Color Filter Array Demosaicking Using High-Order Interpolation Techniques With a Weighted Median Filter for Sharp Color Edge Preservation

Demosaicking is an estimation process to determine missing color values when a single-sensor digital camera is used for color image capture. In this paper, we propose a number of new methods based on the application of Taylor series and cubic spline interpolation for color filter array demosaicking. To avoid the blurring of an edge, interpolants are first estimated in four opposite directions so that no interpolation is carried out across an edge. A weighted median filter, whose filter coefficients are determined by a classifier based on an edge orientation map, is then used to produce an output from the four interpolants to preserve edges. Using the proposed methods, the original color can be faithfully reproduced with minimal amount of color artifacts even at edges

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

Sensor Signal and Information Processing II

In the current age of information explosion, newly invented technological sensors and software are now tightly integrated with our everyday lives. Many sensor processing algorithms have incorporated some forms of computational intelligence as part of their core framework in problem solving. These algorithms have the capacity to generalize and discover knowledge for themselves and learn new information whenever unseen data are captured. The primary aim of sensor processing is to develop techniques to interpret, understand, and act on information contained in the data. The interest of this book is in developing intelligent signal processing in order to pave the way for smart sensors. This involves mathematical advancement of nonlinear signal processing theory and its applications that extend far beyond traditional techniques. It bridges the boundary between theory and application, developing novel theoretically inspired methodologies targeting both longstanding and emergent signal processing applications. The topic ranges from phishing detection to integration of terrestrial laser scanning, and from fault diagnosis to bio-inspiring filtering. The book will appeal to established practitioners, along with researchers and students in the emerging field of smart sensors processing

Integrating colour correction algorithms

Digital cameras sense colour different than the human visual system (HVS). Digital cameras sense colour using imaging sensor, whereas the HVS senses colour using the cone photoreceptors in our retina. Each digital camera model has its own device specific spectral sensitivity function. It is therefore necessary to convert the device specific colour responses of an imaging sensor to values that are related to the HVS. This process is typically referred to as colour correction, and it is common to the image processing pipeline across all cameras. In this thesis, we explore the topic of colour correction for digital cameras. Colour correction algorithms establish the mapping between device specific responses of the camera with HVS related colour responses. Colour correction algorithms typically need to be trained with datasets. During the training process, we adjust the parameters of the colour correction algorithm, in order to minimise the fitting error between the device specific responses and the corresponding HVS responses. In this thesis, we first show that the choice of the training dataset affects the performance of the colour correction algorithm. Then, we propose to circumvent this problem by considering a reflectance dataset as a set of samples of a much larger reflectance space. We approximate the convex closure of the reflectance dataset in the reflectance space using a hypercube. Finally we integrate over this hypercube in order to calculate a matrix for linear colour correction. By computing the linear colour correction matrix this way, we are able to fill in the gap within a reflectance dataset. We then expand upon the idea of reflectance space further, by allowing all possible reflectances. We explore an alternative formulation of Maximum Ignorance with Positivity (MIP) colour correction. Our alternative formulation allows us to develop a polynomial variant of the concept. Polynomial MIP colour correction is far more complex thant MIP colour correction in terms of formulation. Our contribution is theoretically interesting, however practically, it delivers poorer performance

Characterisation of a multispectral digital camera System for quantitatively comparing complex animal Patterns in natural environments.

Animal coloration can be described by complex colour patterns including elements of varying size, shape and spectral profile which commonly reflect energy outside the spectral range visible for humans. Whilst spectrometry is currently employed for the quantitative study of animal coloration, it is limited on its ability to describe the spatial characteristics of spectral differences in patterns. Digital photography has recently been used as a tool for measuring spatial and spectral properties of patterns based on quantitative analysis of linear camera responses recovered after characterising the device. However current applications of digital imaging for studying animal coloration are limited to image recording within a laboratory environment considering controlled lighting conditions. Here a refined methodology for camera characterisation is developed permitting the recording of images under different illumination conditions typical of natural environments. The characterised camera system thus allows recording images from reflected ultraviolet and visible radiation resulting in a multispectral digital camera system. Furthermore a standardised imaging processing workflow was developed based on specific characteristics of the camera thus making possible an objective comparison from images. An application of the characterised camera system is exemplified in the study of animal colour patterns adapted for camouflage using as a model two Australian, endemic lizard species. The interaction between the spectral and spatial properties of the respective lizards produces complex patterns than cannot be interpreted by spectrophotometry alone. Data obtained from analysis of images recorded with the characterised camera system in the visible and near-ultraviolet region of the spectrum reveal significative differences between sex and species and a possible interaction between sex and species, suggesting microhabitat specialisation to different backgrounds

