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Concept# Pixel

Résumé

thumb|upright=1.4|Image numérique dont une portion est très agrandie. Les pixels apparaissent ici comme des petits carrés.
Le pixel, souvent abrégé p ou px, est l'unité de base de la définition d'une matricielle. Ce mot provient de la locution anglaise picture element, qui signifie « élément d'image ».
Définition
Le pixel est l'unité minimale adressable par le contrôleur vidéo. C'est aussi l'unité utilisée pour spécifier les définitions d'affichage (largeur × hauteur).
À chaque pixel est associée une couleur, usuellement décomposée en trois composantes primaires par synthèse additive : rouge vert bleu.
Sur un téléviseur, chaque pixel est reconstitué par une triade de composants électroluminescents, rendant des tons rouge, vert et bleu par excitation électrique (canon à électrons du tube cathodique, écran à diodes électroluminescentes, à cristaux liquides, ou à plasma).
Dimension d'un pixel
gauche|vignette|Pixel de plus en plus petit donnant une image de m

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Appareil photographique numérique

Un appareil photographique numérique (ou APN) est un appareil photographique qui recueille la lumière sur un capteur photographique électronique, plutôt que sur une pellicule photographique, et qui c

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Image resolution is the level of detail an holds. The term applies to digital images, film images, and other types of images. "Higher resolution" means more image detail.
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Écran à cristaux liquides

thumb|right|Dans une Tablet PC.
thumb|right|Dans un appareil photographique numérique.
L'écran à cristaux liquides ou LCD (de l'anglais liquid crystal display) (ACL au Québec pour affichage à cristau

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Standard image sensors feature dynamic range about 60 to 70 dB while the light flux of natural scenes may be over 120 dB. Most imagers dedicated to address such dynamic ranges, need specific, and large pixels. However, canonical imagers can be used for high dynamic range (HDR) by performing multicapture acquisitions to compensate saturation. This technique is made possible at the expense of the need for large memory requirements and an increase of the overall acquisition time. On the other hand, the implementation of compressive sensing (CS) raises the same issues regarding the modifications of both the pixel and the read-out circuitry. Assuming HDR images are sufficiently sparse, CS claims they can be reconstructed from few random linear measurements. A novel CS-based image sensor design is presented in this paper allowing a compressive acquisition without changing the classical pixel design, as well as the overall sensor architecture. In addition to regular CS, HDR CS is enabled thanks to specific time diagrams of the control signals. An alternative nondestructive column-based readout mode constitutes the main change compared to a traditional functioning. The HDR reconstruction, which is also presented in this paper, is based on merging the information of multicapture compressed measurements while taking into account noise sources and nonlinearities introduced by both the proposed acquisition scheme and its practical implementation.

In the past decades, two recording tools have established themselves as the working horses in the field of electrophysiological cell research: the microelectrode array (MEA) and the optical fluorescence imaging. The former is a grid of miniature electrodes allowing to monitor and stimulate cell networks; the latter uses voltage-sensitive dyes (VSDs) rendering the membrane potential visible. So far, researchers trying to combine these two measurement methods face complex electro-optical setups that suffer from the limitations of both techniques. On this ground, it is highly desirable to have a new tool at hand that (a) implements both measurement methods in a single device, (b) offers a high spatio-temporal resolution for both the electrical and the optical sensors, (c) is compact, (d) is easy to handle, and (e) allows long-term measurements. The project, this work is part of, aims to achieve these goals by means of a new concept: the multi-mode sensor array (MMSA). It consists of two parts: (1) a quartz-based microfabricated biointerface housing the sensing devices, i.e. platinum electrodes and amorphous silicon photodiodes; (2) an application specific integrated circuit (ASIC) for the amplification and the conditioning of the detected signals. The two devices are flip-chip bonded together by high-density indium bumps. The resulting measurement tool features a sensor array containing 1024 electrodes and 3072 photodiodes at a pitch of 60 μm and 30 μm, respectively. The main objective of this work is the development of said ASIC with the main task being the design of low-noise, high-density electronic circuits to read the signals detected by the biointerface electrodes and photodiodes. Secondary goals are the packaging of the system and the development of a data acquisition platform to visualise and analyse the measured values. In the course of this dissertation, two ASIC versions were designed: the MMSA I and MMSAII chips. With regard to the electrode low-noise amplifier (LNA), the two versions exhibit substantially different architectures. The MMSAI LNA features a fully-differential architecture, two amplification stages with selectable gain iii Abstract (60/50 dB) and a stable bandwidth of 10 kHz, and two integrated offset compensation techniques. While the measured gain and bandwidth are in good agreement with the simulations, the noise is substantially higher than anticipated since flicker noise was not taken into account in the circuit analysis. Based on the experiences from the MMSA I chip, the second ASIC was developed with a single-ended architecture with a folded-cascode amplifier at its core. It exhibits a gain of 52 dB, a bandwidth of 52 kHz, an average noise of 26 μVrms, and a power consumption of less than 85 μW. The offset compensation allows to deal with input-referred offset in a total range of 40 mV. Furthermore, each pixel was equipped with a sample & hold buffer and a programmable stimulation circuitry. The former stores the amplified signal locally thereby realising an electronic shutter for the electrode signals; the latter is centered around a SRAM (static random access memory) cell and permits the creation of complex stimulation patterns on the array. Comparing the design with literature reveals the competitiveness of the circuit in terms of noise efficiency factor versus area. For the treatment of the photodiode signals, the three-transistor active pixel sensor (APS) architecture is employed due to its simplicity and compactness. To maximise the sensitivity of the sensor, very small transistor dimensions were chosen, thereby minimising the capacitance. The measured responsivity ranges from 0.75 V/μWμs to 0.91 V/μWμs depending on the wavelenght of the incident light. The detectable amplitude can be adjusted by choosing an appropriate integration time. The developed package provides for the biocompatibility of the system, a stable casing, thermal dissipation, a container for the cell culture and nutrient solution, and modularity for easy exchange. The data acquisition platform transfers data at a rate of up to 35 MB/s to a computer via USB 2.0. Furthermore, it offers the creation of protocols to define the course of experiments including the choice of stimulation signal waveform, stimulation patterns, timing information, and recording options.

