The development of the quaternion wavelet transform

The purpose of this article is to review what has been written on what other authors have called quaternion wavelet transforms (QWTs): there is no consensus about what these should look like and what their properties should be. We briefly explain what real continuous and discrete wavelet transforms and multiresolution analysis are and why complex wavelet transforms were introduced; we then go on to detail published approaches to QWTs and to analyse them. We conclude with our own analysis of what it is that should define a QWT as being truly quaternionic and why all but a few of the “QWTs” we have described do not fit our definition

Noise Level Estimation for Digital Images Using Local Statistics and Its Applications to Noise Removal

In this paper, an automatic estimation of additive white Gaussian noise technique is proposed. This technique is built according to the local statistics of Gaussian noise. In the field of digital signal processing, estimation of the noise is considered as pivotal process that many signal processing tasks relies on. The main aim of this paper is to design a patch-based estimation technique in order to estimate the noise level in natural images and use it in blind image removal technique. The estimation processes is utilized selected patches which is most contaminated sub-pixels in the tested images sing principal component analysis (PCA). The performance of the suggested noise level estimation technique is shown its superior to state of the art noise estimation and noise removal algorithms, the proposed algorithm produces the best performance in most cases compared with the investigated techniques in terms of PSNR, IQI and the visual perception

Sensing and Signal Processing in Smart Healthcare

In the last decade, we have witnessed the rapid development of electronic technologies that are transforming our daily lives. Such technologies are often integrated with various sensors that facilitate the collection of human motion and physiological data and are equipped with wireless communication modules such as Bluetooth, radio frequency identification, and near-field communication. In smart healthcare applications, designing ergonomic and intuitive human–computer interfaces is crucial because a system that is not easy to use will create a huge obstacle to adoption and may significantly reduce the efficacy of the solution. Signal and data processing is another important consideration in smart healthcare applications because it must ensure high accuracy with a high level of confidence in order for the applications to be useful for clinicians in making diagnosis and treatment decisions. This Special Issue is a collection of 10 articles selected from a total of 26 contributions. These contributions span the areas of signal processing and smart healthcare systems mostly contributed by authors from Europe, including Italy, Spain, France, Portugal, Romania, Sweden, and Netherlands. Authors from China, Korea, Taiwan, Indonesia, and Ecuador are also included

Curvelet-Based Texture Classification in Computerized Critical Gleason Grading of Prostate Cancer Histological Images

Classical multi-resolution image processing using wavelets provides an efficient analysis of image characteristics represented in terms of pixel-based singularities such as connected edge pixels of objects and texture elements given by the pixel intensity statistics. Curvelet transform is a recently developed approach based on curved singularities that provides a more sparse representation for a variety of directional multi-resolution image processing tasks such as denoising and texture analysis. The objective of this research is to develop a multi-class classifier for the automated classification of Gleason patterns of prostate cancer histological images with the utilization of curvelet-based texture analysis. This problem of computer-aided recognition of four pattern classes between Gleason Score 6 (primary Gleason grade 3 plus secondary Gleason grade 3) and Gleason Score 8 (both primary and secondary grades 4) is of critical importance affecting treatment decision and patients’ quality of life. Multiple spatial sampling within each histological image is examined through the curvelet transform, the significant curvelet coefficient at each location of an image patch is obtained by maximization with respect to all curvelet orientations at a given location which represents the apparent curved-based singularity such as a short edge segment in the image structure. This sparser representation reduces greatly the redundancy in the original set of curvelet coefficients. The statistical textural features are extracted from these curvelet coefficients at multiple scales. We have designed a 2-level 4-class classification scheme, attempting to mimic the human expert’s decision process. It consists of two Gaussian kernel support vector machines, one support vector machine in each level and each is incorporated with a voting mechanism from classifications of multiple windowed patches in an image to reach the final decision for the image. At level 1, the support vector machine with voting is trained to ascertain the classification of Gleason grade 3 and grade 4, thus Gleason score 6 and score 8, by unanimous votes to one of the two classes, while the mixture voting inside the margin between decision boundaries will be assigned to the third class for consideration at level 2. The support vector machine in level 2 with supplemental features is trained to classify an image patch to Gleason grade 3+4 or 4+3 and the majority decision from multiple patches to consolidate the two-class discrimination of the image within Gleason score 7, or else, assign to an Indecision category. The developed tree classifier with voting from sampled image patches is distinct from the traditional voting by multiple machines. With a database of TMA prostate histological images from Urology/Pathology Laboratory of the Johns Hopkins Medical Center, the classifier using curvelet-based statistical texture features for recognition of 4-class critical Gleason scores was successfully trained and tested achieving a remarkable performance with 97.91% overall 4-class validation accuracy and 95.83% testing accuracy. This lends to an expectation of more testing and further improvement toward a plausible practical implementation

