Antenna Array Pattern Synthesis via Coordinate Descent Method

This paper presents an array pattern synthesis algorithm for arbitrary arrays based on coordinate descent method (CDM). With this algorithm, the complex element weights are found to minimize a weighted L2 norm of the difference between desired and achieved pattern. Compared with traditional optimization techniques, CDM is easy to implement and efficient to reach the optimum solutions. Main advantage is the flexibility. CDM is suitable for linear and planar array with arbitrary array elements on arbitrary positions. With this method, we can configure arbitrary beam pattern, which gives it the ability to solve variety of beam forming problem, e.g. focused beam, shaped beam, nulls at arbitrary direction and with arbitrary beam width. CDM is applicable for phase-only and amplitude-only arrays as well, and furthermore, it is a suitable method to treat the problem of array with element failures

A total variation regularization based super-resolution reconstruction algorithm for digital video

Super-resolution (SR) reconstruction technique is capable of producing a high-resolution image from a sequence of low-resolution images. In this paper, we study an efficient SR algorithm for digital video. To effectively deal with the intractable problems in SR video reconstruction, such as inevitable motion estimation errors, noise, blurring, missing regions, and compression artifacts, the total variation (TV) regularization is employed in the reconstruction model. We use the fixed-point iteration method and preconditioning techniques to efficiently solve the associated nonlinear Euler-Lagrange equations of the corresponding variational problem in SR. The proposed algorithm has been tested in several cases of motion and degradation. It is also compared with the Laplacian regularization-based SR algorithm and other TV-based SR algorithms. Experimental results are presented to illustrate the effectiveness of the proposed algorithm.£.published_or_final_versio

Multiscale and Multitopic Sparse Representation for Multisensor Infrared Image Superresolution

Methods based on sparse coding have been successfully used in single-image superresolution (SR) reconstruction. However, the traditional sparse representation-based SR image reconstruction for infrared (IR) images usually suffers from three problems. First, IR images always lack detailed information. Second, a traditional sparse dictionary is learned from patches with a fixed size, which may not capture the exact information of the images and may ignore the fact that images naturally come at different scales in many cases. Finally, traditional sparse dictionary learning methods aim at learning a universal and overcomplete dictionary. However, many different local structural patterns exist. One dictionary is inadequate in capturing all of the different structures. We propose a novel IR image SR method to overcome these problems. First, we combine the information from multisensors to improve the resolution of the IR image. Then, we use multiscale patches to represent the image in a more efficient manner. Finally, we partition the natural images into documents and group such documents to determine the inherent topics and to learn the sparse dictionary of each topic. Extensive experiments validate that using the proposed method yields better results in terms of quantitation and visual perception than many state-of-the-art algorithms

A Variational Model for Joint Motion Estimation and Image Reconstruction

The aim of this paper is to derive and analyze a variational model for the joint estimation of motion and reconstruction of image sequences, which is based on a time-continuous Eulerian motion model. The model can be set up in terms of the continuity equation or the brightness constancy equation. The analysis in this paper focuses on the latter for robust motion estimation on sequences of twodimensional images. We rigorously prove the existence of a minimizer in a suitable function space setting. Moreover, we discuss the numerical solution of the model based on primal-dual algorithms and investigate several examples. Finally, the benefits of our model compared to existing techniques, such as sequential image reconstruction and motion estimation, are shown.The work of the first author was also supported by the German Science Foundation DFG via EXC 1003 Cells in Motion Cluster of Excellence, M¨unster, German

