Zhancheng Zhang

Image fusion with contextual statistical similarity and nonsubsampled shearlet transform

Image fusion has the capability to integrate useful information from source images into a more comprehensive image. How to obtain the effective representation of source images is a key step to image fusion. Due to the loss of the dependence of coefficients, most of traditional multi-scale decomposition-based image fusion methods suffer from an inaccurate image representation. To solve this problem, a novel image fusion method with contextual statistical similarity in nonsubsampled shearlet transform (NSST) is presented. The key contributions include: 1) the dependence of NSST coefficients is captured by the contextual hidden Markov model (CHMM); 2) the contextual statistical similarity of coefficients is proposed; 3) an effective fusion rule based on the characteristic of CHMM is developed for high-frequency subbands in NSST domain. By the visual analysis and quantitative evaluations on experimental results, the superiority of the proposed method is demonstrated.

Adaptive multistrategy image fusion method

Abstract. Design of fusion rule is an important step in fusion process. Traditional single fusion rules are inflexible when they are being used to fuse feature-rich images. To address this problem, an adaptive multistrategy image fusion method is proposed. Its flexibility lies in the combination of a choose-max strategy and a weighted average strategy. Moreover, the region-based characteristics and the shift-invariant shearlet transform (SIST)-based activity measures are proposed to guide the selection of strategies. The key points of our method are: (1) Window-based features are extracted from the source images. (2) Use of the fuzzy c-means clustering algorithm to construct a region map in the feature difference space. (3) The dissimilarity between corresponding regions is employed to quantify the characteristic of regions and the local average variance of the SIST coefficients are considered as activity measures to evaluate the salience of the related coefficient. (4) The adaptive multistrategy selection scheme is achieved by a sigmoid function. Experimental results show that the proposed method is superior to the conventional image fusion methods both in subjective and objective evaluations.

Image Fusion Using Region Segmentation and Sigmoid Function

In this paper, a novel region segmentation and sigmoid function based image fusion method is proposed. Different from the traditional fusion approaches limiting to a single fusion strategy, the proposed method is designed with an adaptive multi-strategy fusion rule (AMFR). In our method, the source images are decomposed into low frequency sub bands and high frequency sub bands via the shift-invariant Shear let transform (SIST). The low frequency sub bands are fused by the choose-max scheme and the high frequency sub bands are fused by the AMFR based on a sigmoid function. The AMFR includes the choose-max scheme and the weighted average scheme, which of them is selected is determined by the sigmoid function. The fused sub bands are merged to reconstruct fused image by using inverse SIST. Experiments conducted on various types of source images demonstrate that our approach achieve superior results compared with the existing fusion methods in both visual presentation and objective evaluation.

Statistical Modeling of Multi-modal Medical Image Fusion Method Using C-CHMM and M-PCNN

In this paper, a new Contextual hidden Markov Model (CHMM) and modified Pulse Coupled Neural Network (M-PCNN) based fusion approach in the Contour domain is proposed for multi-modal medical image fusion. The Contour transform as an emerging multi-scale multi-direction geometric analyzing tool can provide an efficient and flexible representation of images, e.g. edges, contours and textures, which overcomes the drawback of the 2-D wavelet transform. Considering the powerful advantages for statistical modeling and processing of Contour let coefficients by HMM, the context information integrated with HMM is established to construct a comprehensive statistical correlative model, which can collectively capture persistence across scales, directional selectivity within scales and energy concentration in the spatial neighborhood of the high-frequency sub-band coefficients. Low-frequency sub-band coefficients are fused by the magnitude maximum rule, and a modified PCNN is developed where the linking strength of each neuron is determined by the normalized region energy of Edge PDF and modified spatial frequency is employed as the image feature to motivate M-PCNN. The high-frequency directional sub-band coefficients are selected by total pulse number maximum strategy. The experimental results demonstrate that the presented fusion method can further improve fusion image quality and visual effects.