Two and three dimensional segmentation of multimodal imagery

The role of segmentation in the realms of image understanding/analysis, computer vision, pattern recognition, remote sensing and medical imaging in recent years has been significantly augmented due to accelerated scientific advances made in the acquisition of image data. This low-level analysis protocol is critical to numerous applications, with the primary goal of expediting and improving the effectiveness of subsequent high-level operations by providing a condensed and pertinent representation of image information. In this research, we propose a novel unsupervised segmentation framework for facilitating meaningful segregation of 2-D/3-D image data across multiple modalities (color, remote-sensing and biomedical imaging) into non-overlapping partitions using several spatial-spectral attributes. Initially, our framework exploits the information obtained from detecting edges inherent in the data. To this effect, by using a vector gradient detection technique, pixels without edges are grouped and individually labeled to partition some initial portion of the input image content. Pixels that contain higher gradient densities are included by the dynamic generation of segments as the algorithm progresses to generate an initial region map. Subsequently, texture modeling is performed and the obtained gradient, texture and intensity information along with the aforementioned initial partition map are used to perform a multivariate refinement procedure, to fuse groups with similar characteristics yielding the final output segmentation. Experimental results obtained in comparison to published/state-of the-art segmentation techniques for color as well as multi/hyperspectral imagery, demonstrate the advantages of the proposed method. Furthermore, for the purpose of achieving improved computational efficiency we propose an extension of the aforestated methodology in a multi-resolution framework, demonstrated on color images. Finally, this research also encompasses a 3-D extension of the aforementioned algorithm demonstrated on medical (Magnetic Resonance Imaging / Computed Tomography) volumes

Non-Standard Imaging Techniques

The first objective of the thesis is to investigate the problem of reconstructing a small-scale object (a few millimeters or smaller) in 3D. In Chapter 3, we show how this problem can be solved effectively by a new multifocus multiview 3D reconstruction procedure which includes a new Fixed-Lens multifocus image capture and a calibrated image registration technique using analytic homography transformation. The experimental results using the real and synthetic images demonstrate the effectiveness of the proposed solutions by showing that both the fixed-lens image capture and multifocus stacking with calibrated image alignment significantly reduce the errors in the camera poses and produce more complete 3D reconstructed models as compared with those by the conventional moving lens image capture and multifocus stacking. The second objective of the thesis is modelling the dual-pixel (DP) camera. In Chapter 4, to understand the potential of the DP sensor for computer vision applications, we study the formation of the DP pair which links the blur and the depth information. A mathematical DP model is proposed which can benefit depth estimation by the blur. These explorations motivate us to propose an end-to-end DDDNet (DP-based Depth and Deblur Network) to jointly estimate the depth and restore the image . Moreover, we define a reblur loss, which reflects the relationship of the DP image formation process with depth information, to regularize our depth estimate in training. To meet the requirement of a large amount of data for learning, we propose the first DP image simulator which allows us to create datasets with DP pairs from any existing RGBD dataset. As a side contribution, we collect a real dataset for further research. Extensive experimental evaluation on both synthetic and real datasets shows that our approach achieves competitive performance compared to state-of-the-art approaches. Another (third) objective of this thesis is to tackle the multifocus image fusion problem, particularly for long multifocus image sequences. Multifocus image stacking/fusion produces an in-focus image of a scene from a number of partially focused images of that scene in order to extend the depth of field. One of the limitations of the current state of the art multifocus fusion methods is not considering image registration/alignment before fusion. Consequently, fusing unregistered multifocus images produces an in-focus image containing misalignment artefacts. In Chapter 5, we propose image registration by projective transformation before fusion to remove the misalignment artefacts. We also propose a method based on 3D deconvolution to retrieve the in-focus image by formulating the multifocus image fusion problem as a 3D deconvolution problem. The proposed method achieves superior performance compared to the state of the art methods. It is also shown that, the proposed projective transformation for image registration can improve the quality of the fused images. Moreover, we implement a multifocus simulator to generate synthetic multifocus data from any RGB-D dataset. The fourth objective of this thesis is to explore new ways to detect the polarization state of light. To achieve the objective, in Chapter 6, we investigate a new optical filter namely optical rotation filter for detecting the polarization state with a fewer number of images. The proposed method can estimate polarization state using two images, one with the filter and another without. The accuracy of estimating the polarization parameters using the proposed method is almost similar to that of the existing state of the art method. In addition, the feasibility of detecting the polarization state using only one RGB image captured with the optical rotation filter is also demonstrated by estimating the image without the filter from the image with the filter using a generative adversarial network