An Overview of Multi-Processor Approximate Message Passing

Approximate message passing (AMP) is an algorithmic framework for solving linear inverse problems from noisy measurements, with exciting applications such as reconstructing images, audio, hyper spectral images, and various other signals, including those acquired in compressive signal acquisiton systems. The growing prevalence of big data systems has increased interest in large-scale problems, which may involve huge measurement matrices that are unsuitable for conventional computing systems. To address the challenge of large-scale processing, multiprocessor (MP) versions of AMP have been developed. We provide an overview of two such MP-AMP variants. In row-MP-AMP, each computing node stores a subset of the rows of the matrix and processes corresponding measurements. In column- MP-AMP, each node stores a subset of columns, and is solely responsible for reconstructing a portion of the signal. We will discuss pros and cons of both approaches, summarize recent research results for each, and explain when each one may be a viable approach. Aspects that are highlighted include some recent results on state evolution for both MP-AMP algorithms, and the use of data compression to reduce communication in the MP network

Quantitative MRI and machine learning for the diagnosis and prognosis of Multiple Sclerosis

Multiple sclerosis (MS) is an immune-mediated, inflammatory, neurological disease affecting myelin in the central nervous system, whose driving mechanisms are not yet fully understood. Conventional magnetic resonance imaging (MRI) is largely used in the MS diagnostic process, but because of its lack of specificity, it cannot reliably detect microscopic damage. Quantitative MRI provides instead feature maps that can be exploited to improve prognosis and treatment monitoring, at the cost of prolonged acquisition times and specialised MR-protocols. In this study, two converging approaches were followed to investigate how to best use the available MRI data for the diagnosis and prognosis of MS. On one hand, qualitative data commonly used in clinical research for lesion and anatomical purposes were shown to carry quantitative information that could be used to conduct myelin and relaxometry analyses on cohorts devoid of dedicated quantitative acquisitions. In this study arm, named bottom-up, qualitative information was up-converted to quantitative surrogate: traditional model-fitting and deep-learning frameworks were proposed and tested on MS patients to extract relaxometry and indirect-myelin quantitative data from qualitative scans. On the other hand, when using multi-modal MRI data to classify MS patients with different clinical status, different MR-features contribute to specific classification tasks. The top-down study arm consisted in using machine learning to reduce the multi-modal dataset dimensionality only to those MR-features that are more likely to be biophysically meaningful with respect to each MS phenotype pathophysiology. Results show that there is much more potential to qualitative data than lesion and tissue segmentation, and that specific MRI modalities might be better suited for investigating individual MS phenotypes. Efficient multi-modal acquisitions informed by biophysical findings, whilst being able to extract quantitative information from qualitative data, would provide huge statistical power through the use of large, historical datasets, as well as constitute a significant step forward in the direction of sustainable research

Research and developments of distributed video coding

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The recent developed Distributed Video Coding (DVC) is typically suitable for the applications such as wireless/wired video sensor network, mobile camera etc. where the traditional video coding standard is not feasible due to the constrained computation at the encoder. With DVC, the computational burden is moved from encoder to decoder. The compression efficiency is achieved via joint decoding at the decoder. The practical application of DVC is referred to Wyner-Ziv video coding (WZ) where the side information is available at the decoder to perform joint decoding. This join decoding inevitably causes a very complex decoder. In current WZ video coding issues, many of them emphasise how to improve the system coding performance but neglect the huge complexity caused at the decoder. The complexity of the decoder has direct influence to the system output. The beginning period of this research targets to optimise the decoder in pixel domain WZ video coding (PDWZ), while still achieves similar compression performance. More specifically, four issues are raised to optimise the input block size, the side information generation, the side information refinement process and the feedback channel respectively. The transform domain WZ video coding (TDWZ) has distinct superior performance to the normal PDWZ due to the exploitation in spatial direction during the encoding. However, since there is no motion estimation at the encoder in WZ video coding, the temporal correlation is not exploited at all at the encoder in all current WZ video coding issues. In the middle period of this research, the 3D DCT is adopted in the TDWZ to remove redundancy in both spatial and temporal direction thus to provide even higher coding performance. In the next step of this research, the performance of transform domain Distributed Multiview Video Coding (DMVC) is also investigated. Particularly, three types transform domain DMVC frameworks which are transform domain DMVC using TDWZ based 2D DCT, transform domain DMVC using TDWZ based on 3D DCT and transform domain residual DMVC using TDWZ based on 3D DCT are investigated respectively. One of the important applications of WZ coding principle is error-resilience. There have been several attempts to apply WZ error-resilient coding for current video coding standard e.g. H.264/AVC or MEPG 2. The final stage of this research is the design of WZ error-resilient scheme for wavelet based video codec. To balance the trade-off between error resilience ability and bandwidth consumption, the proposed scheme emphasises the protection of the Region of Interest (ROI) area. The efficiency of bandwidth utilisation is achieved by mutual efforts of WZ coding and sacrificing the quality of unimportant area. In summary, this research work contributed to achieves several advances in WZ video coding. First of all, it is targeting to build an efficient PDWZ with optimised decoder. Secondly, it aims to build an advanced TDWZ based on 3D DCT, which then is applied into multiview video coding to realise advanced transform domain DMVC. Finally, it aims to design an efficient error-resilient scheme for wavelet video codec, with which the trade-off between bandwidth consumption and error-resilience can be better balanced

