title: Accurate and robust image superresolution by neural processing of local image representations creator: Miravet, Carlos creator: Rodriguez, Francisco B. subject: Machine Vision subject: Neural Nets description: 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. publisher: Springer-Verlag date: 2005 type: Conference Paper type: PeerReviewed format: application/pdf identifier: http://cogprints.org/4567/1/miravet_rodriguez_arxiv_05.pdf identifier: Miravet, Carlos and Rodriguez, Francisco B. (2005) Accurate and robust image superresolution by neural processing of local image representations. [Conference Paper] relation: http://cogprints.org/4567/