Contourlet

About: Contourlet is a research topic. Over the lifetime, 3533 publications have been published within this topic receiving 38980 citations.

A Class of Multiresolution Directional Filter Banks

Abstract: In this paper, we introduced a class of directional filter banks (DFBs) having the previously proposed uniform DFB (uDFB) as a special case. Except for the uDFB, each DFB in this class can be used to decompose an image yielding up to 12 directions while maintaining perfect reconstruction and maximal decimation. A multiresolution representation can be obtained by repeating the same decomposition at the lowpass band. The permissible property of the filter banks in cases of being implemented by a tree structure and by direct implementation is discussed. The result shows that only one DFB in the class, called the uniform quincunx DFB (uqDFB), satisfies the permissible property when being implemented directly without using the tree structure. The nonuniform quincunx DFB (nuqDFB) is then constructed from the uqDFB by merging its two lowpass subbands. An alternative structure for constructing the nuqDFB is presented. The new structure, while yielding the same frequency partitioning, allows the DFB to be realized with complexity comparable to that of the separable wavelet filter bank. The connection between the discrete filter bank and the continuous directional wavelet is also established. Numerical experiments on directional feature extractions, image denoising and nonlinear approximation are presented at the end of the paper to demonstrate the potential of the nuqDFB

Multimodal Sensor Medical Image Fusion Based on Local Difference in Non-Subsampled Domain

Abstract: Medical imaging sensors, such as positron emission tomography and single-photon emission computed tomography, can provide rich information, but each has its inherent drawbacks In this scenario, multimodal sensor medical image fusion becomes an effective solution The chief objective of medical imaging is to extract as much preponderant and complementary information as possible from the source into a single output that can play a critical role in medical diagnosis and clinical operations In this paper, a novel fusion method is presented for multimodal sensor medical images, based on local difference (LD) in non-subsampled domain In this method, the source medical images are first decomposed into low-frequency and high-frequency subimages, via non-subsampled schemes Then, the coefficients of sub-bands are fused by an operator, called LD The final fused image is reconstructed, via the inverse non-subsampled schemes, with all composite coefficients The proposed fusion method was applied in several clinical studies, and the results show that it is a much more straightforward and effective method than some of the state-of-the-art methods, in terms of both subjective visual performance and objective evaluation results Also, the performance of the proposed method was compared with that of two non-subsampled schemes, namely, non-subsampled contourlet transform and non-subsampled shearlet transform

A robust semi-blind watermarking for color images based on multiple decompositions

Abstract: In this paper, a robust semi-blind watermarking scheme for color images, based on multiple decompositions is proposed to preserve the copyrights of the owner. Using multiple decompositions, the gray watermark is embedded into a host color image. Prior to that, to enhance security the gray watermark is encrypted with Arnold transform and SVD by generating secret keys. The luminance component of the given host image is subjected to discrete wavelet transform(DWT), contourlet transform(CT), Schur decomposition and singular value decomposition(SVD) in sequence and finally the watermark is embedded. In the semi-blind extraction process, the watermark is extracted without the help of the original host image. Experimental results show that the proposed watermarking scheme has better visual imperceptibility and high robustness against image & signal processing attacks compared to other methods.

A Robust Image Watermarking Scheme Using Local Statistical Distribution in the Contourlet Domain

Abstract: Data security is a main concern in everyday data transmissions in the Internet. A possible solution to guarantee a secure and legitimate transaction is via hiding a piece of tractable information into the multimedia signal, i.e., watermarking. This brief proposes a new multiplicative image watermarking scheme in the contourlet domain by taking into account the local statistical properties and inter-scale dependencies of the contourlet coefficients of images. Although the contourlet coefficients are non-Gaussian within a sub-band, their local distribution fits the Gaussian distribution very well. In addition, it is known that there exist across-scale dependencies among these coefficients. In view of this, we propose the use of bivariate Gaussian (BVG) distribution to model the distribution of the contourlet coefficients. Motivated by the modeling results, an optimum blind watermark decoder is designed in the contourlet domain using the maximum likelihood method. By means of carrying out a number of experiments, the performance of the proposed decoder is investigated with regard to the bit error rate and compared to other decoders. It will be shown that the proposed decoder built upon the BVG model is superior to other decoders in terms of rate of error. It will also be shown that the proposed decoder provides higher robustness in comparison to other decoders in presence of attacks such as filtering, compression, cropping, scaling, and noise.