A Study on Super-Resolution Image Reconstruction Techniques

With the rapid development of space technology and its related technologies, more and more remote sensing platforms are sent to outer space to survey our earth. Recognizing and positioning all these space objects is the basis of knowing about the space, but there are no other effective methods in space target recognition except orbit and radio signal recognition. Super-resolution image reconstruction, which is based on the image of space objects, provides an effective way of solving this problem. In this paper, the principle of super-resolution image reconstruction and several typical reconstruction methods were introduced. By comparison, Nonparametric Finite Support Restoration Techniques were analyzed in details. At last, several aspects of super-resolution image reconstruction that should be studied further more were put forward

A Short Survey of Image Super Resolution Algorithms

Image super resolution is to estimate a high resolution image from a low resolution image or a sequence of low resolution images using image processing and machine learning technology. So far, there have emerged lots of super resolution algorithms. According to the input number of image, these algorithms can usually be divided as single image based algorithm and multiple images based algorithm. And according to technique principle, these algorithms can also be divided into three categories - interpolation based algorithm, reconstruction based algorithm and learning based one. This work mainly addresses the basic principle and different strategy of super resolution algorithms in detail. Then, the evaluation criteria and its application issues of super resolution are also discussed in the end

Метод субпіксельної обробки аерозображень, отриманих оптичною системою квадрокоптеру

Структура та обсяг роботи. Пояснювальна записка магістерської дисертації складається з чотирьох розділів, містить 52 рисунка, 8 таблиць, 1 додаток, 77 джерел. Магістерська дисертація присвячена розробці програмного модуля підвищення просторової розрізненності аерозображень, отриманих за допомогою квадрокоптеру. Метою даної розробки є покращення якості зображень, отриманих за допомогою оптичної системи квадрокоптера, за рахунок підвищення просторової розрізненності аерозображень. У розділі загальних підходів до підвищення роздільної здатності та просторової розрізненності зображень наводяться різні методи підвищення роздільної здатності, а також методи підвищення просторової розрізненності зображень. Розділ реконструкції зображення високої розрізненності присвячений отриманню зображення високої розрізненності за допомогою методу субпіксельної обробки зображень. Розділ алгоритму реконструкції зображення високої розрізненності містить алгоритм реконструкції зображення високої розрізненності, опис використаних технологій та архітектуру програмного комплексу підвищення просторової розрізненності аерознімків. У розділі випробування програмного продукту наведено методику випробувань, результати випробувань програмного продукту, а також результати досліджень.Structure and content. Master's thesis consists of four sections, containing 52 figures, 8 tables, 1 appendix, 77 sources. The master's thesis is devoted to the development of a software module for spatial resolution enchancement of aerial images acquired with a quadcopter. The purpose of this research is to improve the quality of images acquired by the digital camera of a quadcopter, using the super-resolution approach. In overview section the general methods for imagery spatial resolution enchancement are described. The image reconstruction section is devoted to restoring a high-resolution image using the subpixel processing of input low-resolution images. The algorithm section contains an algorithm description for the high-resolution image restoring, a description of engaged techniques as well as the software architecture developed for the spatial resolution enchancement of aerial imagery. The test section presented the both test method and the test results of developed software product. The conclusion section overviews the research are gives the thesis’s general outputs

Super-Resolution of Unmanned Airborne Vehicle Images with Maximum Fidelity Stochastic Restoration

Super-resolution (SR) refers to reconstructing a single high resolution (HR) image from a set of subsampled, blurred and noisy low resolution (LR) images. One may, then, envision a scenario where a set of LR images is acquired with sensors on a moving platform like unmanned airborne vehicles (UAV). Due to the wind, the UAV may encounter altitude change or rotational effects which can distort the acquired as well as the processed images. Also, the visual quality of the SR image is affected by image acquisition degradations, the available number of the LR images and their relative positions. This dissertation seeks to develop a novel fast stochastic algorithm to reconstruct a single SR image from UAV-captured images in two steps. First, the UAV LR images are aligned using a new hybrid registration algorithm within subpixel accuracy. In the second step, the proposed approach develops a new fast stochastic minimum square constrained Wiener restoration filter for SR reconstruction and restoration using a fully detailed continuous-discrete-continuous (CDC) model. A new parameter that accounts for LR images registration and fusion errors is added to the SR CDC model in addition to a multi-response restoration and reconstruction. Finally, to assess the visual quality of the resultant images, two figures of merit are introduced: information rate and maximum realizable fidelity. Experimental results show that quantitative assessment using the proposed figures coincided with the visual qualitative assessment. We evaluated our filter against other SR techniques and its results were found to be competitive in terms of speed and visual quality

