The fusion of infrared and visible images together can fully leverage the respective advantages of each, providing a more comprehensive and richer set of information. This is applicable in various fields such as military surveillance, night navigation, environmental monitoring, etc. In this paper, a novel infrared and visible image fusion method based on sparse representation and guided filtering in Laplacian pyramid (LP) domain is introduced. The source images are decomposed into low- and high-frequency bands by the LP, respectively. Sparse representation has achieved significant effectiveness in image fusion, and it is used to process the low-frequency band; the guided filtering has excellent edge-preserving effects and can effectively maintain the spatial continuity of the high-frequency band. Therefore, guided filtering combined with the weighted sum of eight-neighborhood-based modified Laplacian (WSEML) is used to process high-frequency bands. Finally, the inverse LP transform is used to reconstruct the fused image. We conducted simulation experiments on the publicly available TNO dataset to validate the superiority of our proposed algorithm in fusing infrared and visible images. Our algorithm preserves both the thermal radiation characteristics of the infrared image and the detailed features of the visible image.

The injection scheme provides an efficient way for CS- and MRA-based pansharpening approaches. Within this paradigm, the estimation of injection gains is one of the keys to pansharpening outcomes, which has attracted much attention in the community. Most of the existing models are derived from the regression methodology. Hence the reference is indispensable for the estimation. However, the reference is unavailable in practice, and therefore the estimation is usually performed at a degraded scale. This paper is devoted to the estimation of injection gains without reference. A hybrid-scale (HS) estimation which involves both of the high-resolution and low-resolution data is proposed, along with three HS models. The proposed method features a context-based and fast implementation with fewer tunable parameters. Experimental results show that the HS models yield more accurate and robust results compared with the typical regression-based models, and they are also competitive with the state-of-the-art approaches.

The ventilator is an important means of assisting and even replacing spontaneous breathing and has an unshakable position in the medical field. This paper mainly studies the method of fuzzy adaptive PID control and traditional PID control of mechanical ventilation pressure based on the dual lung model. In this study, a mathematical model of the lungs during breathing is established and simulated. Furthermore, the experimental platform was built based on a mechanical ventilation system, and the classic PID control and fuzzy adaptive PID control were realized by programming the single chip. Finally, debugging and experimentation were carried out on the experimental platform, and the experimental results were analyzed. The study of the two-lung model and the two control algorithms not only provides a simple and easy test method, but also has guiding significance for the development of mechanical ventilation control mode.

Pansharpening is a fusion technique that merges panchromatic (PAN) and multispectral (MS) images in order to enhance the spatial resolution of MS. Over the last two decades, a variety of approaches have been proposed either based on component substitution or multiresolution analysis. This paper proposes a new pansharpening algorithm using the morphological lifting transform (MLT) with region-based injection. The lifting structure with morphological operators provides a flexible tool to construct new morphological wavelet transforms. The morphological operators compared to the linear operators can extract the spatial details more effectively. In addition, an adaptive injection gain based on minimum mean squared error (MMSE) with k-means clustering is adopted. The proposed algorithm is validated in two datasets: Pléiades and Landsat 7. Experimental results show that the proposed method has advantages on enhancing the spatial details while retaining the spectral information. And it outperforms other state-of-the-art methods.

The fusion of Panchromatic (PAN) and Hyperspectral image (HSI) aims at improving resolution in spatial and spectral domain simultaneously. Multiresolution analysis is a widely used method for Mutispectral or HSI pansharpening. However, only detail information from PAN is considered while ignoring the detail information from HSI. In this paper, an improved approach based on multiresolution analysis is proposed, which extracts detail information from both PAN and HSI by choosing optimal multiresolution layers. Another contribution is that we discuss the weight when fusing the detail information. The experimental results demonstrate that the proposed method can provide better quality metrics and visual effects when compared with some existing methods.