Propuesta de arquitectura y circuitos para la mejora del rango dinámico de sistemas de visión en un chip diseñados en tecnologías CMOS profundamente submicrométrica

El trabajo presentado en esta tesis trata de proponer nuevas técnicas para la expansión del rango dinámico en sensores electrónicos de imagen. En este caso, hemos dirigido nuestros estudios hacia la posibilidad de proveer dicha funcionalidad en un solo chip. Esto es, sin necesitar ningún soporte externo de hardware o software, formando un tipo de sistema denominado Sistema de Visión en un Chip (VSoC). El rango dinámico de los sensores electrónicos de imagen se define como el cociente entre la máxima y la mínima iluminación medible. Para mejorar este factor surgen dos opciones. La primera, reducir la mínima luz medible mediante la disminución del ruido en el sensor de imagen. La segunda, incrementar la máxima luz medible mediante la extensión del límite de saturación del sensor. Cronológicamente, nuestra primera opción para mejorar el rango dinámico se basó en reducir el ruido. Varias opciones se pueden tomar para mejorar la figura de mérito de ruido del sistema: reducir el ruido usando una tecnología CIS o usar circuitos dedicados, tales como calibración o auto cero. Sin embargo, el uso de técnicas de circuitos implica limitaciones, las cuales sólo pueden ser resueltas mediante el uso de tecnologías no estándar que están especialmente diseñadas para este propósito. La tecnología CIS utilizada está dirigida a la mejora de la calidad y las posibilidades del proceso de fotosensado, tales como sensibilidad, ruido, permitir imagen a color, etcétera. Para estudiar las características de la tecnología en más detalle, se diseñó un chip de test, lo cual permite extraer las mejores opciones para futuros píxeles. No obstante, a pesar de un satisfactorio comportamiento general, las medidas referentes al rango dinámico indicaron que la mejora de este mediante sólo tecnología CIS es muy limitada. Es decir, la mejora de la corriente oscura del sensor no es suficiente para nuestro propósito. Para una mayor mejora del rango dinámico se deben incluir circuitos dentro del píxel. No obstante, las tecnologías CIS usualmente no permiten nada más que transistores NMOS al lado del fotosensor, lo cual implica una seria restricción en el circuito a usar. Como resultado, el diseño de un sensor de imagen con mejora del rango dinámico en tecnologías CIS fue desestimado en favor del uso de una tecnología estándar, la cual da más flexibilidad al diseño del píxel. En tecnologías estándar, es posible introducir una alta funcionalidad usando circuitos dentro del píxel, lo cual permite técnicas avanzadas para extender el límite de saturación de los sensores de imagen. Para este objetivo surgen dos opciones: adquisición lineal o compresiva. Si se realiza una adquisición lineal, se generarán una gran cantidad de datos por cada píxel. Como ejemplo, si el rango dinámico de la escena es de 120dB al menos se necesitarían 20-bits/píxel, log2(10120/20)=19.93, para la representación binaria de este rango dinámico. Esto necesitaría de amplios recursos para procesar esta gran cantidad de datos, y un gran ancho de banda para moverlos al circuito de procesamiento. Para evitar estos problemas, los sensores de imagen de alto rango dinámico usualmente optan por utilizar una adquisición compresiva de la luz. Por lo tanto, esto implica dos tareas a realizar: la captura y la compresión de la imagen. La captura de la imagen se realiza a nivel de píxel, en el dispositivo fotosensor, mientras que la compresión de la imagen puede ser realizada a nivel de píxel, de sistema, o mediante postprocesado externo. Usando el postprocesado, existe un campo de investigación que estudia la compresión de escenas de alto rango dinámico mientras se mantienen los detalles, produciendo un resultado apropiado para la percepción humana en monitores convencionales de bajo rango dinámico. Esto se denomina Mapeo de Tonos (Tone Mapping) y usualmente emplea solo 8-bits/píxel para las representaciones de imágenes, ya que éste es el estándar para las imágenes de bajo rango dinámico. Los píxeles de adquisición compresiva, por su parte, realizan una compresión que no es dependiente de la escena de alto rango dinámico a capturar, lo cual implica una baja compresión o pérdida de detalles y contraste. Para evitar estas desventajas, en este trabajo, se presenta un píxel de adquisición compresiva que aplica una técnica de mapeo de tonos que permite la captura de imágenes ya comprimidas de una forma optimizada para mantener los detalles y el contraste, produciendo una cantidad muy reducida de datos. Las técnicas de mapeo de tonos ejecutan normalmente postprocesamiento mediante software en un ordenador sobre imágenes capturadas sin compresión, las cuales contienen una gran cantidad de datos. Estas técnicas han pertenecido tradicionalmente al campo de los gráficos por ordenador debido a la gran cantidad de esfuerzo computacional que requieren. Sin embargo, hemos desarrollado un nuevo algoritmo de mapeo de tonos especialmente adaptado para aprovechar los circuitos dentro del píxel y que requiere un reducido esfuerzo de computación fuera de la matriz de píxeles, lo cual permite el desarrollo de un sistema de visión en un solo chip. El nuevo algoritmo de mapeo de tonos, el cual es un concepto matemático que puede ser simulado mediante software, se ha implementado también en un chip. Sin embargo, para esta implementación hardware en un chip son necesarias algunas adaptaciones y técnicas avanzadas de diseño, que constituyen en sí mismas otra de las contribuciones de este trabajo. Más aún, debido a la nueva funcionalidad, se han desarrollado modificaciones de los típicos métodos a usar para la caracterización y captura de imágenes

