Universal Demosaicking of Color Filter Arrays

A large number of color filter arrays (CFAs), periodic or aperiodic, have been proposed. To reconstruct images from all different CFAs and compare their imaging quality, a universal demosaicking method is needed. This paper proposes a new universal demosaicking method based on inter-pixel chrominance capture and optimal demosaicking transformation. It skips the commonly used step to estimate the luminance component at each pixel, and thus, avoids the associated estimation error. Instead, we directly use the acquired CFA color intensity at each pixel as an input component. Two independent chrominance components are estimated at each pixel based on the interpixel chrominance in the window, which is captured with the difference of CFA color values between the pixel of interest and its neighbors. Two mechanisms are employed for the accurate estimation: distance-related and edge-sensing weighting to reflect the confidence levels of the inter-pixel chrominance components, and pseudoinverse-based estimation from the components in a window. Then from the acquired CFA color component and two estimated chrominance components, the three primary colors are reconstructed by a linear color transform, which is optimized for the least transform error. Our experiments show that the proposed method is much better than other published universal demosaicking methods.National Key Basic Research Project of China (973 Program) [2015CB352303, 2011CB302400]; National Natural Science Foundation (NSF) of China [61071156, 61671027]SCI(E)[email protected]; [email protected]; [email protected]; [email protected]

Model-based demosaicking for acquisitions by a RGBW color filter array

Microsatellites and drones are often equipped with digital cameras whose sensing system is based on color filter arrays (CFAs), which define a pattern of color filter overlaid over the focal plane. Recent commercial cameras have started implementing RGBW patterns, which include some filters with a wideband spectral response together with the more classical RGB ones. This allows for additional light energy to be captured by the relevant pixels and increases the overall SNR of the acquisition. Demosaicking defines reconstructing a multi-spectral image from the raw image and recovering the full color components for all pixels. However, this operation is often tailored for the most widespread patterns, such as the Bayer pattern. Consequently, less common patterns that are still employed in commercial cameras are often neglected. In this work, we present a generalized framework to represent the image formation model of such cameras. This model is then exploited by our proposed demosaicking algorithm to reconstruct the datacube of interest with a Bayesian approach, using a total variation regularizer as prior. Some preliminary experimental results are also presented, which apply to the reconstruction of acquisitions of various RGBW cameras

Twice Binnable Color Filter Arrays

Pixel binning enables high speed, low power readout in low resolution modes, and more importantly, a reduction of read noise via floating diffusion binning. New, high resolution CMOS image sensors for mobile phones have moved beyond the once-binnable Quad Bayer and RGBW-Kodak patterns to the twice binnable Hexadeca Bayer pattern featuring 4x4 tiles of like colored pixels.Pixel binning enables high speed, low power readout in low resolution modes, and more importantly, a reduction of read noise via floating diffusion binning. New, high resolution CMOS image sensors for mobile phones have moved beyond the once-binnable Quad Bayer and RGBW-Kodak patterns to the twice binnable Hexadeca Bayer pattern featuring 4x4 tiles of like colored pixels. In this paper we present the non-intuitive result that Nona and Hexadeca Bayer can be superior to Quad Bayer in demosaicking quality due to degeneracies in the latter's spectrum. Hexadeca Bayer, nevertheless, suffers from the weakness of generating Quad Bayer after one round of binning. We present a novel twice binnable RGBW CFA, composed of 2x2 tiles capable of 4:1 floating diffusion binning, that is free from spectral degeneracies and thus demosaick well in full resolution and both binned modes. It also has a 4 dB low light SNR advantage over Quad and Hexadeca Bayer in the full resolution mode, and 6 dB SNR advantage in both the binned modes.Comment: 5 pages, 7 figures. Initial versio

Demosaicking di immagini a colori: tecniche, analisi delle prestazioni, campionamento