The trends in the design of image sensors are to build sensors with low noise, high sensitivity, high dynamic range, and small pixel size. How can we benefit from pixels with small size and high sensitivity? In this dissertation, we study a new image sensor that is reminiscent of traditional photographic film. Each pixel in the sensor has a binary response, giving only a one-bit quantized measurement of the local light intensity. The response function of the image sensor is non-linear and similar to a logarithmic function, which makes the sensor suitable for high dynamic range imaging. We first formulate the oversampled binary sensing scheme as a parameter estimation problem based on quantized Poisson statistics. We show that, with a single-photon quantization threshold and large oversampling factors, the Cramér-Rao lower bound (CRLB) of the estimation variance approaches that of an ideal unquantized sensor, that is, as if there were no quantization in the sensor measurements. Furthermore, the CRLB is shown to be asymptotically achievable by the maximum likelihood estimator (MLE). By showing that the log-likelihood function is concave, we guarantee the global optimality of iterative algorithms in finding the MLE. We study the performance of the oversampled binary sensing scheme in presence of dark current noise. The noise model is an additive Bernoulli noise with a known parameter, and the noise only flips the binary output from "0" to "1". We show that the binary sensor is quite robust with respect to noise and its dynamic range is only slightly reduced. The binary sensor first benefits from the increasing of the oversampling factor and then suffers in term of dynamic range. We again use the MLE to estimate the light intensity. When the threshold is a single photon, we show that the log-likelihood function is still concave. Thus, the global optimality can be achieved. But for thresholds larger than "1", this property does not hold true. By proving that when the light intensity is piecewise-constant, the likelihood function is a strictly pseudoconcave function, we guarantee to find the optimal solution of the MLE using iterative algorithms for arbitrary thresholds. For the general linear light field model, the log-likelihood function is not even quasiconcave when thresholds are larger than "1". In this circumstance, we find an initial solution by approximating the light intensity field with a piecewise-constant model, and then we use Newton's method to refine the estimation using the exact model. We then examine one of the most important parameters in the binary sensor, i.e., the threshold used to generate binary values. We prove the intuitive result that large thresholds achieve better estimation performance for strong light intensities, while small thresholds work better for low light intensities. To make a binary sensor that works in a larger range of light intensities, we propose to design a threshold array containing multiple thresholds instead of a single threshold for the binary sensing. The criterion is to minimize the average CRLB which is a good approximation of the mean squared error (MSE). The performance analysis on the new binary sensor verifies the effectiveness of our design. Again, the MLE is used for reconstructing the light intensity field from the binary measurements. By showing that the log-likelihood function is concave for arbitrary threshold arrays, we ensure that the iterative algorithms can find the optimal solution of the MLE. Finally, we study the reconstruction problem for the binary image sensor under a generalized piecewise-constant light intensity field model, which is quite useful when parameters like oversampling factors are unknown. We directly estimate light exposure values, i.e., the number of photons hitting on each pixel. We assume that the light exposure values are piecewise-constant and we use the MLE for the reconstruction. This optimization problem is solved by iteratively working out two subproblems. The first one is to find the optimal light exposure value for each segment, given the optimal segmentation of the binary measurements. The second one is to find the optimal segmentation of the binary measurements given the optimal light exposure values for each segment. Several algorithms are provided for solving this optimization problem. Dynamic programming can obtain the optimal solution for 1-D signals, but the computation is quite heavy. To reduce the burden of computation, we propose a greedy algorithm and a method based on pruning of binary trees or quadtrees.

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