We aim to develop a scoring method for expressing directional dominance in the images. It is predicted that this score will give an information of how much improvement in system performance can be achieved when using a directional total variation (DTV)-based regularization instead of total variation (TV). For this purpose, a dataset consists of 85 images taken from the noise reduction datasets is used. The DTV values are calculated by using different sensitivities in the direction of the directional dominance of these images. The slope of these values is determined as the directional dominance score of the image. To verify this score, the noise reduction performances are examined by using direction invariant TV and DTV regulators of images. As a result, we observe that the directional dominance score and the improvement rate in noise reduction performance are correlated. Therefore, the resulting score can be used to estimate the performance of DTV method.
CITATION STYLE
Akkoca-Gazioglu, B. S., & Kamasak, M. (2017). A new scoring method for directional dominance in images. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10425 LNCS, pp. 3–13). Springer Verlag. https://doi.org/10.1007/978-3-319-64698-5_1
Mendeley helps you to discover research relevant for your work.