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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Patent•
21 Oct 2009
TL;DR: In this paper, a no-reference natural image quality evaluation method based on Contourlet transform is proposed, which is mainly used to solve the problem of differences between subjective and objective quality evaluation values in case of an unknown original image.
Abstract: The invention discloses a no-reference natural image quality evaluation method based on Contourlet transform. The method is mainly used to solve the problem of differences between subjective and objective quality evaluation values in case of an unknown original image. The method comprises the following steps: performing multi-scale and multi-direction subband decomposition on a distorted image by Contourlet transform; constructing a joint histogram of Contourlet coefficients and predictive coefficients in each decomposed subband by utilizing the correlation between Contourlet coefficients and coefficients in the field; setting a threshold value to partition the joint histogram according to a characteristic that the Contourlet coefficients decreases by degrees as the image scale increases in a Contourlet domain; selecting a characteristic regions which can represent image quality from the partitioned histogram, and performing a nonlinear transformation on the regions to represent the subband quality; and finally obtaining the evaluation value of image quality by a weighted sum of the obtained quality value of each subband. The method has the advantage of being applicable to various distorted images and having objective evaluation well coincided with subjective evaluation, and can be used to evaluate an image processing method effectively.
10 citations
TL;DR: The proposed algorithm is robust to many image attacks and suitable for copyright protection applications and superior of proposed algorithm to JPEG is observed in terms of reduced blocking artifacts.
Abstract: This paper presents a new compression technique and image watermarking algorithm based on Contourlet Transform (CT). For image compression, an energy based quantization is used. Scalar quantization is explored for image watermarking. Double filter bank structure is used in CT. The Laplacian Pyramid (LP) is used to capture the point discontinuities, and then followed by a Directional Filter Bank (DFB) to link point discontinuities. The coefficients of down sampled low pass version of LP decomposed image are re-ordered in a pre-determined manner and prediction algorithm is used to reduce entropy (bits/pixel). In addition, the coefficients of CT are quantized based on the energy in the particular band. The superiority of proposed algorithm to JPEG is observed in terms of reduced blocking artifacts. The results are also compared with wavelet transform (WT). Superiority of CT to WT is observed when the image contains more contours. The watermark image is embedded in the low pass image of contourlet decomposition. The watermark can be extracted with minimum error. In terms of PSNR, the visual quality of the watermarked image is exceptional. The proposed algorithm is robust to many image attacks and suitable for copyright protection applications.
10 citations
TL;DR: The hybrid density model is applied and the proposed approach outperforms previous works involved in the paper with the better despeckling result and edge preservation.
Abstract: A method for synthetic aperture radar (SAR) image despeckling based on a probabilistic generative model in nonsubsampled contourlet transform (NSCT) domain was proposed. The shrinkage estimator in NSCT domain consists of a new type of likelihood ratio and prior ratio, both of which are dependent on the estimated masks for the NSCT coefficients. While the previous probabilistic approaches are restricted to parametric models, the limitation is eliminated and the hybrid density model is applied in this paper. The suggested approach does not make heavy assumptions on the NSCT coefficient distribution, so that it can handle complex NSCT coefficient structures. The likelihood ratio is composed of the hybrid density, and the prior ratio is equipped with the selective neighborhood systems to enhance the detail information. The method can effectively adapt the shrinkage estimator to the redundancy property of the NSCT. The proposed approach was applied to real SAR images despeckling and compared through the SAR image vision effect, the equivalent number of looks, and the edge sustain index. Experimental results show that the proposed approach outperforms previous works involved in the paper with the better despeckling result and edge preservation.
10 citations
TL;DR: Experimental results show the superiority of the proposed methodology in terms of both qualitative and quantitative measures, which also indicates that fused images contain enriched diagnostic information that can aid the detection of tumors and edema.
Abstract: This paper proposes a multispectral magnetic resonance imaging (MRI) image fusion scheme for improved visualization of anatomical and pathological information of meningioma (MG) brain tumors that combines the contourlet transform and fuzzy statistics. The proposed fusion technique mainly targets the tumor and its surrounding hyperintense (edema) region, which leads to improved brain imaging informatics for radiologists. The developed methodology mainly consists of the contourlet transform for multiscale and directional decomposition, fuzzy entropy for fusing approximation coefficients, and region-based fuzzy energy for fusing detailed coefficients of two input images with the same orientations. Two fusion rules are established here in order to fuse corresponding lower- and higher-frequency subbands of images. The proposed methodology is applied to five various combinations (such as T1-weighted and T2-weighted, T1 post-contrast and T2-weighted etc.) generated from four modalities of MRI images (T1-weighted, T1 post-contrast, T2-weighted, and fluid-attenuated inversion recovery (FLAIR)). A total of 150 MRI images (30 images from each of five combinations) are considered from 20 cases of MG brain tumors. A quantitative evaluation of the proposed method is performed in terms of three performance measures. The performance is compared with that of existing medical image fusion techniques tested on the same dataset. Experimental results show the superiority of the proposed methodology in terms of both qualitative and quantitative measures, which also indicates that fused images contain enriched diagnostic information that can aid the detection of tumors and edema. A fusion of post-contrast T1-weighted MRI images with FLAIR and T2-weighted MRI images provided clinically relevant information.
10 citations
01 Nov 1991
TL;DR: A Laplacian image pyramid is employed for coding the prediction error image of a hybrid image sequence coder and a tree growing algorithm for bit assignment within the pyramid is introduced for maximization of a quality criterion in the overall reconstructed image.
Abstract: Pyramid image coding is an important class of encoding for progressive transmission. Image data is represented in a hierarchical structure and is transmitted in the order of decreasing significance. This property can be exploited for image sequence coding. Pyramid encoding allows adaptation to the statistics of prediction error images and to the available residual data rate. In this paper a Laplacian image pyramid is employed for coding the prediction error image of a hybrid image sequence coder. A tree growing algorithm for bit assignment within the pyramid is introduced for maximization of a quality criterion in the overall reconstructed image. Experiments show reasonable image quality even for low bit rates as 8 kbit/s.© (1991) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
10 citations