A multiresolution framework for local similarity based image denoising

In this paper, we present a generic framework for denoising of images corrupted with additive white Gaussian noise based on the idea of regional similarity. The proposed framework employs a similarity function using the distance between pixels in a multidimensional feature space, whereby multiple feature maps describing various local regional characteristics can be utilized, giving higher weight to pixels having similar regional characteristics. An extension of the proposed framework into a multiresolution setting using wavelets and scale space is presented. It is shown that the resulting multiresolution multilateral (MRM) filtering algorithm not only eliminates the coarse-grain noise but can also faithfully reconstruct anisotropic features, particularly in the presence of high levels of noise

The Nonsubsampled Contourlet Transform Based Statistical Medical Image Fusion Using Generalized Gaussian Density

We propose a novel medical image fusion scheme based on the statistical dependencies between coefficients in the nonsubsampled contourlet transform (NSCT) domain, in which the probability density function of the NSCT coefficients is concisely fitted using generalized Gaussian density (GGD), as well as the similarity measurement of two subbands is accurately computed by Jensen-Shannon divergence of two GGDs. To preserve more useful information from source images, the new fusion rules are developed to combine the subbands with the varied frequencies. That is, the low frequency subbands are fused by utilizing two activity measures based on the regional standard deviation and Shannon entropy and the high frequency subbands are merged together via weight maps which are determined by the saliency values of pixels. The experimental results demonstrate that the proposed method significantly outperforms the conventional NSCT based medical image fusion approaches in both visual perception and evaluation indices

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

Fusion of Images and Videos using Multi-scale Transforms

This thesis deals with methods for fusion of images as well as videos using multi-scale transforms. First, a novel image fusion algorithm based primarily on an improved multi-scale coefficient decomposition framework is proposed. The proposed framework uses a combination of non-subsampled contourlet and wavelet transforms for the initial multi-scale decompositions. The decomposed multi-scale coefficients are then fused twice using various local activity measures. Experimental results show that the proposed approach performs better or on par with the existing state-of-the art image fusion algorithms in terms of quantitative and qualitative performance. In addition, the proposed image fusion algorithm can produce high quality fused images even with a computationally inexpensive two-scale decomposition. Finally, we extend the proposed framework to formulate a novel video fusion algorithm for camouflaged target detection from infrared and visible sensor inputs. The proposed framework consists of a novel target identification method based on conventional thresholding techniques proposed by Otsu and Kapur et al. These thresholding techniques are further extended to formulate novel region-based fusion rules using local statistical measures. The proposed video fusion algorithm, when used in target highlighting mode, can further enhance the hidden target, making it much easier to localize the hidden camouflaged target. Experimental results show that the proposed video fusion algorithm performs much better than its counterparts in terms of quantitative and qualitative results as well as in terms of time complexity. The relative low complexity of the proposed video fusion algorithm makes it an ideal candidate for real-time video surveillance applications

A parallel fusion method of remote sensing image based on NSCT

Remote sensing image fusion is very important for playing the advantages of a variety of remote sensing data. However, remote sensing image fusion is large in computing capacity and time consuming. In this paper, in order to fuse remote sensing images accurately and quickly, a parallel fusion algorithm of remote sensing image based on NSCT (nonsubsampled contourlet transform) is proposed. In the method, two important kinds of remote sensing image, multispectral image and panchromatic image are used, and the advantages of parallel computing in high performance computing and the advantages of NSCT in information processing are combined. In the method, based on parallel computing, some processes with large amount of calculation including IHS (Intensity, Hue, Saturation) transform, NSCT, inverse NSCT, inverse IHS transform, etc., are done. To realize the method, multispectral image is processed with IHS transform, and the three components, I, H, and S are gotten. The component I and the panchromatic image are decomposed with NSCT. The obtained low frequency components of NSCT are fused with the fusion rule based on the neighborhood energy feature matching, and the obtained high frequency components are fused with the fusion rule based on the subregion variance. Then the low frequency components and the high frequency components after fusion are processed with the inverse NSCT, and the fused component is gotten. Finally, the fused component, the component H and the component S are processed with the inverse IHS transform, and the fusion image is obtained. The experiment results show that the proposed method can get better fusion results and faster computing speed for multispectral image and panchromatic image.The work was supported in part supported by (1) the Fund Project of National Natural Science of China(U1204402), (2) the Foundation Project(21AT-2016-13) supported by the twenty-first century Aerospace technology Co., Ltd., China, (3) the Natural Science Research Program Project (18A520001) supported by the Department of Education in Henan Province, China

Use of Coherent Point Drift in computer vision applications

This thesis presents the novel use of Coherent Point Drift in improving the robustness of a number of computer vision applications. CPD approach includes two methods for registering two images - rigid and non-rigid point set approaches which are based on the transformation model used. The key characteristic of a rigid transformation is that the distance between points is preserved, which means it can be used in the presence of translation, rotation, and scaling. Non-rigid transformations - or affine transforms - provide the opportunity of registering under non-uniform scaling and skew. The idea is to move one point set coherently to align with the second point set. The CPD method finds both the non-rigid transformation and the correspondence distance between two point sets at the same time without having to use a-priori declaration of the transformation model used. The first part of this thesis is focused on speaker identification in video conferencing. A real-time, audio-coupled video based approach is presented, which focuses more on the video analysis side, rather than the audio analysis that is known to be prone to errors. CPD is effectively utilised for lip movement detection and a temporal face detection approach is used to minimise false positives if face detection algorithm fails to perform. The second part of the thesis is focused on multi-exposure and multi-focus image fusion with compensation for camera shake. Scale Invariant Feature Transforms (SIFT) are first used to detect keypoints in images being fused. Subsequently this point set is reduced to remove outliers, using RANSAC (RANdom Sample Consensus) and finally the point sets are registered using CPD with non-rigid transformations. The registered images are then fused with a Contourlet based image fusion algorithm that makes use of a novel alpha blending and filtering technique to minimise artefacts. The thesis evaluates the performance of the algorithm in comparison to a number of state-of-the-art approaches, including the key commercial products available in the market at present, showing significantly improved subjective quality in the fused images. The final part of the thesis presents a novel approach to Vehicle Make & Model Recognition in CCTV video footage. CPD is used to effectively remove skew of vehicles detected as CCTV cameras are not specifically configured for the VMMR task and may capture vehicles at different approaching angles. A LESH (Local Energy Shape Histogram) feature based approach is used for vehicle make and model recognition with the novelty that temporal processing is used to improve reliability. A number of further algorithms are used to maximise the reliability of the final outcome. Experimental results are provided to prove that the proposed system demonstrates an accuracy in excess of 95% when tested on real CCTV footage with no prior camera calibration