Nonlinear Systems for Image Processing

Advances in imaging and electron physics
Source: OAI

ABSTRACT Nonlinear Systems for Image Processing

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    ABSTRACT: We propose a new algorithm inspired by the properties of diffusion processes for image filtering. We show that purely nonlinear diffusion processes ruled by Fisher equation allows contrast enhancement and noise filtering, but involves a blurry image. By contrast, anisotropic diffusion, described by Perona and Malik algorithm, allows noise filtering and preserves the edges. We show that combining the properties of anisotropic diffusion with those of nonlinear diffusion provides a better processing tool which enables noise filtering, contrast enhancement and edge preserving.
    Physics Letters A 01/2009; · 1.63 Impact Factor
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    Applications and Experiences of Quality Control, 04/2011; , ISBN: 978-953-307-236-4
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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 01/2013; · 0.72 Impact Factor

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