Nelle fotocamere digitali l’immagine viene raccolta tramite matrici di fotorivelatori, a cui solitamente sono anteposti dei filtri, chiamati Color Filter Array(CFA), il vantaggio derivante dal loro utilizzo è che non sono necessarie tre diverse matrici di sensori (sensori di Foveon) ma solamente una, come controparte l’immagine appena catturata è, dal punto di vista cromatico, solo una parte di quella totale, proprio come se fosse un mosaico. Da qui nasce la necessità di ricostruzione o demosaicizzazione (demosaicking) dell’immagine originale per ottenere le tre componenti di colore. In questa trattazione verranno affrontate le principali tecniche di demosaicking e tra loro verranno comparate. A seguire verranno descritti i metodi di stima delle prestazioni per algoritmi di demosaicking sia a partire da un immagine di riferimento sia senza di questa(No-reference). Infine verranno trattate alcune tecniche che consentono di passare in modo rapido ma efficiente da immagini ottenute da CFA di Bayer ad immagini ricostruite e campionate aventi un numero di pixel inferiore (Downscaling

Recent Advances in Image Restoration with Applications to Real World Problems

In the past few decades, imaging hardware has improved tremendously in terms of resolution, making widespread usage of images in many diverse applications on Earth and planetary missions. However, practical issues associated with image acquisition are still affecting image quality. Some of these issues such as blurring, measurement noise, mosaicing artifacts, low spatial or spectral resolution, etc. can seriously affect the accuracy of the aforementioned applications. This book intends to provide the reader with a glimpse of the latest developments and recent advances in image restoration, which includes image super-resolution, image fusion to enhance spatial, spectral resolution, and temporal resolutions, and the generation of synthetic images using deep learning techniques. Some practical applications are also included

Aplicación de la técnica SVM en el análisis forense de imágenes de dispositivos móviles

Uno de los problemas a tratar dentro del análisis forense digital es la identificación de la cámara que se ha usado para obtener una determinada imagen. Debido al aumento en el uso de teléfonos móviles con cámara integrada durante los últimos años, el trabajo está orientado a este tipo de dispositivos, proporcionando al investigador forense una herramienta específica para el análisis de este tipo de imágenes. En este trabajo se analiza la información EXIF contenida en un archivo de imagen y se comprueba porque no es una fuente fiable a la hora de obtener la marca y modelo de la cámara. Por eso desarrollamos un algoritmo que utiliza la información de los píxeles de la imagen. Está basado en las diferencias existentes en los métodos de procesamiento de la imagen que utilizan los distintos fabricantes, como el algoritmo de interpolación del color que tiene lugar en la matriz CFA del sensor, la corrección gamma o la corrección de puntos blancos. Estas diferencias originan un conjunto de huellas en la imagen que nos permitirían diferenciar la marca y modelo de la camara fuente. Para obtener estas huellas, extraemos un conjunto de características de las imagenes, entre las que se encuentran características de color, métricas de la calidad de la imagen y estadísticas wavelet. Para realizar la predicción de la marca y modelo de la camara utilizamos un clasificador SVM. Siguiendo un procedimiento análogo, se desarrolla otro algoritmo que permite saber si una imagen procede de una cámara o de un escáner. Finalmente, llevamos a cabo un conjunto de experimentos que demuestran la efectividad del algoritmo implementado. [ABSTRACT] One of the issues involved in digital forensics is the identification of the camera used to obtain a particular image. Due to the increase use of mobile phones with integrated camera in recent years, this work is aimed at this type of devices, providing to forensic investigator a specific tool for the analysis of images taken with mobile phones. In this work we analyze the EXIF information contained in an image file and we explain because it is not a reliable source if we want to extract the make and model of the camera from it. So we develop an algorithm that uses information from the pixels of the image. It is based on dferences in the image processing methods used by diferent manufacturers, such as color interpolation algorithm which takes place in the color filter array, gamma correction or white point correction. These diferences originate a set of footprints in the image that allow us to diferentiate the brand and model of the source camera. To obtain these footprints, we extract a feature set from the images, among which are color characteristics, image quality metrics and wavelet statistics. To predict the make and the model of camera we use an SVM classifier. Following a similar procedure, we develop another algorithm that let us know if an image comes from a camera or a scanner. Finally, we perform a series of experiments to prove the efectiveness of the implemented algorithm

