Spatial heterodyne spectral technology is a hyperspectral remote sensing technique. With the improvement in detection accuracy, new demands have emerged for denoising methods in spatial heterodyne interferograms. Convolutional neural networks (CNNs) is a currently hot research topic. they have unique advantages in extracting abstract features from data. In recent years, CNNs have demonstrated outstanding performance in the field of image denoising. In this paper, we construct a Spatial heterodyne interferograms denoise CNN(SHI-DnCNN) using batch normalization and residual learning. We utilize the trained SHI-DnCNN to denoise spatial heterodyne interferograms contaminated with Gaussian noise. The results show that SHI-DnCNN exhibits excellent Gaussian noise denoising capability for spatial heterodyne interferograms. Furthermore, we evaluate the denoising results using PSNR, SSIM, and residual spectra, further confirming the superior denoising performance of SHI-DnCNN. This work provides a new and effective solution for denoising spatial heterodyne interferograms.
Perovskite solar cells have been widely used because of their high photoelectric conversion efficiency. It has been shown that the light-trapping structure can enhance absorption and reduce the additional light energy loss. Therefore, we propose a feasible method to construct pit array texture structures at the top and bottom of the glass respectively, and deposit solar cell materials on the substrate in turn. The primary mechanism of absorption enhancement of three different texture cells is simulated by the finite difference time domain method, and their limit efficiency is calculated and compared with planar devices. The results show that the perovskite solar cell with a double-sided textured structure has better anti-reflection and light capture characteristics. The light absorption is significantly improved in the 300-800 nm wavelength range. Compared with planar perovskite solar cells, the reflection is reduced by about 55% and the ultimate efficiency is increased by more than 8%. The textured structure can be used in various solar cell devices to improve cell performance.
As the third generation of solar cells, perovskite solar cells have attracted extensive attention because of their relatively simple structure and high theoretical photoelectric conversion efficiency. However, there are still some problems in thin film perovskite solar cells, such as severe light energy loss, less light absorption in the near-infrared region and part of visible wavelength. Without increasing the thickness of the perovskite absorption layer, using the plasmon effect of metal nanoparticles is an effective method to reduce the light energy loss and enhance the light absorption of solar cells. In this paper, the light absorption of two kinds of perovskite solar cells with and without gold nanobipyramids array structure are simulated by the finite difference time domain method. The results show that the light absorption efficiency of the absorption layer of perovskite solar cells with gold nanobipyramids array structure is improved by 42.93%. Further research shows that the local surface plasmon resonance of gold nanobipyramids can produce substantial electric field enhancement, which may be why gold nanobipyramids enhance the light absorption and improve the efficiency of perovskite solar cells. This study provides a new idea to improve the photoelectric conversion efficiency of perovskite solar cells.
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