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

By Carlos Miravet and Francisco B. Rodriguez


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

Topics: Machine Vision, Neural Nets
Publisher: Springer-Verlag
Year: 2005
DOI identifier: 10.1007/11550822_78
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  1. (2003). A hybrid MLP-PNN architecture for fast image superresolution”,
  2. A two-step neural network based algorithm for fast high-resolution image reconstruction”,
  3. (1996). Extraction of high-resolution frames from video sequences”,
  4. (1998). Highresolution image reconstruction from a sequence of rotated and translated frames and its application to an infrared imaging system",
  5. (1991). Improving resolution by image registration”,
  6. (2001). Independent Component Analysis. Wiley-InterScience
  7. (2000). Infrared image registration and high-resolution reconstruction using multiple translationally shifted aliased video frames”,
  8. Multiframe image restoration and registration”,
  9. (1995). Neural Networks for Pattern Recognition. doi
  10. Numerical recipes in C,
  11. (1998). Super-resolution from image sequences-a review”,
  12. (2003). Super-resolution image reconstruction: a technical overview”,