NSCT edge Enhancement for SIFT key points extraction

Abstract

International audienceImage registration is a key step for matching or mosaicing two or more images taken at different times, and/or with different sensors, hence the need for automatic methods arises. In this work, we present an efficient registration method based on the Non-Subsampled Contourlet Transform (NSCT) combined with the Scale Invariant Feature transform (SIFT) to extract robust local control points. Because NSCT is a shift-invariant multidirectional transform, it is used to extract edges at both spatial and directional resolutions. A comparative study is established between SIFT and NSCT-SIFT and experimental results show clearly that the proposed method (NSCT-SIFT) improves the registration accuracy

Blind Digital Watermarking Framework Based on DTCWT and NSCT for Telemedicine Application

“…The Contourlet Transform (CT) was first presented by Do and Vetteli [26], which is a multi-resolution and multidirectional transform, contours are better exhibited by the Contourlet Transform analogized to divers transforms [27]. Da Cunha et al [28] suggested NSCT, which is the shift invariant model of the CT [29], besides that, in the course of image decomposing and reconstructing, the NSCT abolish down and up samples [30], the Figure 2 shows the NSCT structure.…”