Optical flow segmentation for pedestrian detection

This project will study the motion between consecutive frames in a video in order to segment meaningful regions. In particular, video recorded from a camera on top of a car will be used to analyse the pedestrian moving around it. This information could potentially be used to alert the driver of possible obstacles.Pedestrian detection has become an active area of research in recent years. It is widely applied in different applications such as surveillance systems, automotive safety or robotics among others. The current project aims to localize moving objects on sequences of images, focusing on pedestrian detection. First, the apparent motion in the scene will be computed. Afterward motion vectors will be divided into moving objects or background and finally, the resulting segments will be analysed by introducing them into a classifier in order to determine if they are pedestrians or not.La detección de peatones se ha convertido en un área de investigación muy activa en los últimos años. Se aplica en una gran variedad de aplicaciones, como por ejemplo en sistema de vigilancia, seguridad en automóviles o en la robótica, entre otros. Este proyecto pretende localizar objetos en movimiento en secuencias de imágenes, centrando la atención en la detección de peatones. Primeramente, se calculará el movimiento aparente en la escena, a continuación, los vectores de movimiento se dividirán entre objetos móviles y fondo, y finalmente, las segmentaciones obtenidas serán analizadas introduciéndolas en un clasificador, para determinar si se trata de peatones o no. La detecció de vianants s’ha convertit en una àrea d’investigació molt activa en els darrers anys. S’aplica a una gran varietat d’aplicacions com per exemple en sistemes de vigilància, seguretat en automòbils o en la robòtica, entre d’altres. Aquest projecte pretén localitzar objectes en moviment en seqüències d’imatges, centrant-ne l’atenció en la detecció de vianants. Primerament, es calcularà el moviment aparent en l’escena, a continuació, els vectors de moviment es dividiran entre objectes mòbils o en fons estàtic, i finalment, els segments obtinguts seran analitzats introduint-los en un classificador, per tal de determinar si es tracta de vianants o no

Region-Based Approach for Single Image Super-Resolution

Single image super-resolution (SR) is a technique that generates a high- resolution image from a single low-resolution image [1,2,10,11]. Single image super- resolution can be generally classified into two groups: example-based and self-similarity based SR algorithms. The performance of the example-based SR algorithm depends on the similarity between testing data and the database. Usually, a large database is needed for better performance in general. This would result in heavy computational cost. The self-similarity based SR algorithm can generate a high-resolution (HR) image with sharper edges and fewer ringing artifacts if there is sufficient recurrence within or across scales of the same image [10, 11], but it is hard to generate HR details for an image region with fine texture. Based on the limitation of each type of SR algorithm, we propose to combine these two types of algorithms. We segment each image into regions based on image content, and choose the appropriate SR algorithm to recover the HR image for each region based on the texture feature. Our experimental results show that our proposed method takes advantage of each SR algorithm and can produce natural looking results with sharp edges, while suppressing ringing artifacts. We compute PSNR to qualitatively evaluate the SR results, and our proposed method outperforms the self-similarity based or example-based SR algorithm with higher PSNR (+0.1dB)

3次元画像の高画質化・高機能化に向けた解像度変換処理の研究

学位の種別:課程博士University of Tokyo(東京大学

Super Resolution of Wavelet-Encoded Images and Videos

In this dissertation, we address the multiframe super resolution reconstruction problem for wavelet-encoded images and videos. The goal of multiframe super resolution is to obtain one or more high resolution images by fusing a sequence of degraded or aliased low resolution images of the same scene. Since the low resolution images may be unaligned, a registration step is required before super resolution reconstruction. Therefore, we first explore in-band (i.e. in the wavelet-domain) image registration; then, investigate super resolution. Our motivation for analyzing the image registration and super resolution problems in the wavelet domain is the growing trend in wavelet-encoded imaging, and wavelet-encoding for image/video compression. Due to drawbacks of widely used discrete cosine transform in image and video compression, a considerable amount of literature is devoted to wavelet-based methods. However, since wavelets are shift-variant, existing methods cannot utilize wavelet subbands efficiently. In order to overcome this drawback, we establish and explore the direct relationship between the subbands under a translational shift, for image registration and super resolution. We then employ our devised in-band methodology, in a motion compensated video compression framework, to demonstrate the effective usage of wavelet subbands. Super resolution can also be used as a post-processing step in video compression in order to decrease the size of the video files to be compressed, with downsampling added as a pre-processing step. Therefore, we present a video compression scheme that utilizes super resolution to reconstruct the high frequency information lost during downsampling. In addition, super resolution is a crucial post-processing step for satellite imagery, due to the fact that it is hard to update imaging devices after a satellite is launched. Thus, we also demonstrate the usage of our devised methods in enhancing resolution of pansharpened multispectral images