Real-world radiance values can range over eight orders of magnitude from starlight to direct sunlight but few digital cameras capture more than three orders in a single Low Dynamic Range (LDR) image. We approach this problem using established High Dynamic Range (HDR) techniques in which multiple images are captured with different exposure times so that all portions of the scene are correctly exposed at least once. These images are then combined to create an HDR image capturing the full range of the scene. HDR capture introduces new challenges; movement in the scene creates faded copies of moving objects, referred to as ghosts. Many techniques have been introduced to handle ghosting, but typically they either address specific types of ghosting, or are computationally very expensive. We address ghosting by first detecting moving objects, then reducing their contribution to the final composite on a frame-by-frame basis. The detection of motion is addressed by performing change detection on exposure-normalized images. Additional special cases are developed based on a priori knowledge of the changing exposures; for example, if exposure is increasing every shot, then any decrease in intensity in the LDR images is a strong indicator of motion. Recent Superpixel over-segmentation techniques are used to refine the detection. We also propose a novel solution for areas that see motion throughout the capture, such as foliage blowing in the wind. Such areas are detected as always moving, and are replaced with information from a single input image, and the replacement of corrupted regions can be tailored to the scenario. We present our approach in the context of a panoramic tele-presence system. Tele-presence systems allow a user to experience a remote environment, aiming to create a realistic sense of "being there" and such a system should therefore provide a high quality visual rendition of the environment. Furthermore, panoramas, by virtue of capturing a greater proportion of a real-world scene, are often exposed to a greater dynamic range than standard photographs. Both facets of this system therefore stand to benefit from HDR imaging techniques. We demonstrate the success of our approach on multiple challenging ghosting scenarios, and compare our results with state-of-the-art methods previously proposed. We also demonstrate computational savings over these methods.

Electronic imaging technologies are faced with the challenge of power consumption when transmitting large amounts of image data from the acquisition imager to the display or processing devices. This is especially a concern for portable applications, and becomes more prominent in increasingly high-resolution, high-frame rate imagers. Therefore, new sampling techniques are needed to minimize transmitted data, while maximizing the conveyed image information. <br /><br /> From this point of view, two approaches have been proposed and implemented in this thesis: <ol> <li> A system-level approach, in which the classical 1D row sampling CMOS imager is modified to a 2D ring sampling pyramidal architecture, using the same standard three transistor (3T) active pixel sensor (APS). </li> <li> A device-level approach, in which the classical orthogonal architecture has been preserved while altering the APS device structure, to design an expandable multiresolution image sensor. </li> </ol> A new scanning scheme has been suggested for the pyramidal image sensor, resulting in an intrascene foveated dynamic range (FDR) similar in profile to that of the human eye. In this scheme, the inner rings of the imager have a higher dynamic range than the outer rings. The pyramidal imager transmits the sampled image through 8 parallel output channels, allowing higher frame rates. The human eye is known to have less sensitivity to oblique contrast. Using this fact on the typical oblique distribution of fixed pattern noise, we demonstrate lower perception of this noise than the orthogonal FPN distribution of classical CMOS imagers. <br /><br /> The multiresolution image sensor principle is based on averaging regions of low interest from frame-sampled image kernels. One pixel is read from each kernel while keeping pixels in the region of interest at their high resolution. This significantly reduces the transferred data and increases the frame rate. Such architecture allows for programmability and expandability of multiresolution imaging applications.

