Multi‐sensor image fusion based on NSST domain I 2 CM

ABSTRACTIn order to improve the accuracy of change detection and reduce the running time, a change detection method based on equal weight image fusion and adaptive threshold in the NSST domain is proposed. First, the logarithmic transformation is used to transform images and the mean filter is applied to the transformed images. The log-ratio method and the mean ratio method are adopted to generate two kinds of difference images. The final difference image is achieved by equal weight image fusion method. Then, an adaptive threshold denoising method based on non-subsampled shearlet transform (NSST) is used to achieve noise reduction. Finally, the k-means clustering algorithm is utilized to get the change detection results. The experimental results show that the proposed algorithm has better change detection performance than the reference algorithms in visual effect and objective parameters.

We present a novel fusion method based on improved pulse-coupled neural networks (PCNN) model in non-subsampled shearlet transform (NSST) domain for wholebody PET/CT images. Firstly, source images are decomposed using NSST into one low-pass sub-band and several highpass sub-bands. Then, an improved PCNN is used in highpass sub-bands where energy of edge and average gradient are as external input and linking strength respectively. Maximum region energy (MRE) and maximum selection (MS) rules are as fusion rules for high-and low-pass sub-bands respectively. Finally, inverse NSST is adopted to produce fused result. Experiments show the superiority of our method.

Combined with the shared similarity among multiple source images and depth of field in a camera, a new image fusion algorithm based on nonsubsampled shearlet transform (NSST) domain is proposed. First, NSST is utilized for decomposition of the source images, the low frequency coefficients is fused by weight votes in the structure-driven regions by using shared similarity and depth of field (SSSID), and then apply larger sum-modified-Laplacian (SML) with depth of field to the high frequency coefficients, finally the fusion image is gained after we do the inverse NSST to the fused coefficients. The algorithm can not only preserve the information of the source images well, but also suppress pixel distortion due to nonlinear operations in transform domain. Experimental results demonstrate that the proposed method outperforms the state-of-the-art transform domain fusion methods on image quality and objective fusion criteria.

There are many artificial parameters when fuse infrared and visible images, to overcome the lack of detail in the fusion image because of the artifacts, a novel fusion algorithm for infrared and visible images that is based on different constraints in non-subsampled shearlet transform (NSST) domain is proposed. There are high bands and low bands of images that are decomposed by the NSST. After analyzing the characters of the bands, fusing the high level bands by the gradient constraint, the fused image can obtain more details; fusing the low bands by the constraint of saliency in the images, the targets are more salient. Before the inverse NSST, the Nash equilibrium is used to update the coefficient. The fused images and the quantitative results demonstrate that our method is more effective in reserving details and highlighting the targets when compared with other state-of-the-art methods.

A fusion algorithm for infrared and visible based on guided filtering and phase congruency in NSST domain

Thick cloud coverage presents a significant challenge for the analysis and application of multi-temporal remote sensing imagery. Although existing cloud removal methods attempt to exploit spatio-spectral-temporal correlations, they often fail to adequately capture cross-scale complementary features due to single-scale reconstruction limitations, resulting in texture degradation and color distortion effects. To address this issue, we propose a multi-scale fully connected tensor network decomposition method guided by the non-subsampled shearlet transform (NSST). The proposed approach first applies NSST in the spatial domain, followed by parameter-adaptive pulse-coupled neural network (PCNN) fusion of each decomposed subband along the temporal dimension to construct a high-quality guidance image. This guidance image is subsequently employed to impose regularization constraints on both high- and low-frequency components of the tensor network decomposition in the NSST domain, enabling a comprehensive representation of multi-scale correlations. To efficiently solve the resulting optimization model, we develop a proximal alternating minimization (PAM)-based algorithm. Extensive experiments on both simulated and real satellite datasets demonstrate that the proposed method consistently outperforms existing approaches, achieving average improvements of 1.2–3.5 dB in PSNR and 0.5–2.0% in SSIM over the best baseline across various cloud coverage scenarios, while maintaining superior detail preservation and color consistency.

Many fusion methods have been proposed to fuse medical image, but these approaches alway lead to fusion image distortion or image information loss. To overcome the above disadvantages, combined with nonsubsampled shearlet transform (NSST) and spiking cortical model (SCM), a new medical image fusion is proposed. First, NSST is utilized for decomposition of the source images. Secondly, the low and high frequency coefficients of NSST are all fused by fired map of improved SCM (ISCM), which motivated by larger summodified-Laplacian (SML). Finally, the fusion image is gained by inverse NSST. The algorithm can both preserve the information of the source images well and suppress pixel distortion. Experimental results demonstrate that the proposed method outperforms the state-of-the-art fusion methods.

