Band-pass Sub-band Coefficients Research Articles

Multifocus image fusion scheme based on the multiscale curvature in nonsubsampled contourlet transform domain

An efficient multifocus image fusion scheme in nonsubsampled contourlet transform (NSCT) domain is proposed. Based on the property of optical imaging and the theory of defocused image, we present a selection principle for lowpass frequency coefficients and also investigate the connection between a low-frequency image and the defocused image. Generally, the NSCT algorithm decomposes detail image information indwells in different scales and different directions in the bandpass subband coefficient. In order to correctly pick out the prefused bandpass directional coefficients, we introduce multiscale curvature, which not only inherits the advantages of windows with different sizes, but also correctly recognizes the focused pixels from source images, and then develop a new fusion scheme of the bandpass subband coefficients. The fused image can be obtained by inverse NSCT with the different fused coefficients. Several multifocus image fusion methods are compared with the proposed scheme. The experimental results clearly indicate the validity and superiority of the proposed scheme in terms of both the visual qualities and the quantitative evaluation.

Fusion of Infrared and Visible Images Based on Pulse Coupled Neural Network and Nonsubsampled Contourlet Transform

This paper presents a new fusion algorithm which can effectively solve the problem that has unobvious infrared target and low contrast in infrared and visible image fusion. This paper's innovation point is its fusion rule. Other algo- rithms' fusion rules usually use pulse coupled neural network (PCNN) and region characteristics to select low frequency or bandpass subband coefficients. The proposed algorithm innovatively applies improved PCNN and region characteris- tics to the selection of both low frequency and bandpasssubband coefficients in nonsubsampled contourled transform (NSCT) domain. First, the subband coefficients of original image are obtained by NSCT. Then, the decomposed subband coefficients are processed by using PCNN, whose fire mapping images are obtained. The method of region standard de- viation isused to choose the fusion coefficients of fire mapping image, which satisfies to get more image information in low frequency part. For bandpass subband coefficients' fire mapping image, the method based on region energy is adopted for the fusion coefficients, which makes the bandpass part captures more energy. Finally, the fused image can be obtained by inverse transform of NSCT. Compared with typical wavelet-based, NSCT-based, NSCT-PCNN based fusion algorithms, experiment shows that the new proposed algorithm improves the fused image'sobjective evaluation index sig- nificantly, obtainsa prominent infrared target and better fusion image quality.

A novel image fusion algorithm based on nonsubsampled shearlet transform

To overcome the shortcoming of traditional image fusion method based on multi-scale transform, a novel adaptive image fusion algorithm based on nonsubsampled shearlet transform (NSST) is proposed. Firstly, the NSST is utilized to decompose the source images on various scales and in different directions, and the low frequency sub-band and bandpass sub-band coefficients are obtained. Secondly, for the low frequency sub-band coefficients, the singular value decomposition method in the gradient domain is used to estimate the local structure information of image, and an adaptive ‘weighted averaging’ fusion rule based on the sigmoid function and the extracted features is presented. To improve the quality of fused image, a novel sum-modified-Laplacian (NSML), which can extract more useful information from source images, is employed as the measurement to select bandpass sub-band coefficients. Finally, the fused image is obtained by performing the inverse NSST on the combined coefficients. The proposed fusion method is verified on several sets of multi-source images, and the experimental results show that the proposed approach can significantly outperform the conventional image fusion methods in terms of both objective evaluation criteria and visual quality.

A Novel Image Fusion Method Based on FRFT-NSCT

Nonsubsampled Contourlet transform (NSCT) has properties such as multiscale, localization, multidirection, and shift invariance, but only limits the signal analysis to the time frequency domain. Fractional Fourier transform (FRFT) develops the signal analysis to fractional domain, has many super performances, but is unable to attribute the signal partial characteristic. A novel image fusion algorithm based on FRFT and NSCT is proposed and demonstrated in this paper. Firstly, take FRFT on the two source images to obtain fractional domain matrices. Secondly, the NSCT is performed on the aforementioned matrices to acquire multiscale and multidirection images. Thirdly, take fusion rule for low-frequency subband coefficients and directional bandpass subband coefficients to get the fused coefficients. Finally, the fused image is obtained by performing the inverse NSCT and inverse FRFT on the combined coefficients. Three modes images and three fusion rules are demonstrated in the proposed algorithm test. The simulation results show that the proposed fusion approach is better than the methods based on NSCT at the same parameters.

Similarity-based multimodality image fusion with shiftable complex directional pyramid

For multimodality images, a novel fusion algorithm based on the shiftable complex directional pyramid transform (SCDPT) is proposed in this paper. As well, with the aid of the structural similarity (SSIM) index, a ‘similarity-based’ idea is employed to distinguish regions with ‘redundant’ or ‘complementary’ information between source imagers before the SCDPT coefficients are merged. A ‘weighted averaging’ scheme for regions with ‘redundant’ information and a ‘selecting’ scheme for regions with ‘complementary’ information are then employed, respectively. When merging the low-pass subband coefficients, the SSIM index in spatial domain (SP-SSIM) is employed as similarity measure, and three types of regions are thus determined. Especially, for regions with similar intensity values but different intensity changing directions between source images, a ‘selecting’ scheme based on gradient and energy is proposed. When merging the directional band-pass subband coefficients, the SSIM index in complex wavelet domain (CW-SSIM) is employed as similarity measure. With the CW-SSIM index, not only the magnitude information but also the phase information of SCDPT coefficients can be employed. Compared to the traditional energy matching (EM) index based fusion methods, the proposed method can better deal with ‘redundant’ and ‘complementary’ information of source images. In addition, because of the shift-invariance of the SCDPT and the CW-SSIM index, the proposed fusion algorithm performs well even if the input images are not well registered. Several sets of experimental results demonstrate the validity and feasibility of the proposed method in terms of both visual quality and objective evaluation.

Multifocus image fusion scheme based on features of multiscale products and PCNN in lifting stationary wavelet domain

Multifocus image fusion aims at overcoming imaging cameras's finite depth of field by combining information from multiple images with the same scene. For the fusion problem of the multifocus image of the same scene, a novel algorithm is proposed based on multiscale products of the lifting stationary wavelet transform (LSWT) and the improved pulse coupled neural network (PCNN), where the linking strength of each neuron can be chosen adaptively. In order to select the coefficients of the fused image properly with the source multifocus images in a noisy environment, the selection principles of the low frequency subband coefficients and bandpass subband coefficients are discussed, respectively. For choosing the low frequency subband coefficients, a new sum modified-Laplacian (NSML) of the low frequency subband, which can effectively represent the salient features and sharp boundaries of the image in the LSWT domain, is an input to motivate the PCNN neurons; when choosing the high frequency subband coefficients, a novel local neighborhood sum of Laplacian of multiscale products is developed and taken as one type of feature of high frequency to motivate the PCNN neurons. The coefficients in the LSWT domain with large firing times are selected as coefficients of the fused image. Experimental results demonstrate that the proposed fusion approach outperforms the traditional discrete wavelet transform (DWT)-based, LSWT-based and LSWT–PCNN-based image fusion methods even though the source image is in a noisy environment in terms of both visual quality and objective evaluation.