Curvelet Transform with Adaptive Tiling

摘要

The curvelet transform is a recently introduced non-adaptive multi-scale transform that have gained popularity in the image processing field. In this paper, we study the effect of customized tiling of frequency content in the curvelet transform. Specifically, we investigate the effect of the size of the coarsest level and its relationship to denoising performance. Based on the observed behavior, we introduce an algorithm to automatically choose the optimal number of decompositions. Its performance shows a clear advantage, in denoising applications, when compared to default curvelet decomposition. We also examine how denoising is affected by varying the number of divisions per scale.