Technique for image fusion between gray-scale visual light and infrared images based on NSST and improved RF

Image fusion is a process of integrating two or more images into a single image with more details and information. The different focus images involve different details according to changing the lens of camera. Recently, the two-dimensional discrete wavelet transform (2-D DWT) is applied widely to image processing field. A novel method is proposed for multifocus image fusion based on 2-D DWT. In order to select adapted pixels effectively from the source images, spatial frequency (SF) is adopted to choose maximum value from the high pass subbands, which decomposed using one-level 2-D DWT. Experimental results demonstrate the proposed fusion algorithm can obtain the resultant image well, both in visual effect and objective evaluation criteria.

To overcome the artifact phenomenon caused by the incomplete registration of the source images, a new multi-focus image fusion approach is proposed based on sparse representation and non-subsampled shearlet transform (NSST). Firstly, the source images are decomposed to low- and high-frequency coefficients by NSST. The sparse representation is then adopted to fuse the low-frequency coefficients. For the high-frequency coefficients, a maximum sum-modified-Laplacian (SML) rule is put forward to merge them. Finally, the resultant image is obtained by the inverse NSST on the fused coefficients. Experimental results indicate that the proposed method can achieve satisfied effect compared with various existing image fusion methods.

For the pulse-coupled neural network (PCNN) has an inherent ability to segment images, we present a multisensor image fusion scheme based on PCNN in this paper. The algorithm adopts salience and visibility as two extracted features for each segmented region to determine the fusion weight. Extensive experimental results have demonstrated that the proposed method has extensive application scope and it outperforms the discrete wavelet transform approach, both in visual effect and objective evaluation criteria, particularly when there is movement in the objects or mis-registration of the source images

In order to more accurately realize fusion of remote sensing images, we propose a novel remote sensing images fusion algorithm combining extended non-subsampled shearlet transform (NSST) and modified pulse-coupled neural network (PCNN). Firstly, it makes histogram matching and intensity smoothing and filtering treatment on intensity component and full-color image of multi-spectral image. Secondly, such intensity component and full-color image are decomposed by extended NSST to get corresponding high-frequency and low-frequency coefficients. For low-frequency coefficients, fusion is made by sparse representation; for high-frequency coefficients, a modified pulse-coupled neural network (PCNN) strategy is put forward to process. Finally, the processed result is drawn by inverse transformation of the extended NSST and intensity-hue-saturation inverse transformation. The experimental results show that the proposed algorithm reserves as much spectral information as possible and improve spatial resolution; its visual effects and objective indexes are better than other classical fusion algorithms.

In multi-scale geometric analysis (MGA)-based fusion methods for infrared and visible images, adopting the same representation for the two types of images will result in the non-obvious thermal radiation target in the fused image, which can hardly be distinguished from the background. To solve the problem, a novel fusion algorithm based on nonlinear enhancement and non-subsampled shearlet transform (NSST) decomposition is proposed. Firstly, NSST is used to decompose the two source images into low- and high-frequency sub-bands. Then, the wavelet transform (WT) is used to decompose high-frequency sub-bands to obtain approximate sub-bands and directional detail sub-bands. The “average” fusion rule is performed for fusion for approximate sub-bands. And the “max-absolute” fusion rule is performed for fusion for directional detail sub-bands. The inverse WT is used to reconstruct the high-frequency sub-bands. To highlight the thermal radiation target, we construct a non-linear transform function to determine the fusion weight of low-frequency sub-bands, and whose parameters can be further adjusted to meet different fusion requirements. Finally, the inverse NSST is used to reconstruct the fused image. The experimental results show that the proposed method can simultaneously enhance the thermal target in infrared images and preserve the texture details in visible images, and which is competitive with or even superior to the state-of-the-art fusion methods in terms of both visual and quantitative evaluations.

This paper proposes a novel image fusion method based on weighted non-negative matrix factorization (WNMF) and region segmentation. Firstly, visible and infrared images are endowed different weight, and are taken as original data in WNMF analysis, by which the feature image can be extracted. Then the target regions in infrared image are segmented by region growing method and are extracted, then the pixels of the target regions replace the pixels of corresponding position in feature image. At last, the fusion image is obtained. The experimental results show that the method is better than previous ones, both in visual effect and objective evaluation criteria.

The fusion of infrared and visible images may result in low contrast, which is unsuitable for observation by human eyes. Thus, we propose a contrast-enhanced fusion algorithm with nonsubsampled shearlet transform (NSST) frames, in which the NSST is first employed to decompose each of the source images into one low frequency sub-band and a series of high frequency sub-bands. To improve the fusion performance, we designed two measures for fusion of the low frequency and the high frequency: the low frequency is divided into salient and nonsalient regions in accordance with the human visual system to improve the global contrast by targeted fusion and the high frequency requires a local contrast fusion strategy. Finally, the merged sub-bands are constructed according to the selection principles, and the final fused image is produced by applying the inverse NSST on these merged sub-bands. Experimental results demonstrate the effectiveness and superiority of the proposed method over the state-of-the-art fusion methods in terms of both visual effect and objective evaluation results.