Electronic imaging technologies are faced with the challenge of power consumption when transmitting large amounts of image data from the acquisition imager to the display or processing devices. This is especially a concern for portable applications, and becomes more prominent in increasingly high-resolution, high-frame rate imagers. Therefore, new sampling techniques are needed to minimize transmitted data, while maximizing the conveyed image information. <br /><br /> From this point of view, two approaches have been proposed and implemented in this thesis: <ol> <li> A system-level approach, in which the classical 1D row sampling CMOS imager is modified to a 2D ring sampling pyramidal architecture, using the same standard three transistor (3T) active pixel sensor (APS). </li> <li> A device-level approach, in which the classical orthogonal architecture has been preserved while altering the APS device structure, to design an expandable multiresolution image sensor. </li> </ol> A new scanning scheme has been suggested for the pyramidal image sensor, resulting in an intrascene foveated dynamic range (FDR) similar in profile to that of the human eye. In this scheme, the inner rings of the imager have a higher dynamic range than the outer rings. The pyramidal imager transmits the sampled image through 8 parallel output channels, allowing higher frame rates. The human eye is known to have less sensitivity to oblique contrast. Using this fact on the typical oblique distribution of fixed pattern noise, we demonstrate lower perception of this noise than the orthogonal FPN distribution of classical CMOS imagers. <br /><br /> The multiresolution image sensor principle is based on averaging regions of low interest from frame-sampled image kernels. One pixel is read from each kernel while keeping pixels in the region of interest at their high resolution. This significantly reduces the transferred data and increases the frame rate. Such architecture allows for programmability and expandability of multiresolution imaging applications.

Today&amp / #8217 / s analog modulators and upconverters are inadequate to synthesize and modulate signals with high &amp / #8216 / Spurious Free Dynamic Range&amp / #8217 / (SFDR). Thus, the main objective of this thesis is to design and implement a &amp / #8216 / Digital Signal Synthesizer&amp / #8217 / (DSS) that is capable of synthesizing signals between 50-100 MHz with 60dB SFDR and to modulate them variable symbol rates and modulation techniques with very high phase/frequency resolution and switching speed while keeping the amplitude modulation occurring during a modulated symbol duration as small as possible. In this thesis, digital words of the desired signals are first synthesized in a &amp / #8216 / Field Programmable Gate Array&amp / #8217 / (FPGA) using &amp / #8216 / Direct Digital Synthesizer&amp / #8217 / (DDS) fundamentals and then converted to analog signals with a high speed &amp / #8216 / Digital to Analog Converter&amp / #8217 / (DAC). In order to attain the analog requirements, the system variables such as DAC analog performance, nonlinearities, sample and hold affects, DDS parameters, system clock, bandwidth requirements of analog filters and how they effect the output performance are studied. FPGA blocks that are capable of modulating and synthesizing desired signals are designed and programmed on a FPGA. Finally, single tone and modulated signals are synthesized with this DSS implementation and measured in order to verify this system&amp / #8217 / s performance and capabilities.

In this thesis, image dynamic range enhancement methods are studied in order to solve the problem of representing high dynamic range scenes with low dynamic range images. For this purpose, two main image dynamic range enhancement methods, which are high dynamic range imaging and exposure fusion, are studied. More detailed analysis of exposure fusion algorithms are carried out because the whole enhancement process in the exposure fusion is performed in low dynamic range, and they do not need any prior information about input images. In order to evaluate the performances of exposure fusion algorithms, both objective and subjective quality metrics are used. Moreover, the correlation between the objective quality metrics and subjective ratings is studied in the experiments.

