Feature Extraction for image super-resolution using finite rate of innovation principles

To understand a real-world scene from several multiview pictures, it is necessary to find the disparities existing between each pair of images so that they are correctly related to one another. This process, called image registration, requires the extraction of some specific information about the scene. This is achieved by taking features out of the acquired images. Thus, the quality of the registration depends largely on the accuracy of the extracted features. Feature extraction can be formulated as a sampling problem for which perfect re- construction of the desired features is wanted. The recent sampling theory for signals with finite rate of innovation (FRI) and the B-spline theory offer an appropriate new frame- work for the extraction of features in real images. This thesis first focuses on extending the sampling theory for FRI signals to a multichannel case and then presents exact sampling results for two different types of image features used for registration: moments and edges. In the first part, it is shown that the geometric moments of an observed scene can be retrieved exactly from sampled images and used as global features for registration. The second part describes how edges can also be retrieved perfectly from sampled images for registration purposes. The proposed feature extraction schemes therefore allow in theory the exact registration of images. Indeed, various simulations show that the proposed extraction/registration methods overcome traditional ones, especially at low-resolution. These characteristics make such feature extraction techniques very appropriate for applications like image super-resolution for which a very precise registration is needed. The quality of the super-resolved images obtained using the proposed feature extraction meth- ods is improved by comparison with other approaches. Finally, the notion of polyphase components is used to adapt the image acquisition model to the characteristics of real digital cameras in order to run super-resolution experiments on real images

Distortion Robust Biometric Recognition

abstract: Information forensics and security have come a long way in just a few years thanks to the recent advances in biometric recognition. The main challenge remains a proper design of a biometric modality that can be resilient to unconstrained conditions, such as quality distortions. This work presents a solution to face and ear recognition under unconstrained visual variations, with a main focus on recognition in the presence of blur, occlusion and additive noise distortions. First, the dissertation addresses the problem of scene variations in the presence of blur, occlusion and additive noise distortions resulting from capture, processing and transmission. Despite their excellent performance, ’deep’ methods are susceptible to visual distortions, which significantly reduce their performance. Sparse representations, on the other hand, have shown huge potential capabilities in handling problems, such as occlusion and corruption. In this work, an augmented SRC (ASRC) framework is presented to improve the performance of the Spare Representation Classifier (SRC) in the presence of blur, additive noise and block occlusion, while preserving its robustness to scene dependent variations. Different feature types are considered in the performance evaluation including image raw pixels, HoG and deep learning VGG-Face. The proposed ASRC framework is shown to outperform the conventional SRC in terms of recognition accuracy, in addition to other existing sparse-based methods and blur invariant methods at medium to high levels of distortion, when particularly used with discriminative features. In order to assess the quality of features in improving both the sparsity of the representation and the classification accuracy, a feature sparse coding and classification index (FSCCI) is proposed and used for feature ranking and selection within both the SRC and ASRC frameworks. The second part of the dissertation presents a method for unconstrained ear recognition using deep learning features. The unconstrained ear recognition is performed using transfer learning with deep neural networks (DNNs) as a feature extractor followed by a shallow classifier. Data augmentation is used to improve the recognition performance by augmenting the training dataset with image transformations. The recognition performance of the feature extraction models is compared with an ensemble of fine-tuned networks. The results show that, in the case where long training time is not desirable or a large amount of data is not available, the features from pre-trained DNNs can be used with a shallow classifier to give a comparable recognition accuracy to the fine-tuned networks.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201

Detection and estimation of image blur

The airborne imagery consisting of infrared (IR) and multispectral (MSI) images collected in 2009 under airborne mine and minefield detection program by Night Vision and Electronic Sensors Directorate (NVESD) was found to be severely blurred due to relative motion between the camera and the object and some of them with defocus blurs due to various reasons. Automated detection of blur due to motion and defocus blurs and the estimation of blur like point spread function for severely degraded images is an important task for processing and detection in such airborne imagery. Although several full reference and reduced reference methods are available in the literature, using no reference methods are desirable because there was no information of the degradation function and the original image data. In this thesis, three no reference algorithms viz. Haar wavelet (HAAR), modified Haar using singular value decomposition (SVD), and intentional blurring pixel difference (IBD) for blur detection are compared and their performance is qualified based on missed detections and false alarms. Three human subjects were chosen to perform subjective testing on randomly selected data sets and the truth for each frame was obtained from majority voting. The modified Haar algorithm (SVD) resulted in the least number of missed detections and least number of false alarms. This thesis also evaluates several methods for estimating the point spread function (PSF) of these degraded images. The Auto-correlation function (ACF), Hough transform (Hough) and steer Gaussian filter (SGF) based methods were tested on several synthetically motion blurred images and further validated on naturally blurred images. Statistics of pixel error estimate using these methods were computed based on 8640 artificially blurred image frames --Abstract, page iii