Bulanıklık operatör bilgisi olmadan süper-çözünürlüklü görüntü elde edilmesi

06.03.2018 tarihli ve 30352 sayılı Resmi Gazetede yayımlanan “Yükseköğretim Kanunu İle Bazı Kanun Ve Kanun Hükmünde Kararnamelerde Değişiklik Yapılması Hakkında Kanun” ile 18.06.2018 tarihli “Lisansüstü Tezlerin Elektronik Ortamda Toplanması, Düzenlenmesi ve Erişime Açılmasına İlişkin Yönerge” gereğince tam metin erişime açılmıştır.Süper-çözünürlüklü görüntü oluşturma, eldeki çok sayıda düşük kaliteli (düşük çözünürlüklü, bulanıklığa uğramış) ve birbirine göre kaymış görüntüden yüksek kaliteli (yüksek çözünürlüklü, bulanıklık etkileri giderilmiş) bir görüntü elde etmektir. Literatürde önerilen hemen hemen tüm görüntü süper-çözünürlüğü yöntemlerinde bulanıklık operatörünün bilindiği varsayılmıştır. Ancak, pratik uygulamalarda kullanılacak bir süper-çözünürlük yönteminin gözü kapalı, yani bulanıklık operatörünü biliniyor varsaymayan olması gerekmektedir. Bu tez çalışmasında, bulanıklık operatörü bilinmiyor iken görüntü süper-çözünürlüğünün sağlanması ile ilgili çalışmalar yapılmıştır. Öncelikle gözlem modelinin izin verdiği hareket çeşidi olarak genel kayma hareketi ele alınmıştır. Bu durumda yüksek çözünürlüklü görüntü, iki aşamalı bir yöntemle oluşturulabilir. Birinci aşama, düşük çözünürlüklü görüntülerin boyutunu, ara-değerleme veya piksel aralarına sıfır değerli pikseller ekleme yoluyla arttırmaktır. İkinci aşama, boyutları arttırılmış görüntülerin her birini ayrı ayrı yeniden-oluşturma filtrelerinden geçirip toplamak ve yüksek çözünürlüklü görüntüyü elde etmektir. Yeniden oluşturma filtreleri, uyarlanır bir yapıya sahiptir ve katsayıları, her yinelemede görüntü ile ilgili bir maliyet fonksiyonunu (sabit-büyüklük maliyeti) enküçültecek şekilde yenilenir. Bu şekilde geliştirilen algoritma, piksel başına düşen bit sayısı düşük iken iyi sonuçlar vermiştir, ancak bit sayısı yükseldikçe performansı kötüleşmiştir. Bu durumu engellemek için gerçek görüntü piksel değerlerini karmaşık sayılar varsayan ve karmaşık değerli yeniden-oluşturma filtreleri kullanan yeni bir yöntem geliştirilmiştir. Geliştirilen yöntem, yüksek bit sayılarında performansın kötüleşmesi problemini gidermiş ve yüksek çözünürlüklü görüntüyü elde etmeyi başarmıştır.Gözü-kapalı süper-çözünürlük yöntemlerini geliştirmeden önce yeniden-oluşturma filtrelerinin varlık ve teklik koşulları araştırılmıştır. Yapılan analizler sonucunda, düşük çözünürlüklü görüntü sayısı belli bir değerden fazlaysa ve bulanıklık operatörlerinin birbirlerinden doğrusal bağımsız olması durumunda, boyutları belli bir değerden büyük olacak şekilde yeniden-oluşturma filtre kümelerinin oluşturulabileceği görülmüştür.Super-resolution image reconstruction can be defined as the process of constructing a high-quality and high-resolution image from several shifted, degraded and undersampled ones. In almost all super-resolution methods, the blur operator is assumed to be known. In this thesis, a super-resolution algorithm is presented in which the assumption of availability of the blur parameters is not necessary. The algorithm consists of determining a set of deconvolution filters to be applied on interpolated low-resolution and low-quality images. The adaptation of the filters are done by using the constant modulus algorithm. The method is suitable for pure translational motion and shift-invariant blur. Experimental results show that the method can reconstruct the high-resolution image and remove the blur especially for five or less-bit images. A new method is developed in which the original image pixels are assumed to have complex values and complex-valued adaptive filters are used. This method does not suffer from the problem of degradation of performance as the bit number increases. The method is shown to remove the blur and achieve increase in resolution for any-bit images.Before developing the blind super-resolution algorithms, the conditions for the existence and uniqueness of FIR restoration filters for exact super-resolution image reconstruction in case of pure translational motion and shift-invariant blur are derived. If the number of low-resolution images is larger than a threshold and the blur functions meet a certain property, then a set of restoration filters can be constructed for exact high-resolution image reconstruction even in the absence of motion