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About 2,287 papers for “Analog image processing”

PROGRESS IN ANALOG IMAGE PROCESSING

James J. Burke, John G. Thunen · UA Campus Repository (The University of Arizona) · 1968

QC 351 A7 no. 33

Cited by 1

ANALOG TECHNIQUES FOR PROCESSING IMAGE DATA.

Earle C. Gregg · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · 1970

Excessive particle growth was limited by the use of air attrition-jets inserted directly in the bed. Aqueous hydrofluoric acid solutions containing l.2 to 3.6 M zirconiuni, 0.007 to 0.03 M uranium, and free acid concentrations from 1 to about l0 M were successfully processed in a 6-in.-diameter Inconel fluid-bed spray dryer. Rates equivalent to about 3.l kg/hr of zirconium were achieved, 160 ml/min with the most concentrated feed solution. Experiments were successfully carried out from 240 to 450 deg C. A new design for a two-fluid nozzle was developed. Extensive work was done to identify the various zirconium fluoride compounds formed. The granular dryer product was subsequently fluorinated at temperatures to 600 deg C in fluid beds and to 700 deg C in static beds to remove the uranium as the volatile hexafluoride. About 90 to 95% uranium removal was consistently achieved near 600 deg C. The relatively low uranium recovery under these conditions is a disadvantage for the application to zirconium-base fuels. It was found necessary to resort to static beds and higher temperatures to achieve greater removal. Since the fluorine attack on nickel, the material of construction, is prohibitive at temperatures above 600 deg C, a disposable fluorinator concept for use with static beds is described. Results of corrosion studies are reported. A preliminary chemical flowsheet with a design capacity of 1l00 kg of uranium (93% enriched) annually is presented. (auth)

Cited by 0Open access

Analog Image Processing in Fluorescence Microscopy

Denis K. Samuylov · Repository for Publications and Research Data (ETH Zurich) · 2018

This thesis addresses the problem of studying small objects (with respect to the pixel size and spatial resolution of the imaging system) characterised by a fast dynamics (with respect to the data acquisition rate) from fluorescence microscopy image data.Due to the nature of such objects and the limitations of the imaging system, studying the objects results in solving ill-posed and ill-conditioned inverse problems.Therefore, it requires encoding the available prior knowledge about the objects under scrutiny and the image formation process to restrict the set of admissible solutions.The conventional image processing approaches are mostly digital in nature: they model objects on a digital grid, thus introducing bias entailed by the object model discretisation and limiting the spatio-temporal scales of the object features that can be reconstructed from image data.Repeatedly imaging a random sparse subset of fluorophores and formulating the analog inverse problems for localising them individually allowed achieving a nanometer accuracy in reconstructing objects from images and breaking the resolution limit in fluorescence microscopy.Such accuracy is possible thanks to exploiting the prior knowledge about the sparsity in the fluorophore distribution and the image formation process.However, this approach requires many image acquisitions to reconstruct the full object, thus is only suitable in studying immobile items, and does not provide sufficient flexibility in encoding the prior knowledge about the underlying object.In the thesis, we address these limitations and derive a framework that enables formulating analog inverse problems and provides flexibility in encoding the prior knowledge based on complex spatio-temporal object models.We apply our framework to study the microtubule dynamics in the budding yeast S. cerevisiae.We demonstrate that modelling the microtubule geometry as a curvilinear object embedded in physical space and microtubule photometry as a continuous-domain intensity signal along the geometry allows eliminating bias introduced by simplified object models and revealing complex patterns occurring on the microtubule lattice that cannot be identified using conventional approaches.Our framework allows developing forward problems for other spatial restrictions like surfaces and volumes, and lays ground for formulating the inverse problems for studying other biological objects, e.g.studying the processes restricted to the membranes of intracellular compartments or the cytoplasm.

