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Multiscale Bandelets For Image Compression And Fusion

Posted on:2011-06-26Degree:MasterType:Thesis
Country:ChinaCandidate:C X WangFull Text:PDF
GTID:2178360305464086Subject:Circuits and Systems
Abstract/Summary:PDF Full Text Request
Bandelets transformation is a method based on the edge of the image representation, which can be adaptive to track the direction of the image geometry, and then express the image effectively. The second generation Bandelets transform is multi-scale Bandelets, which make full use of the geometric regularity of the image in multiple scales, and can get more sparse data representation than single-scale Bandelets transform. In this paper, based on an investigation into the theory of Bandelets transform and the construction of the second generation Bandelets, exploring researches on multi-scale Bandelets were done in image compression and multi-focus image fusion. Main tasks can be summarized as follows:(1) In the second generation Bandelets transformation, in each subblock by quadtree subdividing, the best geometry flow can obtain by optimizing Lagrange function. For the size of L×L subblock, you can get uniform discrete angle amount of L2-1. The geometry flow direction that can make the Lagrange function smallest is the optimal geometry flow direction. However, the geometry flow obtained by the methods is limited precision. To solve the problem, we present an improved geometry flow optimization method:getting the homogeneous region by image segmenting firstly; then correct the geometry in each four subblocks according to the geometric similarity of flow direction within the homogeneous region, in order to find a more precise geometry flow direction. The algorithm is applied to optical and SAR image compression, in the 1.0-2.0 bit rate, can obtain about 0.1-0.2dB higher than the second generation Bandelets in PSNR. And the keeping of detail information of the reconstructed image is quite to the second generation Bandelets.(2) The geometric flow is optimized in the second Bandelets, which requires O(N2(log2N)2) operations for an image of NxN pixels. With the increasing of N, the complexity increases fast. Based on this problem, an improved algorithm for its fast computation is developed. Perform a lifting wavelet transform for the multi-scale transform. Then we used a fixed size of image partition in each high frequency subband, and rearrange the wavelet coefficients in the high frequency subbands for computing the geometry quickly. At last EBCOT coding is used for coding the obtained coefficients. It is worth noting that our method provides a (log2N)2 times reduction in the running time. And our methods are outperforming JPEG2000 and comparable with 2G-Bandelets for SAR image compression at high bit.(3) In the tasks of image fusion based on the multi-scale analysis, multi-scale analysis tools often need to have redundancy and translational invariance. The use of nonsubsampled wavelet transform, can obtain the corresponding nonsubsampled Bandelets transformation. By the nonsubsampled Bandelets which have translation invariance, the various high-frequency subbands have the rich direction information; so can be used for multi-focus image fusion. A method for multi-focus image fusion which combined with nonsubsampled Bandelets and morphological processing was proposed. As the fusion decision map was treated using the morphological method and the similar approach to the adjacent pixels, more accurate fusion results were obtained. The results show that the fusion method in this paper is much better than the nonsubsampled wavelet fusion method, and clear image of all objects in the scene could be obtained.The research is supported by the National Nature Science Foundation of China (No. 60201029,No.60971112).
Keywords/Search Tags:Multiscale Geometry Analysis, Bandelets, Image Compression, Multi-focus image fusion, Morphological
PDF Full Text Request
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