Bibliographic Details
| Title: |
Accurate and robust image superresolution by neural processing of local image representations |
| Authors: |
Miravet, Carlos, Rodriguez, Francisco B. |
| Source: |
Miravet, Carlos and Rodriguez, Francisco B. (2005) Accurate and robust image superresolution by neural processing of local image representations. [Conference Paper] |
| Publication Status: |
Published |
| Publisher Information: |
Springer Verlag, 2005. |
| Publication Year: |
2005 |
| Subject Terms: |
Computer Science: Machine Vision, Computer Science: Neural Nets, Machine Vision, Neural Nets |
| More Details: |
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. |
| Document Type: |
Conference Paper |
| File Description: |
application/pdf |
| Access URL: |
http://cogprints.org/4567/ |
| Accession Number: |
edscog.4567 |
| Database: |
CogPrints |