Cited by 0Open access

Metasurfaces for Broadband Analog Image Processing

Akshaj Arora, Michele Cotrufo, Andrea Alù · 2023

Analog computing using metasurfaces offers significant potential for processing information at ultrahigh speeds and low power consumption. Among several operations, implementing spatial differentiation and edge detection with metasurfaces is an active area of research for its potential impact in the automotive industry and for image recognition. However, current designs have been suffering from limitations due to trade-offs between bandwidth, spatial resolution and efficiency. In this work, dispersion engineering of a nonlocal metasurface is leveraged to obtain analog edge detection over a moderately large operational bandwidth, without sacrificing numerical aperture and efficiency. We demonstrate the implementation of this concept by using a single-layered silicon metasurface with triangular lattice of holes, which exhibits dual-polarization and azimuthally isotropic response up to large numerical apertures (NAs ∼0.35), and with an operational bandwidth of 5 THz around a central wavelength of about 1500 nm. The proposed design approach can also be extended to other platforms, operational frequencies and materials.

Cited by 0

Electronic HDR Image Processing: Analog and Digital

John J. McCann, Alessandro Rizzi · 2011

Electronic High-Dynamic-Range (HDR) image capture and display began in the 1960s. At first, it used cumbersome analog processing, but quickly moved to digital image processing. The earliest ideas applied the same principles found in human spatial vision to picture making. This chapter reviews research on spatial vision at that time. This paper suggested that lightness correlated with spatial ratios and expanded it beyond the restraints of uniform illumination. Electronic image processing, fi rst with analog, then with digital image processing and digital photography addressed the rendition of HDR scenes. Early systems set out to mimic human vision ' s example of capturing HDR information and rendering it in LDR media. These systems use HDR spatial information from the entire scene to calculate appearances and write them on prints and displays. Controlled Vocabulary Terms analog processing circuits; digital images; digital photography; image processing; lighting

Cited by 0

Metasurfaces for Image Processing and Analog Computing

Andrea Alù · 2024

I discuss our recent progress in demonstrating ultrathin engineered surfaces that perform image processing and computing on optical signals and images, and the opportunities for photonic technologies. Full-text article not available; see video presentation

Cited by 0

Analog VLSI arrays for morphological image processing

T.G. Morris, Stephen P. DeWeerth · 2002

A two-dimensional analog VLSI array that performs basic morphological image processing operations is presented. The system uses a smart pixel approach that facilitates the parallel computation of continuous real-time outputs. Photodetectors within the array of smart pixels also allow for parallel optical inputs. The processing is performed by current-mode circuitry implemented with CMOS technology. The signal values are encoded as analog current values. The processing array uses local communication to perform the morphological operation known as dilation, and outputs the difference between the original input image and the dilation image to detect edges. The system was fabricated using a standard 2.0 /spl mu/m digital CMOS process. Example output data of the fabricated circuitry are presented.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>

Cited by 16

Analog Image Processing in Modern Color Photography

Joachim Lohmann, Johannes Willsau · Journal of Imaging Science and Technology · 1999

In the context of this article, analog image processing comprises methods to chemically control and improve image quality in color negative films during photographic processing. The chemical control applies to the generation of image dyes and to the optical interaction of partial dye images. The image dyes are generated in two consecutive steps: silver reaction and coupling reaction. Yield- and distribution-parameters of these reaction steps are relevant for image-quality aspects. In this review the various chemical methods to achieve image processing (e. g. inhibition, (dye-) amplification, dye spread) are discussed.

Cited by 0

Analog image processing with nonlinear flat optics

Costantino De Angelis, Domenico de Ceglia · 2025

Digital signal processing has revolutionized many fields of science and engineering, but it still shows critical limits; a long-sought solution is optical analog computing. We demonstrate here that nonlinear phenomena combined with engineered nonlocality in flat optics can be leveraged to synthesize Volterra kernels able to outperform linear devices. We present here one relevant example, namely edge detection in space.