Selected topics on distributed video coding

Distributed Video Coding (DVC) is a new paradigm for video compression based on the information theoretical results of Slepian and Wolf (SW), and Wyner and Ziv (WZ). While conventional coding has a rigid complexity allocation as most of the complex tasks are performed at the encoder side, DVC enables a flexible complexity allocation between the encoder and the decoder. The most novel and interesting case is low complexity encoding and complex decoding, which is the opposite of conventional coding. While the latter is suitable for applications where the cost of the decoder is more critical than the encoder's one, DVC opens the door for a new range of applications where low complexity encoding is required and the decoder's complexity is not critical. This is interesting with the deployment of small and battery-powered multimedia mobile devices all around in our daily life. Further, since DVC operates as a reversed-complexity scheme when compared to conventional coding, DVC also enables the interesting scenario of low complexity encoding and decoding between two ends by transcoding between DVC and conventional coding. More specifically, low complexity encoding is possible by DVC at one end. Then, the resulting stream is decoded and conventionally re-encoded to enable low complexity decoding at the other end. Multiview video is attractive for a wide range of applications such as free viewpoint television, which is a system that allows viewing the scene from a viewpoint chosen by the viewer. Moreover, multiview can be beneficial for monitoring purposes in video surveillance. The increased use of multiview video systems is mainly due to the improvements in video technology and the reduced cost of cameras. While a multiview conventional codec will try to exploit the correlation among the different cameras at the encoder side, DVC allows for separate encoding of correlated video sources. Therefore, DVC requires no communication between the cameras in a multiview scenario. This is an advantage since communication is time consuming (i.e. more delay) and requires complex networking. Another appealing feature of DVC is the fact that it is based on a statistical framework. Moreover, DVC behaves as a natural joint source-channel coding solution. This results in an improved error resilience performance when compared to conventional coding. Further, DVC-based scalable codecs do not require a deterministic knowledge of the lower layers. In other words, the enhancement layers are completely independent from the base layer codec. This is called the codec-independent scalability feature, which offers a high flexibility in the way the various layers are distributed in a network. This thesis addresses the following topics: First, the theoretical foundations of DVC as well as the practical DVC scheme used in this research are presented. The potential applications for DVC are also outlined. DVC-based schemes use conventional coding to compress parts of the data, while the rest is compressed in a distributed fashion. Thus, different conventional codecs are studied in this research as they are compared in terms of compression efficiency for a rich set of sequences. This includes fine tuning the compression parameters such that the best performance is achieved for each codec. Further, DVC tools for improved Side Information (SI) and Error Concealment (EC) are introduced for monoview DVC using a partially decoded frame. The improved SI results in a significant gain in reconstruction quality for video with high activity and motion. This is done by re-estimating the erroneous motion vectors using the partially decoded frame to improve the SI quality. The latter is then used to enhance the reconstruction of the finally decoded frame. Further, the introduced spatio-temporal EC improves the quality of decoded video in the case of erroneously received packets, outperforming both spatial and temporal EC. Moreover, it also outperforms error-concealed conventional coding in different modes. Then, multiview DVC is studied in terms of SI generation, which differentiates it from the monoview case. More specifically, different multiview prediction techniques for SI generation are described and compared in terms of prediction quality, complexity and compression efficiency. Further, a technique for iterative multiview SI is introduced, where the final SI is used in an enhanced reconstruction process. The iterative SI outperforms the other SI generation techniques, especially for high motion video content. Finally, fusion techniques of temporal and inter-view side informations are introduced as well, which improves the performance of multiview DVC over monoview coding. DVC is also used to enable scalability for image and video coding. Since DVC is based on a statistical framework, the base and enhancement layers are completely independent, which is an interesting property called codec-independent scalability. Moreover, the introduced DVC scalable schemes show a good robustness to errors as the quality of decoded video steadily decreases with error rate increase. On the other hand, conventional coding exhibits a cliff effect as the performance drops dramatically after a certain error rate value. Further, the issue of privacy protection is addressed for DVC by transform domain scrambling, which is used to alter regions of interest in video such that the scene is still understood and privacy is preserved as well. The proposed scrambling techniques are shown to provide a good level of security without impairing the performance of the DVC scheme when compared to the one without scrambling. This is particularly attractive for video surveillance scenarios, which is one of the most promising applications for DVC. Finally, a practical DVC demonstrator built during this research is described, where the main requirements as well as the observed limitations are presented. Furthermore, it is defined in a setup being as close as possible to a complete real application scenario. This shows that it is actually possible to implement a complete end-to-end practical DVC system relying only on realistic assumptions. Even though DVC is inferior in terms of compression efficiency to the state of the art conventional coding for the moment, strengths of DVC reside in its good error resilience properties and the codec-independent scalability feature. Therefore, DVC offers promising possibilities for video compression with transmission over error-prone environments requirement as it significantly outperforms conventional coding in this case

SynthSR: A public AI tool to turn heterogeneous clinical brain scans into high-resolution T1-weighted images for 3D morphometry

Every year, millions of brain magnetic resonance imaging (MRI) scans are acquired in hospitals across the world. These have the potential to revolutionize our understanding of many neurological diseases, but their morphometric analysis has not yet been possible due to their anisotropic resolution. We present an artificial intelligence technique, "SynthSR," that takes clinical brain MRI scans with any MR contrast (T1, T2, etc.), orientation (axial/coronal/sagittal), and resolution and turns them into high-resolution T1 scans that are usable by virtually all existing human neuroimaging tools. We present results on segmentation, registration, and atlasing of >10,000 scans of controls and patients with brain tumors, strokes, and Alzheimer's disease. SynthSR yields morphometric results that are very highly correlated with what one would have obtained with high-resolution T1 scans. SynthSR allows sample sizes that have the potential to overcome the power limitations of prospective research studies and shed new light on the healthy and diseased human brain