This work is a first step towards a long-term goal of understanding computations occurring in the brain and using those principles to make more efficient machines. The traditional computing paradigm calls for using digital supercomputers to simulate large scale brain-like neural networks resulting in large power consumption which limits scalability or model detail. For example, IBM's digital simulation of a cat brain with simplistic neurons and synapses consumes power equivalent to that of a thousand houses! Instead of digital methods, this work uses analog processing concepts to develop scalable, low-power silicon models of neurons which have been shown to be around ten thousand times more power efficient. This has been achieved by modeling the dynamical behavior of Hodgkin-Huxley (H-H) or Morris-Lecar type equations instead of modeling the exact equations themselves. In particular, the two silicon neuron designs described exhibit a Hopf and a saddle-node bifurcation. Conditions for the bifurcations allow the identification of correct biasing regimes for the neurons. Also, since the hardware neurons compute in real time, they can be used for dynamic clamp protocols in addition to computational experiments. To empower this analog implementation with the flexibility of a digital simulation, a family of field programmable analog array (FPAA) architectures have been developed in 0.35 um CMOS that provide reconfigurability in the network of neurons as well as tunability of individual neuron parameters. This programmability is obtained using floating-gate (FG) transistors. The neurons are organized in blocks called computational analog blocks (CAB) which are embedded in a programmable switch matrix. An unique feature of the architecture is that the switches, being FG elements, can be used also for computation leading to more than 50,000 analog parameters in 9 sq. mm. Several neural systems including central pattern generators and coincidence detectors are demonstrated. Also, a separate chip that is capable of implementing signal processing algorithms has been designed by modifying the CAB elements to include transconductors, multipliers etc. Several systems including an AM demodulator and a speech processor are presented. An important contribution of this work is developing an architecture for programming the FG elements over a wide dynamic range of currents. An adaptive logarithmic transimpedance amplifier is used for this purpose. This design provides a general solution for wide dynamic range current measurement with a low power dissipation and has been used in imaging chips too. A new generation of integrated circuits have also been designed that are 25 sq. mm in area and contain several new features including adaptive synapses and support for smart sensors. These designs and the previous ones should allow prototyping and rapid development of several neurally inspired systems and pave the path for the design of larger and more complex brain like adaptive neural networks.

Diese Arbeit befasst sich mit dem Entwurf eines asynchron arbeitenden, zeitbasierten CMOS–Bildsensors mit erhöhtem Dynamikbereich und Unterdrückung zeitlich redundanter Daten. Aufgrund immer kleinerer Strukturgrößen in modernen Prozessen zur Fertigung von Halbleitern und einer gleichzeitig physikalisch bedingt immer geringeren Skalierbarkeit konventioneller Bildsensoren wird es zunehmend möglich und praktikabel, Signalverarbeitungsansätze auf Pixelebene zu implementieren. Unter Berücksichtigung dieser Entwicklungen befasst sich die folgende Arbeit mit dem Entwurf eines neuartigen CMOS–Bildsensors mit nahezu vollständiger Unterdrückung zeitlich redundanter Daten auf Pixelebene. Jedes photosensitive Element in der Matrix arbeitet dabei vollkommen autonom. Es detektiert selbständig Änderungen in der Bestrahlung und gibt den Absolutwert nur beim Auftreten einer solchen Änderung mittels asynchroner Signalisierung nach außen. Darüber hinaus zeichnet sich der entwickelte Bildaufnehmer durch einen, gegenüber herkömmlichen Bildsensoren, deutlich erhöhten Dynamikbereich und eine niedrige Energieaufnahme aus, wodurch das Prinzip besonders für die Verwendung in Systemen für den mobilen Einsatz oder zur Durchführung von Überwachungsaufgaben geeignet ist. Die Realisierbarkeit des Konzepts wurde durch die erfolgreiche Implementierung eines entsprechenden Bildaufnehmers in einem Standard–CMOS–Prozess nachgewiesen. Durch die Größe des Designs von 304 x 240 Bildelementen, die den Umfang üblicher Prototypen-Realisierungen deutlich übersteigt, konnte speziell die Anwendbarkeit im Bereich größerer Sensorfelder gezeigt werden. Der Schaltkreis wurde erfolgreich getestet, wobei sowohl das Gesamtsystem als auch einzelne Schaltungsteile messtechnisch analysiert worden sind. Die nachgewiesene Bildqualität deckt sich dabei in guter Näherung mit den theoretischen Vorbetrachtungen.

