Adaptive Wavelet Packet Transform

Two-dimensional over-complete wavelet packet transform can better represent the texture and long oscillatory patterns in natural images

Wavelets and partial differential equations for image denoising

In this paper a wavelet based model for image de-noising is presented. Wavelet coefficients are modelled as waves that grow while dilating along scales. The model establishes a precise link between corresponding modulus maxima in the wavelet domain and then allows to predict wavelet coefficients at each scale from the first one. This property combined with the theoretical results about the characterization of singularities in the wavelet domain enables to discard noise. Significant structures of the image are well recovered while some annoying artifacts along image edges are reduced. Some experimental results show that the proposed approach outperforms the most recent and effective wavelet based denoising schemes

Adaptive wavelet thresholding for image denoising using sure minimization and clustering of wavelet coefficients

Images and video are often coded using block-based discrete cosine transform (DCT) or discrete wavelet transform (DWT) which cause a great deal of visual distortions. In this paper, an extension of the intra-scale dependencies of wavelet coefficients is proposed to improve denoising performance. This method incorporates information on neighbouring wavelet coefficients that are inside of manually created clusters. Extensive experimental results are given to demonstrate the strength of the proposed method

Development of Some Novel Spatial-Domain and Transform-Domain Digital Image Filters

Some spatial-domain and transform-domain digital image filtering algorithms have been developed in this thesis to suppress additive white Gaussian noise (AWGN). In many occasions, noise in digital images is found to be additive in nature with uniform power in the whole bandwidth and with Gaussian probability distribution. Such a noise is referred to as Additive White Gaussian Noise (AWGN). It is difficult to suppress AWGN since it corrupts almost all pixels in an image. The arithmetic mean filter, commonly known as Mean filter, can be employed to suppress AWGN but it introduces a blurring effect. Image denoising is usually required to be performed before display or further processing like segmentation, feature extraction, object recognition, texture analysis, etc. The purpose of denoising is to suppress the noise quite efficiently while retaining the edges and other detailed features as much as possible

Wavelet Based Filtering of Electrocardiograms

Tato dizertační práce pojednává o možnostech využití vlnkových transformací pro odstranění širokopásmového svalového rušení v signálech EKG. V práci jsou nejprve rozebrány vlastnosti signálů EKG a především nejčastěji vyskytující se typy rušení. Dále je představena teorie vlnkových transformací a ukázány návrhy jednoduchého vlnkového filtru i sofistikovanější varianty využívající wienerovské filtrace vlnkových koeficientů. Další část práce je věnována návrhu vlastního filtru, který vychází právě z wienerovské vlnkové filtrace a je doplněn algoritmy zajišťujícími plnou adaptibilitu jeho parametrů při změně vlastností vstupního signálu. Vhodné parametry navrženého systému jsou hledány automatickým způsobem a algoritmus je testován na kompletní standardní databázi elektrokardiogramů CSE, kde dosahuje výrazně lepších výsledků než další srovnávané publikované metody.This dissertation deals with possibilities of using wavelet transforms for elimination of broadband muscle noise in ECG signals. In this work, the characteristics of ECG signals and particularly the most frequently occurring type of interference are discussed firstly. The theory of wavelet transforms is also introduced and followed by design of the simple wavelet filter and the more sophisticated version with wiener filtering of wavelet coefficients. Next part is devoted to the design of our filter, which is based on wavelet wiener filtering and is complemented by algorithms that ensure full adaptability of its parameters when the properties of the input signal are changing. Suitable parameters of the proposed system are searched automatically and the algorithm is tested on the complete standard electrocardiograms database CSE, where it achieves significantly better results than other published methods.

Android Based Chatbot and Mobile Application for Tour and Travel Company

Mobile Applications are rapidly growing segment of global mobile market. This paper involves an application for the android base operating system for a travel agent which will conduct booking transactions for train tickets, airline tickets, hotel, theme park, and tour. This application is integrated with a chatbot, an instant messaging applications. Chatbot is a computer program that can communicate with users. The purpose of chatbot is to support and scale business teams in their relations with customers. This chatbot can be placed in Facebook Messenger, telegram and own website, so it gives the potential to reach a bigger audience. Customers do not need to install new applications. The customer just simply adds chatbot as a friend and starts transacting with a conversation, without needing to understand the intended user interface in an application. By using chatbot, the company can also shorten sales transactions. Company can also broadcast information directly to customers easily. In this paper, we first discuss about chatbot and mobile application system design. Finally we will discuss about the system implementation

Thickness estimation, automated classification and novelty detection in ultrasound images of the plantar fascia tissues