Multi-focus image fusion using HOSVD and edge intensity

The edge intensity metric is proposed.The relationship between the patch energy and the shrink factor of the sigmoid function is constructed.Multi-strategy fusion rule based on edge intensity and patch energy is designed.The proposed fusion method is performed in the HOSVD domain. The purpose of multi-focus image fusion is to integrate the partially focused images into one single image which is focused everywhere. To achieve this purpose, higher order singular value decomposition (HOSVD) and edge intensity (EDI) based multi-focus image fusion method is proposed. The main characteristics of the proposed method includes: 1. an effective and robust sharpness measure based on edge intensity is presented; 2. considering the fact that HOSVD is an effective data-driven decomposition technique and shows the outstanding ability in the representation of high-dimensional data, it is used to decompose multi-focus images; and 3. a multi-strategy fusion rule based on sigmoid function is used to fuse the decomposition coefficients. Furthermore, several experiments are conducted to verify the superiority of the proposed fusion framework in terms of visual and statistical analyses.

A New Pan-Sharpening Method Using Statistical Model and Shearlet Transform

ABSTRACT A statistical model-based pan-sharpening method under the framework of Shearlet transform is developed in this paper. The approximation coefficients of multi-spectral (MS) image are used for reconstruction level of fusion algorithm. The weighted average method is used to fuse the high-pass subbands. The weights are estimated by a statistical model in which a new objective function is proposed. It is partitioned into two parts: the first term maximizes the local variance of the high-pass subband of fused image, which means the details can be injected into MS image as much as possible. The second term is the correlation restriction between the high-pass subband of fused image and that of MS image, which is indirectly beneficial to avoid generating the unreasonable high-frequency information. Finally, the pan-sharpening result is obtained by performing the inverse Shearlet transform. A series of experiments conducted on real remote sensing images demonstrate the effectiveness of our model.

Contextual Information Driven Multi-modal Medical Image Fusion

ABSTRACT To utilize context correlation between coefficients in contourlet domain, a novel multi-modal medical image fusion method based on contextual information is proposed. First, the context information of contourlet coefficients are calculated to capture the strong dependencies of coefficients. Second, hidden Markov model based on context information for the contourlet coefficients (C-CHMM) is constructed to describe the characteristics of medical image in a small number of parameters. Further, low-pass subband coefficients are combined by magnitude maximum rule and high-pass subband coefficients are fused by a new C-CHMM driven multi-strategy fusion rule. Finally, the fused image is obtained by inverse contourlet transform. Experimental results demonstrate that the proposed fusion method can effectively suppress the color distortion and provide a better fusion quality compared with some typical fusion methods.

Image fusion based on shift invariant shearlet transform and stacked sparse autoencoder

Stacked sparse autoencoder is an efficient unsupervised feature extraction method, which has excellent ability in representation of complex data. Besides, shift invariant shearlet transform is a state-of-the-art multiscale decomposition tool, which is superior to traditional tools in many aspects. Motivated by the advantages mentioned above, a novel stacked sparse autoencoder and shift invariant shearlet transform-based image fusion method is proposed. First, the source images are decomposed into low- and high-frequency subbands by shift invariant shearlet transform; second, a two-layer stacked sparse autoencoder is adopted as a feature extraction method to get deep and sparse representation of high-frequency subbands; third, a stacked sparse autoencoder feature-based choose-max fusion rule is proposed to fuse the high-frequency subband coefficients; then, a weighted average fusion rule is adopted to merge the low-frequency subband coefficients; finally, the fused image is obtained by inverse shift invariant shearlet transform. Experimental results show the proposed method is superior to the conventional methods both in terms of subjective and objective evaluations.

Multi-focus image fusion using quaternion wavelet transform

To avoid the introduction of false information during the fusion progress, a novel multi-focus image fusion method is proposed in quaternion wavelet transform domain. To obtain the dependency in different high frequency subbands, a quaternion wavelet contextual hidden Markov model (Q-CHMM) is established for modeling quaternion wavelet coefficients. And for better image representations, several features are proposed by analyzing the transform coefficients, phases of coefficients and the statistical attribution of coefficients. Different from the traditional fusion methods basing on a single feature, a comprehensive feature is constructed by using quaternion matrix to fuse the high frequency subbands. Experimental results demonstrate that the proposed method possess good fusion performance.