Restauração de imagens com precisão subpixel utilizando restrições convexas
Antunes Filho, Amauri
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The super-resolution aims to obtain a higher resolution image, using information from one or more low resolution images. There are different applications where super-resolution may be used, such as medical and forensic images. This work proposes a study and development of algorithms, based on Tekalp and Sezan’s algorithm, using the projection onto convex sets theory, in order to obtain super-resolution, therefore obtaining a higher resolution image, from a low resolution images set, with subpixel informations. We proposed the adition of a convex restriction based on Richardon-Lucy’s algorithm, modified to be weighted by Canny’s filter, along with total variation regularization, aiming to restore frequencies lost during high resolution images decimation and degradation processes . Therefore, we have a hybrid approach, that implements spatial and spectral super-resolution simultaneously, based on projection onto convex sets. The obtained results by the proposed algorithms were compared to Tekalp and Sezan’s base algorithm. The visual analysis of the images, along with the mean square error were taken in consideration for comparisons. In development, grayscale images were used, but the methods are extensible for color images. Results showed improvement in the obtained images, with less noise, blurring and more edge definition than the low resolution images. We conclude that the approach has potential for medical applications and forensic computation.