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

MIPI 2023 Challenge on RGBW Remosaic: Methods and Results

Developing and integrating advanced image sensors with novel algorithms in camera systems are prevalent with the increasing demand for computational photography and imaging on mobile platforms. However, the lack of high-quality data for research and the rare opportunity for an in-depth exchange of views from industry and academia constrain the development of mobile intelligent photography and imaging (MIPI). With the success of the 1st MIPI Workshop@ECCV 2022, we introduce the second MIPI challenge, including four tracks focusing on novel image sensors and imaging algorithms. This paper summarizes and reviews the RGBW Joint Remosaic and Denoise track on MIPI 2023. In total, 81 participants were successfully registered, and 4 teams submitted results in the final testing phase. The final results are evaluated using objective metrics, including PSNR, SSIM, LPIPS, and KLD. A detailed description of the top three models developed in this challenge is provided in this paper. More details of this challenge and the link to the dataset can be found at https://mipi-challenge.org/MIPI2023/.Comment: CVPR 2023 Mobile Intelligent Photography and Imaging (MIPI) Workshop--RGBW Sensor Remosaic Challenge Report. Website: https://mipi-challenge.org/MIPI2023/. arXiv admin note: substantial text overlap with arXiv:2209.08471, arXiv:2209.07060, arXiv:2209.07530, arXiv:2304.1008

Robust Joint Image Reconstruction from Color and Monochrome Cameras

International audienceRecent years have seen an explosion of the number of camera modules integratedinto individual consumer mobile devices, including configurations that contain multi-ple different types of image sensors. One popular configuration is to combine an RGBcamera for color imaging with a monochrome camera that has improved performancein low-light settings, as well as some sensitivity in the infrared. In this work we in-troduce a method to combine simultaneously captured images from such a two-camerastereo system to generate a high-quality, noise reduced color image. To do so, pixel-to-pixel alignment has to be constructed between the two captured monochrome and colorimages, which however, is prone to artifacts due to parallax. The joint image recon-struction is made robust by introducing a novel artifact-robust optimization formulation.We provide extensive experimental results based on the two-camera configuration of a commercially available cell phone

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

High Dynamic Range (HDR) Display Perception

Displays have undergone a huge development in the last several decades. From cathode-ray tube (CRT), liquid crystal display (LCD), to organic light-emitting diode (OLED), even Q-OLED, the new configurations of the display bring more and more functions into industry and daily life. In the recent several years, high dynamic range (HDR) displays become popular. HDR displays usually refer to that the black level of the display is darker and the peak being brighter compared with the standard dynamic range (SDR) display. Traditionally, the peak luminance level can be used as the white in characterization and calibration. However, for HDR displays, the peak luminance is higher than the traditional diffuse white level. Exploration of the perceptual diffuse white in HDR image when presented in displays is proposed, which can be beneficial to the characterizing and the optimizing the usage of the HDR display. Moreover, in addition to the ``diffuse white , 3D color gamut volume can be calculated in some specific color appearance models. Calculation and modeling of the 3D color gamut volume can be very useful for display design and better characterizing display color reproduction capability. Furthermore, the perceptional color gamut volume can be measured through psychophysical experiments. Comparison between the perceptional color gamut volume and the theoretical 3D gamut volume calculations will reveal some insights for optimizing the usage of HDR displays. Another advantage of the HDR display is its darker black compared with the SDR display. Compared with the real black object, what level of black is `perfect\u27 enough in displays? Experiments were proposed and conducted to evaluate that if the HDR display is capable of showing ``perfect black for different types of background images/patterns. A glare-based model was proposed to predict the visual ``perfect black. Additionally, the dynamic range of human vision system is very large. However, the simultaneous dynamic range of human vision system is much smaller and is important for the fine tuning usage of HDR displays. The simultaneous dynamic range was measured directly for different stimulus sizes. Also, it was found that the simultaneous dynamic range was peak luminance level dependent. A mathematical model was proposed based on the experimental data to predict the simultaneous dynamic range. Also the spatial frequency effect of the target pattern on the simultaneous dynamic range was measured and modeled. The four different assessments about HDR displays perception would provide experimental data and models for a better understanding of HDR perception and tuning of the HDR display

Extracción y análisis de características para identificación, agrupamiento y modificación de la fuente de imágenes generadas por dispositivos móviles

