Topic
Contourlet
About: Contourlet is a research topic. Over the lifetime, 3533 publications have been published within this topic receiving 38980 citations.
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TL;DR: This paper develops an algorithm that allows for the approximation inversion operator to be controlled on a multiscale and multidirectional basis and shows that this method can perform significantly better than many competitive deconvolution algorithms.
Abstract: In this paper, a new type of deconvolution algorithm is proposed that is based on estimating the image from a shearlet decomposition. Shearlets provide a multidirectional and multiscale decomposition that has been mathematically shown to represent distributed discontinuities such as edges better than traditional wavelets. Constructions such as curvelets and contourlets share similar properties, yet their implementations are significantly different from that of shearlets. Taking advantage of unique properties of a new M-channel implementation of the shearlet transform, we develop an algorithm that allows for the approximation inversion operator to be controlled on a multiscale and multidirectional basis. A key improvement over closely related approaches such as ForWaRD is the automatic determination of the threshold values for the noise shrinkage for each scale and direction without explicit knowledge of the noise variance using a generalized cross validation (GCV). Various tests show that this method can perform significantly better than many competitive deconvolution algorithms.
78 citations
01 Jan 2002
TL;DR: The contourlet transform as mentioned in this paper is designed to satisfy the anisotropy scaling relation for curves, and thus offers a fast and structured curuelet-like decomposition.
Abstract: We propose a new scheme, named contourlet, that provides a flexible multiresolution, local and directional image expansion. ’ The contourlet transform is realized eficiently via a double iterated filter bank structure. Furthermore, it can be designed to satisfy the anisotropy scaling relation for curves, and thus offers a fast and structured curuelet-like decomposition. As a result, the eontourlet transform provides a sparse representation for two-dimensional piecewise smooth signals resembling images. Finally, we show some numerical experiments demonstrating the potential of contourlets in several image processing tas!e.
78 citations
TL;DR: Experimental evaluation shows that using combination of NSCT, RDWT, SVD and chaotic encryption makes the approach robust, imperceptible, secure and suitable for medical applications.
Abstract: In this paper, a chaotic based secure medical image watermarking approach is proposed. The method is using non sub-sampled contourlet transform (NSCT), redundant discrete wavelet transform (RDWT) and singular value decomposition (SVD) to provide significant improvement in imperceptibility and robustness. Further, security of the approach is ensured by applying 2-D logistic map based chaotic encryption on watermarked medical image. In our approach, the cover image is initially divided into sub-images and NSCT is applied on the sub-image having maximum entropy. Subsequently, RDWT is applied to NSCT image and the singular vector of the RDWT coefficient is calculated. Similar procedure is followed for both watermark images. The singular value of both watermarks is embedded into the singular matrix of the cover. Experimental evaluation shows when the approach is subjected to attacks, using combination of NSCT, RDWT, SVD and chaotic encryption it makes the approach robust, imperceptible, secure and suitable for medical applications.
76 citations
TL;DR: The fusion approach exploits the advantages of both SW-NSCT in multiscale geometric representations and that of PCNN in the determination of fusion rules; the obtained fusion image can preserve much more information regarding textures and edges of the images, compared to its counterparts.
75 citations
TL;DR: This paper proposes to utilize the logarithmic nonsubsampled contourlet transform (LNSCT) to estimate the reflectance component from a single face image and refer it as the illumination invariant feature for face recognition, where NSCT is a fully shift-invariant, multi-scale, and multi-direction transform.
74 citations