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.
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