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Informática, Departamento de Ingeniería del Software e Inteligencia Artificial, leída el 02/10/2017.Nowadays, digital images play an important role in our society. The presence of mobile devices with integrated cameras is growing at an unrelenting pace, resulting in the majority of digital images coming from this kind of device. Technological development not only facilitates the generation of these images, but also the malicious manipulation of them. Therefore, it is of interest to have tools that allow the device that has generated a certain digital image to be identified. The digital image source can be identified through the features that the generating device permeates it with during the creation process. In recent years most research on techniques for identifying the source has focused solely on traditional cameras. The forensic analysis techniques of digital images generated by mobile devices are therefore of particular importance since they have specific characteristics which allow for better results, and forensic techniques for digital images generated by another kind of device are often not valid. This thesis provides various contributions in two of the main research lines of forensic analysis, the field of identification techniques and the counter-forensics or attacks on these techniques. In the field of digital image source acquisition identification techniques, both closed and open scenarios are addressed. In closed scenarios, the images whose acquisition source are to be determined belong to a group of devices known a priori. Meanwhile, an open scenario is one in which the images under analysis belong to a set of devices that is not known a priori by the fo rensic analyst. In this case, the objective is not t he concrete image acquisition source identification, but their classification into groups whose images all belong to the same mobile device. The image clustering t echniques are of particular interest in real situations since in many cases the forensic analyst does not know a priori which devices have generated certain images. Firstly, techniques for identifying the device type (computer, scanner or digital camera of the mobile device) or class (make and model) of the image acquisition source in mobile devices are proposed, which are two relevant branches of forensic analysis of mobile device images. An approach based on different types of image features and Support Vector Machine as a classifier is presented. Secondly, a technique for the ident ification in open scenarios that consists of grouping digital images of mobile devices according to the acquisition source is developed, that is to say, a class-grouping of all input images is performed. The proposal is based on the combination of hierarchical grouping and flat grouping using the Sensor Pattern Noise. Lastly, in the area of att acks on forensic t echniques, topics related to the robustness of the image source identificat ion forensic techniques are addressed. For this, two new algorithms based on the sensor noise and the wavelet transform are designed, one for the destruction of t he image identity and another for its fo rgery. Results obtained by the two algorithms were compared with other tools designed for the same purpose. It is worth mentioning that the solution presented in this work requires less amount and complexity of input data than the tools to which it was compared. Finally, these identification t echniques have been included in a tool for the forensic analysis of digital images of mobile devices called Theia. Among the different branches of forensic analysis, Theia focuses mainly on the trustworthy identification of make and model of the mobile camera that generated a given image. All proposed algorithms have been implemented and integrated in Theia thus strengthening its functionality.Actualmente las imágenes digitales desempeñan un papel importante en nuestra sociedad. La presencia de dispositivos móviles con cámaras fotográficas integradas crece a un ritmo imparable, provocando que la mayoría de las imágenes digitales procedan de este tipo de dispositivos. El desarrollo tecnológico no sólo facilita la generación de estas imágenes, sino también la manipulación malintencionada de éstas. Es de interés, por tanto, contar con herramientas que permitan identificar al dispositivo que ha generado una cierta imagen digital. La fuente de una imagen digital se puede identificar a través de los rasgos que el dispositivo que la genera impregna en ella durante su proceso de creación. La mayoría de las investigaciones realizadas en los últimos años sobre técnicas de identificación de la fuente se han enfocado únicamente en las cámaras tradicionales. Las técnicas de análisis forense de imágenes generadas por dispositivos móviles cobran, pues, especial importancia, ya que éstos presentan características específicas que permiten obtener mejores resultados, no siendo válidas muchas veces además las técnicas forenses para imágenes digitales generadas por otros tipos de dispositivos. La presente Tesis aporta diversas contribuciones en dos de las principales líneas del análisis forense: el campo de las t écnicas de identificación de la fuente de adquisición de imágenes digitales y las contramedidas o at aques a est as técnicas. En el primer campo se abordan tanto los escenarios cerrados como los abiertos. En el escenario denominado cerrado las imágenes cuya fuente de adquisición hay que determinar pertenecen a un grupo de dispositivos conocidos a priori. Por su parte, un escenario abierto es aquel en el que las imágenes pertenecen a un conjunto de dispositivos que no es conocido a priori por el analista forense. En este caso el obj etivo no es la identificación concreta de la fuente de adquisición de las imágenes, sino su clasificación en grupos cuyas imágenes pertenecen todas al mismo dispositivo móvil. Las técnicas de agrupamiento de imágenes son de gran interés en situaciones reales, ya que en muchos casos el analist a forense desconoce a priori cuáles son los dispositivos que generaron las imágenes. En primer lugar se presenta una técnica para la identificación en escenarios cerrados del tipo de dispositivo (computador, escáner o cámara digital de dispositivo móvil) o la marca y modelo de la fuente en dispositivos móviles, que son dos problemáticas relevantes del análisis forense de imágenes digitales. La propuesta muestra un enfoque basado en distintos tipos de características de la imagen y en una clasificación mediante máquinas de soporte vectorial. En segundo lugar se diseña una técnica para la identificación en escenarios abiertos que consiste en el agrupamiento de imágenes digitales de dispositivos móviles según la fuente de adquisición, es decir, se realiza un agrupamiento en clases de todas las imágenes de ent rada. La propuesta combina agrupamiento jerárquico y agrupamiento plano con el uso del patrón de ruido del sensor. Por último, en el área de los ataques a las técnicas fo renses se tratan temas relacionados con la robustez de las técnicas forenses de identificación de la fuente de adquisición de imágenes. Se especifican dos algoritmos basados en el ruido del sensor y en la transformada wavelet ; el primero destruye la identidad de una imagen y el segundo falsifica la misma. Los resultados obtenidos por estos dos algoritmos se comparan con otras herramientas diseñadas para el mismo fin, observándose que la solución aquí presentada requiere de menor cantidad y complejidad de datos de entrada. Finalmente, estas técnicas de identificación han sido incluidas en una herramienta para el análisis forense de imágenes digitales de dispositivos móviles llamada Theia. Entre las diferentes ramas del análisis forense, Theia se centra principalmente en la identificación confiable de la marca y el modelo de la cámara móvil que generó una imagen dada. Todos los algoritmos desarrollados han sido implementados e integrados en Theia, reforzando así su funcionalidad.Depto. de Ingeniería de Software e Inteligencia Artificial (ISIA)Fac. de InformáticaTRUEunpu

