Mastcam image enhancement using estimated point spread functions

C. Kwan, Minh Dao, Bryan Chou, L. Kwan, B. Ayhan
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引用次数: 26

Abstract

This paper summarizes some preliminary results in enhancing the spatial resolution of the left Mastcam images of the Mars Science Laboratory (MSL) onboard the Mars rover Curiosity. There are two multispectral Mastcam imagers, having 9 bands in each. The left imager has wide field of view, but low resolution whereas the right imager is just the opposite. Our goal is to investigate whether we can use the right Mastcam images to enhance the left Mastcam images. We first estimate the point spread function (PSF) between a pair of left and right Mastcam images using a sparsity based approach. We then apply the estimated PSF to enhance the other left images. Actual Mastcam images were used in our experiments. Preliminary results indicated that the image enhancement performance is mixed. That is, we can achieve good results in some left images and poor results in others. The mixed results point to a new direction for a future study, which involves the use of deep learning based on convolutional neural network (CNN) for PSF estimation and robust deblurring.
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使用估计点扩展函数的Mastcam图像增强
本文总结了“好奇号”火星探测器火星科学实验室(MSL)左侧桅杆摄像头图像空间分辨率提高的一些初步成果。有两个多光谱Mastcam成像仪,每个有9个波段。左边的成像仪视野宽,但分辨率低,而右边的成像仪正好相反。我们的目标是研究是否可以使用右侧的Mastcam图像来增强左侧的Mastcam图像。我们首先使用基于稀疏度的方法估计一对左右Mastcam图像之间的点扩散函数(PSF)。然后,我们应用估计的PSF来增强其他左侧图像。我们的实验使用了真实的Mastcam图像。初步结果表明,图像增强效果参差不齐。也就是说,我们可以在一些左图像中获得好的结果,而在另一些左图像中获得不好的结果。混合结果指出了未来研究的新方向,该研究涉及使用基于卷积神经网络(CNN)的深度学习进行PSF估计和鲁棒去模糊。
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