Partition-based Interpolation for Color Filter Array Demosaicking and Super-Resolution Reconstruction

A class of partition-based interpolators that addresses a variety of image interpolation applications are proposed. The proposed interpolators first partition an image into a finite set of partitions that capture local image structures. Missing high resolution pixels are then obtained through linear operations on neighboring pixels that exploit the captured image structure. By exploiting the local image structure, the proposed algorithm produces excellent performance on both edge and uniform regions. The presented results demonstrate that partition-based interpolation yields results superior to traditional and advanced algorithms in the applications of color filter array (CFA) demosaicking and super-resolution reconstruction

Image Forgery Localization via Fine-Grained Analysis of CFA Artifacts

In this paper, a forensic tool able to discriminate between original and forged regions in an image captured by a digital camera is presented. We make the assumption that the image is acquired using a Color Filter Array, and that tampering removes the artifacts due to the demosaicking algorithm. The proposed method is based on a new feature measuring the presence of demosaicking artifacts at a local level, and on a new statistical model allowing to derive the tampering probability of each 2 × 2 image block without requiring to know a priori the position of the forged region. Experimental results on different cameras equipped with different demosaicking algorithms demonstrate both the validity of the theoretical model and the effectiveness of our schem

Quality Assessment of Mobile Phone Video Stabilization

Smartphone cameras are used more than ever for photography and videography. This has driven mobile phone manufacturers to develop and enhance cameras in their mobile phones. While mobile phone cameras have evolved a lot, many aspects of the mobile phone camera still have room for improvement. One is video stabilization which aims to remove unpleasant motion and artifacts from video. Many video stabilization methods for mobile phones exist. However, there is no standard video stabilization quality assessment (VSQA) framework for comparing the performance of the video stabilization methods. Huawei wanted to improve the video stabilization quality of their mobile phones by investigating video stabilization quality assessment. As a part of that endeavor, this work studies existing VSQA frameworks found in the literature and incorporates some of their ideas into a VSQA framework established in this work. The new VSQA framework consists of a repeatable laboratory environment and objective sharpness and motion metrics. To test the VSQA framework, videos were captured on multiple mobile phones in the laboratory environment. These videos were first subjectively evaluated to find issues that are noticeable by humans. Then the videos were objectively evaluated with the objective sharpness and motion metrics. The results show that the proposed VSQA framework can be used for comparing and ranking mobile devices. The VSQA framework successfully identifies the strengths and weaknesses of each tested device's video stabilization quality.Älypuhelimien kameroita käytetään nykyään valokuvaukseen enemmän kuin koskaan. Tämä on saanut älypuhelimien valmistajia kehittämään heidän puhelimiensa kameroita. Vaikka paljon edistystä on tapahtunut, niin moni älypuhelimen kameran osa-alueista kaipaa vielä kehitystä. Yksi heikoista osa-alueista on videostabilointi. Videostabiloinnin tarkoitus on poistaa videosta epämiellyttävä liike. Monia ratkaisuja löytyy, mutta mitään standardoitua tapaa vertailla eri stabilointi ratkaisuja ei ole. Huawei haluaa parantaa tuotteidensa videostabiloinnin laatua. Saavuttaakseen tämän tavoitteen, tässä työssä tehdään katsaus kirjallisuudesta löytyviä videostabiloinnin laadun mittausmenetelmiä ja jalostetaan näistä ideoita, joiden avulla kehitetään oma videonstabiloinnin laadun mittausmenetelmä. Menetelmä koostuu toistettavasta laboratorioympäristöstä, jossa voi kuvata heiluvia videoita eri älypuhelimilla. Näitä videoita vertaillaan objektiivisesti mittaamalla videoista terävyyttä ja liikkeen miellyttävyyttä. Työn videostabiloinnin laadun mittausmenetelmää testattiin kuvaamalla toistettavassa laboratorioympäristössä usealla älypuhelimella videoita, joissa on simuloitua käden tärinää. Ensin kuvattuja videoita arvioitiin ja vertailtiin subjektiivisesti, jotta niistä löytyisi ongelmat, joita videostabilointi ei ole onnistunut korjaamaan. Tämän jälkeen videoita arvioitiin objektiivisilla terävyys- ja liikemittareilla. Tulokset osoittavat, että työssä esitetty videostabiloinnin laadun mittausmenetelmää voidaan käyttää eri älypuhelimien videostabilointimenetelmien vertailuun. Työn mittausmenetelmä onnistui havaitsemaan eri video stabilointimenetelmien vahvuudet ja heikkoudet

Spectral Characterization of a Prototype SFA Camera for Joint Visible and NIR Acquisition

International audienceMultispectral acquisition improves machine vision since it permits capturing more information on object surface properties than color imaging. The concept of spectral filter arrays has been developed recently and allows multispectral single shot acquisition with a compact camera design. Due to filter manufacturing difficulties, there was, up to recently, no system available for a large span of spectrum, i.e., visible and Near Infra-Red acquisition. This article presents the achievement of a prototype of camera that captures seven visible and one near infra-red bands on the same sensor chip. A calibration is proposed to characterize the sensor, and images are captured. Data are provided as supplementary material for further analysis and simulations. This opens a new range of applications in security, robotics, automotive and medical fields

Digital forensic techniques for the reverse engineering of image acquisition chains

In recent years a number of new methods have been developed to detect image forgery. Most forensic techniques use footprints left on images to predict the history of the images. The images, however, sometimes could have gone through a series of processing and modification through their lifetime. It is therefore difficult to detect image tampering as the footprints could be distorted or removed over a complex chain of operations. In this research we propose digital forensic techniques that allow us to reverse engineer and determine history of images that have gone through chains of image acquisition and reproduction. This thesis presents two different approaches to address the problem. In the first part we propose a novel theoretical framework for the reverse engineering of signal acquisition chains. Based on a simplified chain model, we describe how signals have gone in the chains at different stages using the theory of sampling signals with finite rate of innovation. Under particular conditions, our technique allows to detect whether a given signal has been reacquired through the chain. It also makes possible to predict corresponding important parameters of the chain using acquisition-reconstruction artefacts left on the signal. The second part of the thesis presents our new algorithm for image recapture detection based on edge blurriness. Two overcomplete dictionaries are trained using the K-SVD approach to learn distinctive blurring patterns from sets of single captured and recaptured images. An SVM classifier is then built using dictionary approximation errors and the mean edge spread width from the training images. The algorithm, which requires no user intervention, was tested on a database that included more than 2500 high quality recaptured images. Our results show that our method achieves a performance rate that exceeds 99% for recaptured images and 94% for single captured images.Open Acces