Contourlet

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

Fusion of Multi-band SAR Images Based on Contourlet Transform

Abstract: Aim at the fusion of multi-band synthetic aperture radar (SAR) images, a new fused method using the contourlet transform is presented Contourlet transform provides a flexible multiresolution, anisotropy and directional expansion for images Compared with wavelets, it can afford more efficient presentation of image edges This is employed for fusing the directional high- frequency coefficients For the lowpass coefficients, an averaging fusion rule is used For the directional high-frequency coefficients, the higher value of edge information measurement is used to select the better coefficients for fusion The proposed method solves the problem of losing edge information for wavelet based fusion method Finally, the example result of two bands SAR image fusion compared with wavelet fused method shows the vision effect and the statistical evaluation factors for fusion are both improved

An enhanced segmentation of blood vessels in retinal images using contourlet

Abstract: Retinal images acquired using a fundus camera often contain low grey, low level contrast and are of low dynamic range. This may seriously affect the automatic segmentation stage and subsequent results; hence, it is necessary to carry-out preprocessing to improve image contrast results before segmentation. Here we present a new multi-scale method for retinal image contrast enhancement using Contourlet transform. In this paper, a combination of feature extraction approach which utilizes Local Binary Pattern (LBP), morphological method and spatial image processing is proposed for segmenting the retinal blood vessels in optic fundus images. Furthermore, performance of Adaptive Neuro-Fuzzy Inference System (ANFIS) and Multilayer Perceptron (MLP) is investigated in the classification section. The performance of the proposed algorithm is tested on the publicly available DRIVE database. The results are numerically assessed for different proposed algorithms.

Contourlet transform based adaptive nonlinear diffusion filtering for speckle noise removal in ultrasound images

Abstract: Speckle noise removal plays a crucial role in ultrasound (US) image diagnosis, since the visual quality of the US images are largely corrupted by speckle noise. Numerous speckle noise removal techniques have been proposed in the literature based on anisotropic filtering, wavelets and morphology; however they have some major problems like loss of edge information, texture information and inability to remove low frequency noise. Despeckling of US images is usually carried out using conventional anisotropic diffusion or speckle reducing anisotropic diffusion. However, despeckling US images may not be able to preserve the edges which comprises of important clinical information. To overcome the issues in speckle noise removal (despeckling) of US images, contourlet transform based anisotropic nonlinear diffusion filtering is proposed in this paper. Contourlet transform improves some important features like multiscale and directionality. Adaptive nonlinear diffusion has been incorporated in anisotropic filtering to improve the filtering performance. The comparison performance of the proposed method with other despeckling techniques indicates that it has better noise removal performance for medical US images.

Multifocus image fusion method of Ripplet transform based on cycle spinning

Abstract: The curvelet transform can represent images at both different scales and different directions. Ripplet transform, as a higher dimensional generalization of the curvelet transform, provides a new tight frame with sparse representation for images with discontinuities along C2 curves. However, the ripplet transform is lack of translation invariance, which causes the pseudo-Gibbs phenomenon on the edges of image. In this paper, the cycle spinning method is adopted to suppress the pseudo-Gibbs phenomena in the multifocus image fusion. On the other hand, a modified sum-modified-laplacian rule based on the threshold is proposed to make the decision map to select the ripplet coefficient. Several experiments are executed to compare the presented approach with other methods based on the curvelet, sharp frequency localized contourlet transform and shearlet transform. The experiments demonstrate that the presented fusion algorithm outperforms these image fusion works.

An improved multimodal medical image fusion scheme based on hybrid combination of nonsubsampled contourlet transform and stationary wavelet transform

Abstract: This research proposes an improved hybrid fusion scheme for non‐subsampled contourlet transform (NSCT) and stationary wavelet transform (SWT). Initially, the source images are decomposed into different sub‐bands using NSCT. The locally weighted sum of square of the coefficients based fusion rule with consistency verification is used to fuse the detailed coefficients of NSCT. The SWT is employed to decompose approximation coefficients of NSCT into different sub‐bands. The entropy of square of the coefficients and weighted sum‐modified Laplacian is employed as the fusion rules with SWT. The final output is obtained using inverse NSCT. The proposed research is compared with existing fusion schemes visually and quantitatively. From the visual analysis, it is observed that the proposed scheme retained important complementary information of source images in a better way. From the quantitative comparison, it is seen that this scheme gave improved edge information, clarity, contrast, texture, and brightness in the fused image.