Contourlet

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

The Wavelet-based Contourlet Transform for Image Fusion

Abstract: The wavelet-based Contourlet transform (WBCT) is a new directional transform. This transform uses the wavelet transform and the directional filter bank (DFB) to obtain a multiscale and multidirection decomposition of image. The wavelet transform and the DFB are non-redundant and perfect reconstruction. So the WBCT can be regarded as a non-redundant version of the Contourlet transform. A new image fusion scheme based on the WBCT was presented. Firstly, the WBCT is used to perform a multiscale and multidirection decomposition of each image. Then the WBCT coefficients of fused image are constructed using multiple operators according to different fusion rules. The experimental results show that this new fusion scheme is effective and the fused images are better than that of using the Laplacian pyramid transform, the wavelet transform and the Contourlet transform.

Infrared and visible image fusion method of dual NSCT and PCNN.

Abstract: To solve the problem that the details of fusion images are not retained well and the information of feature targets is incomplete, we proposed a new fusion method of infrared (IR) and visible (VI) image-IR and VI image fusion method of dual non-subsampled contourlet transform (NSCT) and pulse-coupled neural network (PCNN). The method makes full use of the flexible multi-resolution and multi-directional of NSCT, and the global coupling and pulse synchronization excitation characteristics of PCNN, effectively combining the features of IR image with the texture details of VI image. Experimental results show that the algorithm can combine IR and VI image features well. At the same time, the obtained fusion image can better display the texture information of image. The fusion performance in contrast, detail information and other aspects is better than the classical fusion algorithm, which has better visual effect and evaluation index.

Visible light and infrared image fusion algorithm based on NSCT domain bottom layer visual features

Abstract: The invention provides a visible light and infrared image fusion algorithm based on non-subsample contourlet transform (NSCT) domain bottom layer visual features.Firstly, visible light and infrared images are subjected to NSCT, high and low frequency subband coefficients of the visible light and the infrared images are obtained, then phase equalization, neighborhood space frequency, neighborhood energy and other information are combined, the pixel active levels of the low frequency subband coefficients are comprehensively measured, fusion weights of the low frequency subband coefficients of the visible light and infrared images are obtained respectively, and therefore low frequency subband coefficients of fusion images are obtained; the pixel active levels of the high frequency subband coefficients are measured through the combination of phase equalization, definition, brightness and other information, fusion weights of the high frequency subband coefficients of the visible light and infrared images are obtained respectively, then high frequency subband coefficients of the fusion images are obtained, finally, NSCT reverse transformation is utilized, and final fusion images are obtained.Detail information of source images can be effectively reserved, and meanwhile useful information of the visible light images and the infrared images is synthesized.

A Comparative Study in Wavelets, Curvelets and Contourlets as Denoising biomedical Images

Abstract: A special member of the emerging family of multi scale geometric transforms is the contourlet transform which was developed in the last few years in an attempt to overcome inherent limitations of traditional multistage representations such as curvelets and wavelets. The biomedical images were denoised using firstly wavelet than curvelets and finally contourlets transform and results are presented in this paper. It has been found that contourlets transform outperforms the curvelets and wavelet transform in terms of signal noise ratio

A new MRI and PET image fusion algorithm based on pulse coupled neural network

Abstract: The fusion of multimodal brain imaging for a given clinical application is a very important performance. Generally, the PET (positron emission tomography) image indicates the brain function and it has a low spatial resolution, the MRI image shows the brain tissue anatomy and contains no functional information. In this paper, we propose a novel medical image fusion algorithm which enhances the spatial resolution of the functional images by combining them with a high-resolution anatomic image. In the event, after the registration process, perform YCbCr on the multispectral image and get luminance, blue-difference and red difference chromatic components, and then DWT (discrete wavelet transform) image fusion algorithm based on PCNN (pulse coupled neural networks) is applied to fuse the MRI image and the luminance component (Y). Ultimately, fused image is obtained by inverse YCbCr transform of the new luminance and the old blue-difference and red difference chromatic components back into RGB space. An important feature of the algorithm is to use PCNN because it has the global couple and pulse synchronization characteristics. It has been proven suitable for image processing and successfully employed in image fusion. Our approach is compared with YCbCr, DWT, Contourlet, Curvelet methods. Results show proposed method preserves more spectral features with less spatial distortion.