Formation d'image : estimation du champ lumineux et matrice de filtres couleurs

Dans ce mémoire de maîtrise de type recherche, nous discutons essentiellement de la formation d’image. Dans un premier chapitre, nous décrivons les modèles classiques de formation d'image. Nous commençons par une description de la lumière voyageant dans la scène pour arriver à l'image formée sur le capteur. Ensuite, nous critiquons ces modèles sur le fait que le capteur entraine une perte d’informations sur la structure de la scène. De plus, pour un point donné du plan image, tous les rayons provenant de la scène ne sont pas pris en compte pour la formation d'une image. Nous essayons alors de combler ces défauts en introduisant la notion de champ lumineux dans le deuxième chapitre. Nous décrivons le modèle des champs lumineux d'une scène. Ce dernier permet alors d'estimer le champ lumineux à partir d'une image à l'aide de deux méthodes : la méthode des moindres carrés et une méthode variationnelle. Celles-ci sont présentées dans le troisième chapitre. Enfin, dans un quatrième chapitre, nous abordons un autre aspect de la formation d'image. En effet, nous travaillons sur une nouvelle matrice de filtres couleurs (color filter arrays, CFA) que nous nommons CFA de Burtoni. Dans ce chapitre, nous comparons, selon une mesure d'aliasing et de résolution, ce CFA avec d'autres CFAs existant dans la littérature, sans faire appel au démosaïquage. Afin d'effectuer ces comparaisons, nous introduisons également des classes d'images correspondantes à différents contenus comme les textures, les zoneshomogènes et les lignes

Passive Techniques for Detecting and Locating Manipulations in Digital Images

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Informática, leída el 19-11-2020El numero de camaras digitales integradas en dispositivos moviles as como su uso en la vida cotidiana esta en continuo crecimiento. Diariamente gran cantidad de imagenes digitales, generadas o no por este tipo de dispositivos, circulan en Internet o son utilizadas como evidencias o pruebas en procesos judiciales. Como consecuencia, el analisis forense de imagenes digitales cobra importancia en multitud de situaciones de la vida real. El analisis forense de imagenes digitales se divide en dos grandes ramas: autenticidad de imagenes digitales e identificacion de la fuente de adquisicion de una imagen. La primera trata de discernir si una imagen ha sufrido algun procesamiento posterior al de su creacion, es decir, que no haya sido manipulada. La segunda pretende identificar el dispositivo que genero la imagen digital. La verificacion de la autenticidad de imagenes digitales se puedellevar a cabo mediante tecnicas activas y tecnicas pasivas de analisis forense. Las tecnicas activas se fundamentan en que las imagenes digitales cuentan con \marcas" presentes desde su creacion, de forma que cualquier tipo de alteracion que se realice con posterioridad a su generacion, modificara las mismas, y, por tanto, permitiran detectar si ha existido un posible post-proceso o manipulacion...The number of digital cameras integrated into mobile devices as well as their use in everyday life is continuously growing. Every day a large number of digital images, whether generated by this type of device or not, circulate on the Internet or are used as evidence in legal proceedings. Consequently, the forensic analysis of digital images becomes important in many real-life situations. Forensic analysis of digital images is divided into two main branches: authenticity of digital images and identi cation of the source of acquisition of an image. The first attempts to discern whether an image has undergone any processing subsequent to its creation, i.e. that it has not been manipulated. The second aims to identify the device that generated the digital image. Verification of the authenticity of digital images can be carried out using both active and passive forensic analysis techniques. The active techniques are based on the fact that the digital images have "marks"present since their creation so that any type of alteration made after their generation will modify them, and therefore will allow detection if there has been any possible post-processing or manipulation. On the other hand, passive techniques perform the analysis of authenticity by extracting characteristics from the image...Fac. de InformáticaTRUEunpu