Scene-Dependency of Spatial Image Quality Metrics

This thesis is concerned with the measurement of spatial imaging performance and the modelling of spatial image quality in digital capturing systems. Spatial imaging performance and image quality relate to the objective and subjective reproduction of luminance contrast signals by the system, respectively; they are critical to overall perceived image quality. The Modulation Transfer Function (MTF) and Noise Power Spectrum (NPS) describe the signal (contrast) transfer and noise characteristics of a system, respectively, with respect to spatial frequency. They are both, strictly speaking, only applicable to linear systems since they are founded upon linear system theory. Many contemporary capture systems use adaptive image signal processing, such as denoising and sharpening, to optimise output image quality. These non-linear processes change their behaviour according to characteristics of the input signal (i.e. the scene being captured). This behaviour renders system performance “scene-dependent” and difficult to measure accurately. The MTF and NPS are traditionally measured from test charts containing suitable predefined signals (e.g. edges, sinusoidal exposures, noise or uniform luminance patches). These signals trigger adaptive processes at uncharacteristic levels since they are unrepresentative of natural scene content. Thus, for systems using adaptive processes, the resultant MTFs and NPSs are not representative of performance “in the field” (i.e. capturing real scenes). Spatial image quality metrics for capturing systems aim to predict the relationship between MTF and NPS measurements and subjective ratings of image quality. They cascade both measures with contrast sensitivity functions that describe human visual sensitivity with respect to spatial frequency. The most recent metrics designed for adaptive systems use MTFs measured using the dead leaves test chart that is more representative of natural scene content than the abovementioned test charts. This marks a step toward modelling image quality with respect to real scene signals. This thesis presents novel scene-and-process-dependent MTFs (SPD-MTF) and NPSs (SPDNPS). They are measured from imaged pictorial scene (or dead leaves target) signals to account for system scene-dependency. Further, a number of spatial image quality metrics are revised to account for capture system and visual scene-dependency. Their MTF and NPS parameters were substituted for SPD-MTFs and SPD-NPSs. Likewise, their standard visual functions were substituted for contextual detection (cCSF) or discrimination (cVPF) functions. In addition, two novel spatial image quality metrics are presented (the log Noise Equivalent Quanta (NEQ) and Visual log NEQ) that implement SPD-MTFs and SPD-NPSs. The metrics, SPD-MTFs and SPD-NPSs were validated by analysing measurements from simulated image capture pipelines that applied either linear or adaptive image signal processing. The SPD-NPS measures displayed little evidence of measurement error, and the metrics performed most accurately when they used SPD-NPSs measured from images of scenes. The benefit of deriving SPD-MTFs from images of scenes was traded-off, however, against measurement bias. Most metrics performed most accurately with SPD-MTFs derived from dead leaves signals. Implementing the cCSF or cVPF did not increase metric accuracy. The log NEQ and Visual log NEQ metrics proposed in this thesis were highly competitive, outperforming metrics of the same genre. They were also more consistent than the IEEE P1858 Camera Phone Image Quality (CPIQ) metric when their input parameters were modified. The advantages and limitations of all performance measures and metrics were discussed, as well as their practical implementation and relevant applications