and the structural similarity, especially in images with contours. The proposed transform manages to preserve the directional capability without pseudo-Gibbs artifacts and at the same time reduces the complexity of wavelet transform with directional lter. Fur-ther investigation to colour images shows the transform able to preserve texture and curve.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Advancements and Breakthroughs in Ultrasound Imaging

Ultrasonic imaging is a powerful diagnostic tool available to medical practitioners, engineers and researchers today. Due to the relative safety, and the non-invasive nature, ultrasonic imaging has become one of the most rapidly advancing technologies. These rapid advances are directly related to the parallel advancements in electronics, computing, and transducer technology together with sophisticated signal processing techniques. This book focuses on state of the art developments in ultrasonic imaging applications and underlying technologies presented by leading practitioners and researchers from many parts of the world

Ultrasound image processing in the evaluation of labor induction failure risk

Labor induction is defined as the artificial stimulation of uterine contractions for the purpose of vaginal birth. Induction is prescribed for medical and elective reasons. Success in labor induction procedures is related to vaginal delivery. Cesarean section is one of the potential risks of labor induction as it occurs in about 20% of the inductions. A ripe cervix (soft and distensible) is needed for a successful labor. During the ripening cervical, tissues experience micro structural changes: collagen becomes disorganized and water content increases. These changes will affect the interaction between cervical tissues and sound waves during ultrasound transvaginal scanning and will be perceived as gray level intensity variations in the echographic image. Texture analysis can be used to analyze these variations and provide a means to evaluate cervical ripening in a non-invasive way

Facial Expression Recognition Based on Deep Learning Convolution Neural Network: A Review

Facial emotional processing is one of the most important activities in effective calculations, engagement with people and computers, machine vision, video game testing, and consumer research. Facial expressions are a form of nonverbal communication, as they reveal a person's inner feelings and emotions. Extensive attention to Facial Expression Recognition (FER) has recently been received as facial expressions are considered. As the fastest communication medium of any kind of information. Facial expression recognition gives a better understanding of a person's thoughts or views and analyzes them with the currently trending deep learning methods. Accuracy rate sharply compared to traditional state-of-the-art systems. This article provides a brief overview of the different FER fields of application and publicly accessible databases used in FER and studies the latest and current reviews in FER using Convolution Neural Network (CNN) algorithms. Finally, it is observed that everyone reached good results, especially in terms of accuracy, with different rates, and using different data sets, which impacts the results