The plantar fascia (PF) tissue plays an important role in the movement and the stability of the foot during walking and running. Thus it is possible for the overuse and the associated medical problems to cause injuries and some severe common diseases. Ultrasound (US) imaging offers significant potential in diagnosis of PF injuries and monitoring treatments. Despite the advantages of US, the generated PF images are difficult to interpret during medical assessment. This is partly due to the size and position of the PF in relation to the adjacent tissues. This limits the use of US in clinical practice and therefore impacts on patient services for what is a common problem and a major cause of foot pain and discomfort. It is therefore a requirement to devise an automated system that allows better and easier interpretation of PF US images during diagnosis. This study is concerned with developing a computer-based system using a combination of medical image processing techniques whereby different PF US images can be visually improved, segmented, analysed and classified as normal or abnormal, so as to provide more information to the doctors and the clinical treatment department for early diagnosis and the detection of the PF associated medical problems. More specifically, this study is required to investigate the possibility of a proposed model for localizing and estimating the PF thickness a cross three different sections (rearfoot, midfoot and forefoot) using a supervised ANN segmentation technique. The segmentation method uses RBF artificial neural network module in order to classify small overlapping patches into PF and non-PF tissue. Feature selection technique was performed as a post-processing step for feature extraction to reduce the number of the extracted features. Then the trained RBF-ANN is used to segment the desired PF region. The PF thickness was calculated using two different methods: distance transformation and a proposed area-length calculation algorithm. Additionally, different machine learning approaches were investigated and applied to the segmented PF region in order to distinguish between symptomatic and asymptomatic PF subjects using the best normalized and selected feature set. This aims to facilitate the characterization and the classification of the PF area for the identification of patients with inferior heel pain at risk of plantar fasciitis. Finally, a novelty detection framework for detecting the symptomatic PF samples (with plantar fasciitis disorder) using only asymptomatic samples is proposed. This model implies the following: feature analysis, building a normality model by training the one-class SVDD classifier using only asymptomatic PF training datasets, and computing novelty scores using the trained SVDD classifier, training and testing asymptomatic datasets, and testing symptomatic datasets of the PF dataset. The performance evaluation results showed that the proposed approaches used in this study obtained favourable results compared to other methods reported in the literature

Adaptive nonlocal and structured sparse signal modeling and applications

Features based on sparse representation, especially using the synthesis dictionary model, have been heavily exploited in signal processing and computer vision. Many applications such as image and video denoising, inpainting, demosaicing, super-resolution, magnetic resonance imaging (MRI), and computed tomography (CT) reconstruction have been shown to benefit from adaptive sparse signal modeling. However, synthesis dictionary learning typically involves expensive sparse coding and learning steps. Recently, sparsifying transform learning received interest for its cheap computation and its optimal updates in the alternating algorithms. Prior works on transform learning have certain limitations, including (1) limited model richness and structure for handling diverse data, (2) lack of non-local structure, and (3) lack of effective extension to high-dimensional or streaming data. This dissertation focuses on advanced data-driven sparse modeling techniques, especially with nonlocal and structured sparse signal modeling. In the first work of this dissertation, we propose a methodology for learning, dubbed Flipping and Rotation Invariant Sparsifying Transforms (FRIST), to better represent natural images that contain textures with various geometrical directions. The proposed alternating FRIST learning algorithm involves efficient optimal updates. We provide a convergence guarantee, and demonstrate the empirical convergence behavior of the proposed FRIST learning approach. Preliminary experiments show the promising performance of FRIST learning for image sparse representation, segmentation, denoising, robust inpainting, and compressed sensing-based magnetic resonance image reconstruction. Next, we present an online high-dimensional sparsifying transform learning method for spatio-temporal data, and demonstrate its usefulness with a novel video denoising framework, dubbed VIDOSAT. The proposed method is based on our previous work on online sparsifying transform learning, which has low computational and memory costs, and can potentially handle streaming video. By combining with a block matching (BM) technique, the learned model can effectively adapt to video data with various motions. The patches are constructed either from corresponding 2D patches in successive frames or using an online block matching technique. The proposed online video denoising requires little memory and others efficient processing. Numerical experiments are used to analyze the contribution of the various components of the proposed video denoising scheme by "switching off" these components - for example, fixing the transform to be 3D DCT, rather than a learned transform. Other experiments compare to the performance of prior schemes such as dictionary learning-based schemes, and the state-of-the-art VBM3D and VBM4D on several video data sets, demonstrating the promising performance of the proposed methods. In the third part of the dissertation, we propose a joint sparse and low-rank model, dubbed STROLLR, to better represent natural images. Patch-based methods exploit local patch sparsity, whereas other works apply low-rankness of grouped patches to exploit image non-local structures. However, using either approach alone usually limits performance in image restoration applications. In order to fully utilize both the local and non-local image properties, we develop an image restoration framework using a transform learning scheme with joint low-rank regularization. The approach owes some of its computational efficiency and good performance to the use of transform learning for adaptive sparse representation rather than the popular synthesis dictionary learning algorithms, which involve approximation of NP-hard sparse coding and expensive learning steps. We demonstrate the proposed framework in various applications to image denoising, inpainting, and compressed sensing based magnetic resonance imaging. Results show promising performance compared to state-of-the-art competing methods. Last, we extend the effective joint sparsity and low-rankness model from image to video applications. We propose a novel video denoising method, based on an online tensor reconstruction scheme with a joint adaptive sparse and low-rank model, dubbed SALT. An efficient and unsupervised online unitary sparsifying transform learning method is introduced to impose adaptive sparsity on the fly. We develop an efficient 3D spatio-temporal data reconstruction framework based on the proposed online learning method, which exhibits low latency and can potentially handle streaming videos. To the best of our knowledge, this is the first work that combines adaptive sparsity and low-rankness for video denoising, and the first work that solves the proposed problem in an online fashion. We demonstrate video denoising results over commonly used videos from public datasets. Numerical experiments show that the proposed video denoising method outperforms competing methods