Scalable image quality assessment with 2D mel-cepstrum and machine learning approach

Cataloged from PDF version of article.Measurement of image quality is of fundamental importance to numerous image and video processing applications. Objective image quality assessment (IQA) is a two-stage process comprising of the following: (a) extraction of important information and discarding the redundant one, (b) pooling the detected features using appropriate weights. These two stages are not easy to tackle due to the complex nature of the human visual system (HVS). In this paper, we first investigate image features based on two-dimensional (20) mel-cepstrum for the purpose of IQA. It is shown that these features are effective since they can represent the structural information, which is crucial for IQA. Moreover, they are also beneficial in a reduced-reference scenario where only partial reference image information is used for quality assessment. We address the second issue by exploiting machine learning. In our opinion, the well established methodology of machine learning/pattern recognition has not been adequately used for IQA so far; we believe that it will be an effective tool for feature pooling since the required weights/parameters can be determined in a more convincing way via training with the ground truth obtained according to subjective scores. This helps to overcome the limitations of the existing pooling methods, which tend to be over simplistic and lack theoretical justification. Therefore, we propose a new metric by formulating IQA as a pattern recognition problem. Extensive experiments conducted using six publicly available image databases (totally 3211 images with diverse distortions) and one video database (with 78 video sequences) demonstrate the effectiveness and efficiency of the proposed metric, in comparison with seven relevant existing metrics. (C) 2011 Elsevier Ltd. All rights reserved

Motion blur invariant for estimating motion parameters of medical ultrasound images

High-quality medical ultrasound imaging is definitely concerning motion blur, while medical image analysis requires motionless and accurate data acquired by sonographers. The main idea of this paper is to establish some motion blur invariant in both frequency and moment domain to estimate the motion parameters of ultrasound images. We propose a discrete model of point spread function of motion blur convolution based on the Dirac delta function to simplify the analysis of motion invariant in frequency and moment domain. This model paves the way for estimating the motion angle and length in terms of the proposed invariant features. In this research, the performance of the proposed schemes is compared with other state-of-the-art existing methods of image deblurring. The experimental study performs using fetal phantom images and clinical fetal ultrasound images as well as breast scans. Moreover, to validate the accuracy of the proposed experimental framework, we apply two image quality assessment methods as no-reference and full-reference to show the robustness of the proposed algorithms compared to the well-known approaches

Modulation Transfer Function Compensation Through A Modified Wiener Filter For Spatial Image Quality Improvement.

Kebergunaan data imej yang diperolehi dari suatu sensor pengimejan amat bergantung kepada keupayaan sensor tersebut untuk meresolusikan perincian spatial ke satu tahap yang boleh diterima. The usefulness of image data acquired from an imaging sensor critically depends on the ability of the sensor to resolve spatial details to an acceptable level

Scale-invariant segmentation of dynamic contrast-enhanced perfusion MR-images with inherent scale selection

Selection of the best set of scales is problematic when developing signaldriven approaches for pixel-based image segmentation. Often, different possibly conflicting criteria need to be fulfilled in order to obtain the best tradeoff between uncertainty (variance) and location accuracy. The optimal set of scales depends on several factors: the noise level present in the image material, the prior distribution of the different types of segments, the class-conditional distributions associated with each type of segment as well as the actual size of the (connected) segments. We analyse, theoretically and through experiments, the possibility of using the overall and class-conditional error rates as criteria for selecting the optimal sampling of the linear and morphological scale spaces. It is shown that the overall error rate is optimised by taking the prior class distribution in the image material into account. However, a uniform (ignorant) prior distribution ensures constant class-conditional error rates. Consequently, we advocate for a uniform prior class distribution when an uncommitted, scaleinvariant segmentation approach is desired. Experiments with a neural net classifier developed for segmentation of dynamic MR images, acquired with a paramagnetic tracer, support the theoretical results. Furthermore, the experiments show that the addition of spatial features to the classifier, extracted from the linear or morphological scale spaces, improves the segmentation result compared to a signal-driven approach based solely on the dynamic MR signal. The segmentation results obtained from the two types of features are compared using two novel quality measures that characterise spatial properties of labelled images