High resolution images from low resolution video sequences

In some cases, low resolution of those images composing a video film hinders the proper visual interpretation of its data. A typical example of this is video obtained from security cameras. There thus exists the need to count with some method allowing the processing of such information in order to obtain a better quality and a higher level of detail of those images. This gives rise to the possibility of making a more reliable interpretation of images, all of which eases the determination of, for example, some people face features or a car plate numbers. Nowadays, there exist some techniques that are related to this topic (called Image Super- Resolution techniques), though in the theoretical field in principle. Besides, there is no integral solution presented as integral product for its utilization. This paper presents the preliminary results of the Super-Resolution techniques applied to video sequences with the possibility of using quality enhancement preprocessing in each individual image.Facultad de Informátic

Development Of A High Performance Mosaicing And Super-Resolution Algorithm

In this dissertation, a high-performance mosaicing and super-resolution algorithm is described. The scale invariant feature transform (SIFT)-based mosaicing algorithm builds an initial mosaic which is iteratively updated by the robust super resolution algorithm to achieve the final high-resolution mosaic. Two different types of datasets are used for testing: high altitude balloon data and unmanned aerial vehicle data. To evaluate our algorithm, five performance metrics are employed: mean square error, peak signal to noise ratio, singular value decomposition, slope of reciprocal singular value curve, and cumulative probability of blur detection. Extensive testing shows that the proposed algorithm is effective in improving the captured aerial data and the performance metrics are accurate in quantifying the evaluation of the algorithm

High resolution images from low resolution video sequences

In some cases, low resolution of those images composing a video film hinders the proper visual interpretation of its data. A typical example of this is video obtained from security cameras. There thus exists the need to count with some method allowing the processing of such information in order to obtain a better quality and a higher level of detail of those images. This gives rise to the possibility of making a more reliable interpretation of images, all of which eases the determination of, for example, some people face features or a car plate numbers. Nowadays, there exist some techniques that are related to this topic (called Image Super- Resolution techniques), though in the theoretical field in principle. Besides, there is no integral solution presented as integral product for its utilization. This paper presents the preliminary results of the Super-Resolution techniques applied to video sequences with the possibility of using quality enhancement preprocessing in each individual image.Facultad de Informátic

Advances in single frame image recovery

This thesis tackles a problem of recovering a high resolution image from a single compressed frame. A new image-prior that is devised based on Pearson type VII density is integrated with a Markov Random Field model which has desirable robustness properties. A fully automated hyper-parameter estimation procedure for this approach is developed, which makes it advantageous in comparison with alternatives. Although this recovery algorithm is very simple to implement, it achieves statistically significant improvements over previous results in under-determined problem settings, and it is able to recover images that contain texture. This advancement opens up the opportunities for several potential extensions, of which we pursue two: (i) Most of previous work does not consider any specific extra information to recover the signal. Thus, this thesis exploits the similarity between the signal of interest and a consecutive motionless frame to address this problem. Additional information of similarity that is available is incorporated into a probabilistic image-prior based on the Pearson type VII Markov Random Field model. Results on both synthetic and real data of Magnetic Resonance Imaging (MRI) images demonstrate the effectiveness of our method in both compressed setting and classical super-resolution experiments. (ii) This thesis also presents a multi-task approach for signal recovery by sharing higher-level hyperparameters which do not relate directly to the actual content of the signals of interest but only to their statistical characteristics. Our approach leads to a very simple model and algorithm that can be used to simultaneously recover multipl

Generación de imágenes de alta resolución utilizando secuencias de video de baja resolución

En ciertos casos, la baja resolución de las imágenes que conforman una filmación de video impide la correcta interpretación visual de la información almacenada en ésta. Un ejemplo típico son los videos obtenidos a partir de cámaras de seguridad. Existe entonces la necesidad de contar con algún método que permita procesar dicha información para obtener una mejor calidad y un mayor nivel de detalle de éstas. Esto brinda la posibilidad de realizar una interpretación más confiable de las imágenes, ayudando así a determinar, por ejemplo, los rasgos del rostro de una persona o el número de patente de un automóvil. Actualmente existen técnicas relacionadas con este tema (denominadas técnicas de Súper Resolución de imágenes) aunque principalmente en el campo teórico. No se cuenta además, con una solución integral presentada como producto integral para su utilización. Este trabajo presenta resultados preliminares sobre las técnicas de Súper Resolución aplicadas a secuencias de video con la posibilidad de utilizar preprocesamiento de mejora de la calidad a cada imagen individual.Eje: II - Workshop de computación gráfica, imágenes y visualizaciónRed de Universidades con Carreras en Informática (RedUNCI

Simultaneous estimation of super-resolved scene and depth map from low resolution defocused observations

This paper presents a novel technique to simultaneously estimate the depth map and the focused image of a scene, both at a super-resolution, from its defocused observations. Super-resolution refers to the generation of high spatial resolution images from a sequence of low resolution images. Hitherto, the super-resolution technique has been restricted mostly to the intensity domain. In this paper, we extend the scope of super-resolution imaging to acquire depth estimates at high spatial resolution simultaneously. Given a sequence of low resolution, blurred, and noisy observations of a static scene, the problem is to generate a dense depth map at a resolution higher than one that can be generated from the observations as well as to estimate the true high resolution focused image. Both the depth and the image are modeled as separate Markov random fields (MRF) and a maximum a posteriori estimation method is used to recover the high resolution fields. Since there is no relative motion between the scene and the camera, as is the case with most of the super-resolution and structure recovery techniques, we do away with the correspondence problem

Super resolution and dynamic range enhancement of image sequences

Camera producers try to increase the spatial resolution of a camera by reducing size of sites on sensor array. However, shot noise causes the signal to noise ratio drop as sensor sites get smaller. This fact motivates resolution enhancement to be performed through software. Super resolution (SR) image reconstruction aims to combine degraded images of a scene in order to form an image which has higher resolution than all observations. There is a demand for high resolution images in biomedical imaging, surveillance, aerial/satellite imaging and high-definition TV (HDTV) technology. Although extensive research has been conducted in SR, attention has not been given to increase the resolution of images under illumination changes. In this study, a unique framework is proposed to increase the spatial resolution and dynamic range of a video sequence using Bayesian and Projection onto Convex Sets (POCS) methods. Incorporating camera response function estimation into image reconstruction allows dynamic range enhancement along with spatial resolution improvement. Photometrically varying input images complicate process of projecting observations onto common grid by violating brightness constancy. A contrast invariant feature transform is proposed in this thesis to register input images with high illumination variation. Proposed algorithm increases the repeatability rate of detected features among frames of a video. Repeatability rate is increased by computing the autocorrelation matrix using the gradients of contrast stretched input images. Presented contrast invariant feature detection improves repeatability rate of Harris corner detector around %25 on average. Joint multi-frame demosaicking and resolution enhancement is also investigated in this thesis. Color constancy constraint set is devised and incorporated into POCS framework for increasing resolution of color-filter array sampled images. Proposed method provides fewer demosaicking artifacts compared to existing POCS method and a higher visual quality in final image