Real-world radiance values can range over eight orders of magnitude from starlight to direct sunlight but few digital cameras capture more than three orders in a single Low Dynamic Range (LDR) image. We approach this problem using established High Dynamic Range (HDR) techniques in which multiple images are captured with different exposure times so that all portions of the scene are correctly exposed at least once. These images are then combined to create an HDR image capturing the full range of the scene. HDR capture introduces new challenges; movement in the scene creates faded copies of moving objects, referred to as ghosts. Many techniques have been introduced to handle ghosting, but typically they either address specific types of ghosting, or are computationally very expensive. We address ghosting by first detecting moving objects, then reducing their contribution to the final composite on a frame-by-frame basis. The detection of motion is addressed by performing change detection on exposure-normalized images. Additional special cases are developed based on a priori knowledge of the changing exposures; for example, if exposure is increasing every shot, then any decrease in intensity in the LDR images is a strong indicator of motion. Recent Superpixel over-segmentation techniques are used to refine the detection. We also propose a novel solution for areas that see motion throughout the capture, such as foliage blowing in the wind. Such areas are detected as always moving, and are replaced with information from a single input image, and the replacement of corrupted regions can be tailored to the scenario. We present our approach in the context of a panoramic tele-presence system. Tele-presence systems allow a user to experience a remote environment, aiming to create a realistic sense of "being there" and such a system should therefore provide a high quality visual rendition of the environment. Furthermore, panoramas, by virtue of capturing a greater proportion of a real-world scene, are often exposed to a greater dynamic range than standard photographs. Both facets of this system therefore stand to benefit from HDR imaging techniques. We demonstrate the success of our approach on multiple challenging ghosting scenarios, and compare our results with state-of-the-art methods previously proposed. We also demonstrate computational savings over these methods.

A síntese de imagens com alta faixa dinâmica é uma prática cada vez mais comum em computação gráfica. O desafio consiste em relacionar o grande conjunto de intensidades da imagem sintetizada com um sub-conjunto muito inferior suportado por um dispositivo de exibição, evitando a perda de detalhes contrastivos. Os operadores locais de reprodução de tons (local tone-mapping operators) são capazes de realizar tal compressão, adaptando o nível de luminância de cada pixel com respeito à sua vizinhança. Embora produzam resultados significativamente superiores aos operadores globais, o custo computacional é consideravelmente maior, o que vem impedindo sua utilização em aplicações em tempo real. Este trabalho apresenta uma técnica para aproximar o operador fotográfico local de reprodução de tons. Todas as etapas da técnica são implementadas em GPU, adequando-se ao cenário de aplicações em tempo real, sendo significativamente mais rápida que implementações existentes e produzindo resultados semelhantes. A abordagem é baseada no uso de tabelas de áreas acumuladas (summed-area tables) para acelerar a convolução das vizinhanças, usando filtros da média (box-filter), proporcionando uma solução elegante para aplicações que utilizam imagens em alta faixa dinâmica e que necessitam de performance sem comprometer a qualidade da imagem sintetizada. Uma investigação sobre algoritmos para a geração de somatórios pré-fixados (prefix sum) e uma possível melhoria para um deles também são apresentada. / High dynamic range (HDR) rendering is becoming an increasingly popular technique in computer graphics. Its challenge consists on mapping the resulting images’ large range of intensities to the much narrower ones of the display devices in a way that preserves contrastive details. Local tone-mapping operators effectively perform the required compression by adapting the luminance level of each pixel with respect to its neighborhood. While they generate significantly better results when compared to global operators, their computational costs are considerably higher, thus preventing their use in real-time applications. This work presents a real-time technique for approximating the photographic local tone reproduction that runs entirely on the GPU and is significantly faster than existing implementations that produce similar results. Our approach is based on the use of summed-area tables for accelerating the convolution of the local neighborhoods with a box filter and provides an attractive solution for HDR rendering applications that require high performance without compromising image quality. A survey of prefix sum algorithms and possible improvements are also presented.

Digital photography became widespread with the global use of smartphones. However, most of the captured images do not fully use the camera capabilities by storing the captured photos in a format with limited dynamic range. The subject of dynamic range expansion and reconstruction has been researched since early 2000s and recently gave rise to several new reconstruction methods using convolutional neural networks (CNNs), whose performance has not yet been comprehensively compared. By implementing and using our dynamic range reconstruction evaluation framework we compare the reconstruction quality of individual CNN-based approaches. We also implement a mobile HDR camera application and evaluate the feasibility of running the best-performing reconstruction method directly on a mobile device.