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Accurate and robust image superresolution by neural processing of local image representations

Miravet, Carlos and Rodriguez, Francisco B. (2005) Accurate and robust image superresolution by neural processing of local image representations. [Conference Paper]

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Abstract

Image superresolution involves the processing of an image sequence to generate a still image with higher resolution. Classical approaches, such as bayesian MAP methods, require iterative minimization procedures, with high computational costs. Recently, the authors proposed a method to tackle this problem, based on the use of a hybrid MLP-PNN architecture. In this paper, we present a novel superresolution method, based on an evolution of this concept, to incorporate the use of local image models. A neural processing stage receives as input the value of model coefficients on local windows. The data dimension-ality is firstly reduced by application of PCA. An MLP, trained on synthetic se-quences with various amounts of noise, estimates the high-resolution image data. The effect of varying the dimension of the network input space is exam-ined, showing a complex, structured behavior. Quantitative results are presented showing the accuracy and robustness of the proposed method.

Item Type:Conference Paper
Keywords:superresolution, neural networks, image sequence processing
Subjects:Computer Science > Machine Vision
Computer Science > Neural Nets
ID Code:4567
Deposited By: Miravet, Carlos
Deposited On:20 Oct 2005
Last Modified:11 Mar 2011 08:56

References in Article

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