Name: THAÍS PEDRUZZI DO NASCIMENTO
Publication date: 09/10/2015
Advisor:
Name |
Role |
|---|---|
| EVANDRO OTTONI TEATINI SALLES | Advisor * |
Examining board:
| Name |
Role |
|---|---|
| EVANDRO OTTONI TEATINI SALLES | Advisor * |
| KLAUS FABIAN COCO | External Examiner * |
| MARCELO DE OLIVEIRA CAMPONEZ | External Examiner * |
Summary: Multiframe Superresolution is a technique that generates one high resolution image from several lower resolution images. Different SR methods attempt to implement superresolution successfully. Some of them use bayesian approaches and are based on l2 -norm or l1 -norm. The estimated high resolution image is obtained, in general, by running an iterative algorithm that usually uses MSE (Mean Squared Error) as its convergence criteria. However, MSE does not consider the images structural characteristics that are perceived by the human eye. The structural similarity index method (SSIM), on the other hand, uses such characteristics, and luminance and contrast as well, to quantify the differences between the image estimated by superresolution and the original high resolution one. Therefore, in this work, we propose the use of SSIM as an error metrics to lead the iterative adjustment process to obtain the high resolution image, in order to explore in a better way, the gains obtained from a priori functions that preserve edges and structural details. In this regard, we have compared results from different variational Bayesian methods by changing the convergence criteria from MSE to SSIM. A set of experiments were proposed and the results showed that, in addition to the improvement of the quality image assessment, there was an improvement on the execution time of the algorithms.
