Global motion based video super-resolution reconstruction using discrete wavelet transform

Different from the existing super-resolution (SR) reconstruction approaches working under either the frequency-domain or the spatial- domain, this paper proposes an improved video SR approach based on both frequency and spatial-domains to improve the spatial resolution and recover the noiseless high-frequency components of the observed noisy low-resolution video sequences with global motion. An iterative planar motion estimation algorithm followed by a structure-adaptive normalised convolution reconstruction method are applied to produce the estimated low-frequency sub-band. The discrete wavelet transform process is employed to decompose the input low-resolution reference frame into four sub-bands, and then the new edge-directed interpolation method is used to interpolate each of the high-frequency sub-bands. The novelty of this algorithm is the introduction and integration of a nonlinear soft thresholding process to filter the estimated high-frequency sub-bands in order to better preserve the edges and remove potential noise. Another novelty of this algorithm is to provide flexibility with various motion levels, noise levels, wavelet functions, and the number of used low-resolution frames. The performance of the proposed method has been tested on three well-known videos. Both visual and quantitative results demonstrate the high performance and improved flexibility of the proposed technique over the conventional interpolation and the state-of-the-art video SR techniques in the wavelet- domain

Wavelet-based image and video super-resolution reconstruction.

Super-resolution reconstruction process offers the solution to overcome the high-cost and inherent resolution limitations of current imaging systems. The wavelet transform is a powerful tool for super-resolution reconstruction. This research provides a detailed study of the wavelet-based super-resolution reconstruction process, and wavelet-based resolution enhancement process (with which it is closely associated). It was addressed to handle an explicit need for a robust wavelet-based method that guarantees efficient utilisation of the SR reconstruction problem in the wavelet-domain, which will lead to a consistent solution of this problem and improved performance. This research proposes a novel performance assessment approach to improve the performance of the existing wavelet-based image resolution enhancement techniques. The novel approach is based on identifying the factors that effectively influence on the performance of these techniques, and designing a novel optimal factor analysis (OFA) algorithm. A new wavelet-based image resolution enhancement method, based on discrete wavelet transform and new-edge directed interpolation (DWT-NEDI), and an adaptive thresholding process, has been developed. The DWT-NEDI algorithm aims to correct the geometric errors and remove the noise for degraded satellite images. A robust wavelet-based video super-resolution technique, based on global motion is developed by combining the DWT-NEDI method, with super-resolution reconstruction methods, in order to increase the spatial-resolution and remove the noise and aliasing artefacts. A new video super-resolution framework is designed using an adaptive local motion decomposition and wavelet transform reconstruction (ALMD-WTR). This is to address the challenge of the super-resolution problem for the real-world video sequences containing complex local motions. The results show that OFA approach improves the performance of the selected wavelet-based methods. The DWT-NEDI algorithm outperforms the state-of-the art wavelet-based algorithms. The global motion-based algorithm has the best performance over the super-resolution techniques, namely Keren and structure-adaptive normalised convolution methods. ALMD-WTR framework surpass the state-of-the-art wavelet-based algorithm, namely local motion-based video super-resolution.PhD in Manufacturin

Face recognition in low resolution video sequences using super resolution

Human activity is a major concern in a wide variety of applications, such as video surveillance, human computer interface and face image database management. Detecting and recognizing faces is a crucial step in these applications. Furthermore, major advancements and initiatives in security applications in the past years have propelled face recognition technology into the spotlight. The performance of existing face recognition systems declines significantly if the resolution of the face image falls below a certain level. This is especially critical in surveillance imagery where often, due to many reasons, only low-resolution video of faces is available. If these low-resolution images are passed to a face recognition system, the performance is usually unacceptable. Hence, resolution plays a key role in face recognition systems. In this thesis, we address this issue by using super-resolution techniques as a middle step, where multiple low resolution face image frames are used to obtain a high-resolution face image for improved recognition rates. Two different techniques based on frequency and spatial domains were utilized in super resolution image enhancement. In this thesis, we apply super resolution to both images and video utilizing these techniques and we employ principal component analysis for face matching, which is both computationally efficient and accurate. The result is a system hat can accurately recognize faces using multiple low resolution images/frames

Computational Imaging Approach to Recovery of Target Coordinates Using Orbital Sensor Data

This dissertation addresses the components necessary for simulation of an image-based recovery of the position of a target using orbital image sensors. Each component is considered in detail, focusing on the effect that design choices and system parameters have on the accuracy of the position estimate. Changes in sensor resolution, varying amounts of blur, differences in image noise level, selection of algorithms used for each component, and lag introduced by excessive processing time all contribute to the accuracy of the result regarding recovery of target coordinates using orbital sensor data. Using physical targets and sensors in this scenario would be cost-prohibitive in the exploratory setting posed, therefore a simulated target path is generated using Bezier curves which approximate representative paths followed by the targets of interest. Orbital trajectories for the sensors are designed on an elliptical model representative of the motion of physical orbital sensors. Images from each sensor are simulated based on the position and orientation of the sensor, the position of the target, and the imaging parameters selected for the experiment (resolution, noise level, blur level, etc.). Post-processing of the simulated imagery seeks to reduce noise and blur and increase resolution. The only information available for calculating the target position by a fully implemented system are the sensor position and orientation vectors and the images from each sensor. From these data we develop a reliable method of recovering the target position and analyze the impact on near-realtime processing. We also discuss the influence of adjustments to system components on overall capabilities and address the potential system size, weight, and power requirements from realistic implementation approaches