Artistic Path Space Editing of Physically Based Light Transport

Die Erzeugung realistischer Bilder ist ein wichtiges Ziel der Computergrafik, mit Anwendungen u.a. in der Spielfilmindustrie, Architektur und Medizin. Die physikalisch basierte Bildsynthese, welche in letzter Zeit anwendungsübergreifend weiten Anklang findet, bedient sich der numerischen Simulation des Lichttransports entlang durch die geometrische Optik vorgegebener Ausbreitungspfade; ein Modell, welches für übliche Szenen ausreicht, Photorealismus zu erzielen. Insgesamt gesehen ist heute das computergestützte Verfassen von Bildern und Animationen mit wohlgestalteter und theoretisch fundierter Schattierung stark vereinfacht. Allerdings ist bei der praktischen Umsetzung auch die Rücksichtnahme auf Details wie die Struktur des Ausgabegeräts wichtig und z.B. das Teilproblem der effizienten physikalisch basierten Bildsynthese in partizipierenden Medien ist noch weit davon entfernt, als gelöst zu gelten. Weiterhin ist die Bildsynthese als Teil eines weiteren Kontextes zu sehen: der effektiven Kommunikation von Ideen und Informationen. Seien es nun Form und Funktion eines Gebäudes, die medizinische Visualisierung einer Computertomografie oder aber die Stimmung einer Filmsequenz -- Botschaften in Form digitaler Bilder sind heutzutage omnipräsent. Leider hat die Verbreitung der -- auf Simulation ausgelegten -- Methodik der physikalisch basierten Bildsynthese generell zu einem Verlust intuitiver, feingestalteter und lokaler künstlerischer Kontrolle des finalen Bildinhalts geführt, welche in vorherigen, weniger strikten Paradigmen vorhanden war. Die Beiträge dieser Dissertation decken unterschiedliche Aspekte der Bildsynthese ab. Dies sind zunächst einmal die grundlegende Subpixel-Bildsynthese sowie effiziente Bildsyntheseverfahren für partizipierende Medien. Im Mittelpunkt der Arbeit stehen jedoch Ansätze zum effektiven visuellen Verständnis der Lichtausbreitung, die eine lokale künstlerische Einflussnahme ermöglichen und gleichzeitig auf globaler Ebene konsistente und glaubwürdige Ergebnisse erzielen. Hierbei ist die Kernidee, Visualisierung und Bearbeitung des Lichts direkt im alle möglichen Lichtpfade einschließenden "Pfadraum" durchzuführen. Dies steht im Gegensatz zu Verfahren nach Stand der Forschung, die entweder im Bildraum arbeiten oder auf bestimmte, isolierte Beleuchtungseffekte wie perfekte Spiegelungen, Schatten oder Kaustiken zugeschnitten sind. Die Erprobung der vorgestellten Verfahren hat gezeigt, dass mit ihnen real existierende Probleme der Bilderzeugung für Filmproduktionen gelöst werden können

Novel image processing algorithms and methods for improving their robustness and operational performance

Image processing algorithms have developed rapidly in recent years. Imaging functions are becoming more common in electronic devices, demanding better image quality, and more robust image capture in challenging conditions. Increasingly more complicated algorithms are being developed in order to achieve better signal to noise characteristics, more accurate colours, and wider dynamic range, in order to approach the human visual system performance levels. [Continues.

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