Image fusion is a very practical technology that can be applied in many fields, such as medicine, remote sensing and surveillance. An image fusion method using multi-scale decomposition and joint sparse representation is introduced in this paper. First, joint sparse representation is applied to decompose two source images into a common image and two innovation images. Second, two initial weight maps are generated by filtering the two source images separately. Final weight maps are obtained by joint bilateral filtering according to the initial weight maps. Then, the multi-scale decomposition of the innovation images is performed through the rolling guide filter. Finally, the final weight maps are used to generate the fused innovation image. The fused innovation image and the common image are combined to generate the ultimate fused image. The experimental results show that our method’s average metrics are: mutual information ( $MI$ )—5.3377, feature mutual information ( $FMI$ )—0.5600, normalized weighted edge preservation value ( $Q^{AB/F}$ )—0.6978 and nonlinear correlation information entropy ( $NCIE$ )—0.8226. Our method can achieve better performance compared to the state-of-the-art methods in visual perception and objective quantification.

In this paper, the propagation characteristics of a ring Airy vortex beam (RAVB) in slant atmospheric turbulence are studied. The effects of some key parameters, such as the truncation factor, the zenith angle, and the propagation distance, on the propagation characteristics of RAVB are analyzed. The results show that the beam quality becomes worse with an increase in the propagation distance, zenith angle, and truncation factor. When the propagation distance exceeds 3 km in turbulence, the value of the aperture-averaged scintillation index will reach the peak value. On the other hand, the mean signal-to-noise ratio of this beam will be affected by the propagation distance. The beam quality can be improved by increasing the topological charges. When the truncation factor is 0.1, RAVB performs best when the distance is long.

Recently, the rapid development of the Internet of Things has contributed to the generation of telemedicine. However, online diagnoses by doctors require the analyses of multiple multi-modal medical images, which are inconvenient and inefficient. Multi-modal medical image fusion is proposed to solve this problem. Due to its outstanding feature extraction and representation capabilities, convolutional neural networks (CNNs) have been widely used in medical image fusion. However, most existing CNN-based medical image fusion methods calculate their weight maps by a simple weighted average strategy, which weakens the quality of fused images due to the effect of inessential information. In this paper, we propose a CNN-based CT and MRI image fusion method (MMAN), which adopts a visual saliency-based strategy to preserve more useful information. Firstly, a multi-scale mixed attention block is designed to extract features. This block can gather more helpful information and refine the extracted features both in the channel and spatial levels. Then, a visual saliency-based fusion strategy is used to fuse the feature maps. Finally, the fused image can be obtained via reconstruction blocks. The experimental results of our method preserve more textual details, clearer edge information and higher contrast when compared to other state-of-the-art methods.

High-resolution multi-spectral images are desired for applications in remote sensing. However, multi-spectral images can only be provided in low resolutions by optical remote sensing satellites. The technique of pan-sharpening wants to generate high-resolution multi-spectral (MS) images based on a panchromatic (PAN) image and the low-resolution counterpart. The conventional deep learning based pan-sharpening methods process the panchromatic and the low-resolution image in a feedforward manner where shallow layers fail to access useful information from deep layers. To make full use of the powerful deep features that have strong representation ability, we propose a two-path network with feedback connections, through which the deep features can be rerouted for refining the shallow features in a feedback manner. Specifically, we leverage the structure of a recurrent neural network to pass the feedback information. Besides, a power feature extraction block with multiple projection pairs is designed to handle the feedback information and to produce power deep features. Extensive experimental results show the effectiveness of our proposed method.

Vortex beam has potential applications in free space optical communication because of its capacity of data transmission. Therefore, it is necessary to study the propagation characteristics of vortex beams in atmospheric turbulence. When the vortex beam propagates in the atmospheric turbulence the beam drift will occur, which has a great influence on the free space optical communication. In this paper, the beam drift of vortex beams with coma and spherical aberration transmitted in atmospheric turbulence is studied by using multi-phase screen and Fourier transform method. The numerical results show that as the transmission distance increases, the effects of both coma and spherical aberration on the beam drift are enhanced. The larger the transmission zenith angle and the coma coefficients, the greater the beam drift of the vortex beam is. However, the beam drift decreases with spherical aberration coefficient increasing. When the zenith angle and the transmission distance are both unchanged, the beam drift of the both vortex beams decrease with topological charges increasing. The influence of coma aberration on beam drift is bigger than that of spherical aberration.