Super-resolution of 3-dimensional scenes

Super-resolution is an image enhancement method that increases the resolution of images and video. Previously this technique could only be applied to 2D scenes. The super-resolution algorithm developed in this thesis creates high-resolution views of 3-dimensional scenes, using low-resolution images captured from varying, unknown positions

Enhancing face recognition at a distance using super resolution

The characteristics of surveillance video generally include low-resolution images and blurred images. Decreases in image resolution lead to loss of high frequency facial components, which is expected to adversely affect recognition rates. Super resolution (SR) is a technique used to generate a higher resolution image from a given low-resolution, degraded image. Dictionary based super resolution pre-processing techniques have been developed to overcome the problem of low-resolution images in face recognition. However, super resolution reconstruction process, being ill-posed, and results in visual artifacts that can be visually distracting to humans and/or affect machine feature extraction and face recognition algorithms. In this paper, we investigate the impact of two existing super-resolution methods to reconstruct a high resolution from single/multiple low-resolution images on face recognition. We propose an alternative scheme that is based on dictionaries in high frequency wavelet subbands. The performance of the proposed method will be evaluated on databases of high and low-resolution images captured under different illumination conditions and at different distances. We shall demonstrate that the proposed approach at level 3 DWT decomposition has superior performance in comparison to the other super resolution methods

Motion Segmentation Aided Super Resolution Image Reconstruction

This dissertation addresses Super Resolution (SR) Image Reconstruction focusing on motion segmentation. The main thrust is Information Complexity guided Gaussian Mixture Models (GMMs) for Statistical Background Modeling. In the process of developing our framework we also focus on two other topics; motion trajectories estimation toward global and local scene change detections and image reconstruction to have high resolution (HR) representations of the moving regions. Such a framework is used for dynamic scene understanding and recognition of individuals and threats with the help of the image sequences recorded with either stationary or non-stationary camera systems. We introduce a new technique called Information Complexity guided Statistical Background Modeling. Thus, we successfully employ GMMs, which are optimal with respect to information complexity criteria. Moving objects are segmented out through background subtraction which utilizes the computed background model. This technique produces superior results to competing background modeling strategies. The state-of-the-art SR Image Reconstruction studies combine the information from a set of unremarkably different low resolution (LR) images of static scene to construct an HR representation. The crucial challenge not handled in these studies is accumulating the corresponding information from highly displaced moving objects. In this aspect, a framework of SR Image Reconstruction of the moving objects with such high level of displacements is developed. Our assumption is that LR images are different from each other due to local motion of the objects and the global motion of the scene imposed by non-stationary imaging system. Contrary to traditional SR approaches, we employed several steps. These steps are; the suppression of the global motion, motion segmentation accompanied by background subtraction to extract moving objects, suppression of the local motion of the segmented out regions, and super-resolving accumulated information coming from moving objects rather than the whole scene. This results in a reliable offline SR Image Reconstruction tool which handles several types of dynamic scene changes, compensates the impacts of camera systems, and provides data redundancy through removing the background. The framework proved to be superior to the state-of-the-art algorithms which put no significant effort toward dynamic scene representation of non-stationary camera systems

Kinect based system applied to breast cancer conservative treatment

Tese de Mestrado Integrado. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 201

Super-resolution from unregistered aliased images

Aliasing in images is often considered as a nuisance. Artificial low frequency patterns and jagged edges appear when an image is sampled at a too low frequency. However, aliasing also conveys useful information about the high frequency content of the image, which is exploited in super-resolution applications. We use a set of input images of the same scene to extract such high frequency information and create a higher resolution aliasing-free image. Typically, there is a small shift or more complex motion between the different images, such that they contain slightly different information about the scene. Super-resolution image reconstruction can be formulated as a multichannel sampling problem with unknown offsets. This results in a set of equations that are linear in the unknown signal coefficients but nonlinear in the offsets. This thesis concentrates on the computation of these offsets, as they are an essential prerequisite for an accurate high resolution reconstruction. If a part of the image spectra is free of aliasing, the planar shift and rotation parameters can be computed using only this low frequency information. In such a case, the images can be registered pairwise to a reference image. Such a method is not applicable if the images are undersampled by a factor of two or larger. A higher number of images needs to be registered jointly. Two subspace methods are discussed for such highly aliased images. The first approach is based on a Fourier description of the aliased signals as a sum of overlapping parts of the spectrum. It uses a rank condition to find the correct offsets. The second one uses a more general expansion in an arbitrary Hilbert space to compute the signal offsets. The sampled signal is represented as a linear combination of sampled basis functions. The offsets are computed by projecting the signal onto varying subspaces. Under certain conditions, in particular for bandlimited signals, the nonlinear super-resolution equations can be written as a set of polynomial equations. Using Buchberger's algorithm, the solution can then be computed as a Gröbner basis for the corresponding polynomial ideal. After a description of a standard algorithm, adaptations are made for the use with noisy measurements. The techniques presented in this thesis are tested in simulations and practical experiments. The experiments are performed on sets of real images taken with a digital camera. The results show the validity of the algorithms: registration parameters are computed with subpixel precision, and aliasing is accurately removed from the resulting high resolution image. This thesis is produced according to the concepts of reproducible research. All the results and examples used in this thesis are reproducible using the code and data available online