Image Denoising with Directional Bases

Directional information is an important component of both natural and synthetic images, and it is exploited in many image processing applications. Directional basis analysis is used to capture significant structural information. This paper presents an empirical study of image denoising with directional bases. We consider two distinct approaches. One involves the multi-resolution Fourier transform (MFT) facilitated with a multi-directional selective filter. The other is based on statistics, independent component analysis (ICA) that adaptively decomposes an image into a set of directional bases. We then present a combined approach that benefits from the computational efficiency of the MFT and the data adaptiveness of ICA. Experimental results are compared with those from other recent directional transforms such as the curvelet and directional cosine transform.