Nonlinear Systems for Image Processing

Advances in imaging and electron physics
Source: OAI

ABSTRACT Nonlinear Systems for Image Processing

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    ABSTRACT: It is progressively realized that noise can play a constructive role in the domain of nonlinear information pro-cessing. This phenomenon, also known as stochastic resonance (SR) effect, has experienced large varieties of extensions with variations concerning the type of noise, the type of information carrying signal or the type of nonlinear system interacting with the signal-noise mixture. In this article, we propose an interpretation for the mechanism of noise-enhanced image restoration with nonlinear PDE (Partial Differential Equation) recently demonstrated in literature. More precisely, a link is established between the action of noise in a nonlinear Perona–Malik anisotropic diffusion and stochastic resonance in memoryless nonlinear systems for 1-D signals. For illustration some preliminary results are presented on classical "camera-man" image and the inner of SR mechanism is theoretically and practically studied using a simple set of parameters regarding the PDE used and the modeling of boundaries within images.
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    ABSTRACT: An electrical transmission lattice exactly ruled by the Klein-Gordon equation is proposed. It is experimentally shown that this medium transmits energy even outside its bandpass via the supratransmission effect. Information transmission applications are discussed.
    Electronics Letters 02/2010; · 1.04 Impact Factor
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    ABSTRACT: Raw output data from image sensors tends to exhibit a form of bias due to slight on-die variations between photodetectors, as well as between amplifiers. The resulting bias, called fixed pattern noise (FPN), is often corrected by subtracting its value, estimated through calibration, from the sensor’s raw signal. This paper introduces an on-line scene-based technique for an improved fixed-pattern noise compensation which does not rely on calibration, and hence is more robust to the dynamic changes in the FPN which may occur slowly over time. This article first gives a quick summary of existing FPN correction methods and explains how our approach relates to them. Three different pipeline architectures for realtime implementation on a FPGA-based smart camera are then discussed. For each of them, FPGA implementations details, performance and hardware costs are provided. Experimental results on a set of seven different scenes are also depicted showing that the proposed correction chain induces little additional resource use while guarantying high quality images on a wide variety of scenes.
    Journal of Systems Architecture 11/2013; · 0.72 Impact Factor

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May 20, 2014