Cited by 0

Analog Signal Processing Solution for Image Alignment

Nihar Athreyas, Zhiguo Lai, Jai Gupta et al. · 2014

Imaging and Image sensors is a field that is continuously evolving. There are new products coming into the market every day. Some of these have very severe Size, Weight and Power constraints whereas other devices have to handle very high computational loads. Some require both these conditions to be met simultaneously. Current imaging architectures and digital image processing solutions will not be able to meet these ever increasing demands. There is a need to develop novel imaging architectures and image processing solutions to address these requirements. In this work we propose analog signal processing as a solution to this problem. The analog processor is not suggested as a replacement to a digital processor but it will be used as an augmentation device which works in parallel with the digital processor, making the system faster and more efficient. In order to show the merits of analog processing the highly computational Normalized Cross Correlation algorithm is implemented. We propose two novel modifications to the algorithm and a new imaging architecture which, significantly reduces the computation time.

Cited by 0Open access

Engineering Fourier planes for analog image processing

Muhammad 'Aqil Bin Rosdi · DR-NTU (Nanyang Technological University) · 2024

This report delves into the realm of optical information processing, focusing on the implementation of edge detection within a Fourier domain-based 4f optical system. Utilising MATLAB and its Image Processing Toolbox, the project aims to design and optimise a system for real-time image processing. By initialising fundamental physical constants and system parameters, and employing Sobel operators within the Fourier domain, the study demonstrates effective edge detection of various input patterns, including structured geometric shapes and arbitrary images. The results underscore the potential of combining optical and computational techniques to achieve efficient and accurate edge detection.\n\nThe project's success is evidenced by the clear delineation of edges in test patterns and complex images, highlighting the Sobel operator's robustness across different orientations and complexities. Furthermore, the exploration of MATLAB-based algorithms showcases advancements in edge detection methodologies, contributing to fields such as medical imaging, robotics, and quality control.\n\nThis comprehensive study not only addresses a critical research gap but also lays the groundwork for future innovations in optical information processing.

Cited by 0

An Analog Image Processing LSI Employing Scanning

Teruyasu Taguchi, Makoto Ogawa, Tadashi Shibata · 2003

A 64 x 64-pixel analog image filtering processor has been developed. The chip employs the scanning lineparallel processing architecture in order to realize a very fast execution of image filtering operation. In addition, any typical filtering kernels are programmable in this architecture. The line parallel processing is carried out for the pixels in a single row or the pixels in a single column at one time. The essence of the processing is that in the row parallel processing all necessary matrix multiplications in the column direction are carried out simultaneously, and that the rest of the matrix multiplication is completed in the column parallel processing that follows. The entire image filtering operation is completed by just scanning the line processing in both vertical and horizontal directions. As a result, the number of cycles required for the total operation is only in the order of M + N for an M x N-pixel image. A prototype chip of 64 x 64-pixel array was fabricated in a 0.6-µm double-polysilicon triple-metal CMOS technology, and the proposed concept has been experimentally demonstrated. 1.

Cited by 0

Nonlinear distortion in analog networks for image processing

Rare & Special e-Zone (The Hong Kong University of Science and Technology) · 1997

Cited by 0

Analog image processing with nonlinear nonlocal flat optics

de Ceglia, Domenico, Alu, Andrea, Neshev, Dragomir et al. · Figshare · 2023

Digital signal processing has revolutionized many fields of science and engineering, but it still shows critical limits, mainly related to the complexity, power consumption, and limited speed of analogue-to-digital converters. A long-sought solution to overcome these hurdles is optical analog computing. In this regard, flat optics has been recently unveiled as a powerful platform to perform data processing in real-time, with low power consumption and a small footprint. So far, these explorations have been mainly limited to linear optics. Arguably, significant more impact may be garnered from pushing this operation towards nonlinear processing of the incoming signals. In this context, we demonstrate here that nonlinear phenomena combined with engineered nonlocality in flat optics devices can be leveraged to synthesize Volterra kernels able to outperform linear optical analog image processing.

