0510008 - Accurate and robust image superresolution by...

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Accurate and robust image superresolution by neural processing of local image representations Carlos Miravet 1,2 and Francisco B. Rodríguez 1 1 Grupo de Neurocomputación Biológica (GNB), Escuela Politécnica Superior, Universidad Autónoma de Madrid, 28049 Madrid, Spain [email protected] , [email protected] 2 SENER Ingeniería y Sistemas, S. A.,Severo Ochoa 4 (P.T.M.), 28760 Madrid, Spain 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. Introduction Image superresolution [1, 2] involves the processing of an image sequence to generate a high-resolution description of the underlying scene. From the earliest algorithm pro- posed by Tsai and Huang [3], a number of approaches have been proposed. Of these, bayesian MAP (Maximum A Posteriori) methods [4,5] have gained particular acceptance due to their robustness and their capability to incorporate a priori con- straints. The main drawback of these methods comes from their associated high com- putational loads, as they use iterative techniques in spaces of high dimensionality. Recently [6, 7], the authors have proposed a neural network based technique that provides results comparable to classical methods with a substantial decrease in com- putational complexity. This technique estimates image values in a dense grid using an irregular interpolation scheme, with distance dependent interpolation weights. Opti- mal distance-to-weight mappings are learned from synthetic sequences and corre- sponding high-resolution images, using a hybrid MLP-PNN (Multi Layer Perceptron – Probabilistic Neural Network) architecture. In a second step, high-resolution image values are restored from estimated grid values using optimal filters. The use of interpolation schemes based on distance dependent weights, no matter how optimally these weights can be tuned, poses a fundamental limit on the attainable
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performance as, being independent of local image structure, the method is forced to operate in the same way near an image edge or inside a uniform image patch. In this paper, an evolution of our previous distance-based algorithm is presented, which makes explicit use of local image representations. As in our previous approach, the
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0510008 - Accurate and robust image superresolution by...

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