Cited by 0Open access

Analog image processing with nonlinear nonlocal flat optics

de Ceglia, Domenico, Alu, Andrea, Neshev, Dragomir et al. · Figshare · 2023

Cited by 0Open access

Analog image processing with nonlinear nonlocal flat optics

de Ceglia, Domenico, Alu, Andrea, Neshev, Dragomir et al. · Figshare · 2023

Cited by 0Open access

Image processing using a CMOS analog parallel architecture

Ion Vornicu, Liviu Goraş · 2010

The possibilities of image processing using the spatio-temporal dynamics of a Cellular Neural Network (CNN) type CMOS analog parallel architecture are investigated. It is shown that the network, consisting of an array of identically coupled identical cells, can be programmed to perform various spatial filtering operations and segmentation in a very short time and with low power consumption. Comparison with previously reported segmentation techniques and simulation results are discussed.

Cited by 6

Analog image processing with nonlinear nonlocal flat optics

Domenico de Ceglia, Andrea Alù, Dragomir N. Neshev et al. · Optical Materials Express · 2023

Digital signal processing has revolutionized many fields of science and engineering, but it still shows critical limits, mainly related to the complexity, power consumption, and limited speed of analogue-to-digital converters. A long-sought solution to overcome these hurdles is optical analog computing. In this regard, flat optics has been recently unveiled as a powerful platform to perform data processing in real-time, with low power consumption and a small footprint. So far, these explorations have been mainly limited to linear optics. Arguably, significantly more impact may be garnered from pushing this operation towards nonlinear processing of the incoming signals. In this context, we demonstrate here that nonlinear phenomena combined with engineered nonlocality in flat optics devices can be leveraged to synthesize Volterra kernels able to outperform linear optical analog image processing.

Cited by 22Open access

Ultra Low Energy Analog Image Processing Using Spin Neurons

Sharad, Mrigank, Augustine, Charles, Panagopoulos, Georgios et al. · arXiv (Cornell University) · 2012

In this work we present an ultra low energy, 'on-sensor' image processing architecture, based on cellular array of spin based neurons. The 'neuron' constitutes of a lateral spin valve (LSV) with multiple input magnets, connected to an output magnet, using metal channels. The low resistance, magneto-metallic neurons operate at a small terminal voltage of ~20mV, while performing analog computation upon photo sensor inputs. The static current-flow across the device terminals is limited to small periods, corresponding to magnet switching time, and, is determined by a low duty-cycle system-clock. Thus, the energy-cost of analog-mode processing, inevitable in most image sensing applications, is reduced and made comparable to that of dynamic and leakage power consumption in peripheral CMOS units. Performance of the proposed architecture for some common image sensing and processing applications like, feature extraction, halftone compression and digitization, have been obtained through physics based device simulation framework, coupled with SPICE. Results indicate that the proposed design scheme can achieve more than two orders of magnitude reduction in computation energy, as compared to the state of art CMOS designs, that are based on conventional mixed-signal image acquisition and processing schemes. To the best of authors' knowledge, this is the first work where application of nano magnets (in LSV's) in analog signal processing has been proposed.

Cited by 0Open access

Ultrafast Optically Tunable GaAs Metasurfaces for Analog Image Processing

V. V. Yushkov, Alexander S. Shorokhov, Andrey A. Fedyanin · ACS Omega · 2026

Optical metasurfaces offer unprecedented potential for miniaturizing conventional bulk optics into compact, energy-efficient devices suitable for advanced edge computing applications. However, current implementations lack the dynamic tunability essential for real-time optical processing functions. Here, we numerically demonstrate a gallium arsenide metasurface that achieves subpicosecond switching between dual complex-valued transfer functions through ultrafast photoinduced carrier modulation. Our device exploits specially designed Mie-resonant nanoantennas for nonuniform amplitude and phase profile change under pump pulses with typical energies about 4.8 nJ. The metasurface enables real-time Fourier filtering with switching frequency exceeding 100 GHz, providing a pathway to ultrafast neuromorphic vision sensors and live image processing applications.

Cited by 0Open access