Hui Mei, Wu Yong, Li Weiqian, Liu Ming, Dong Liquan, Kong Lingqin, Zhao Yuejin
Opt Express. 2020 Mar 30;28(7):9929-9943. doi: 10.1364/OE.387623.
Optical synthetic aperture imaging systems, which consist of in-phase circular sub-mirrors, can greatly improve the spatial resolution of a space telescope. Due to the sub-mirrors' dispersion and sparsity, the modulation transfer function is decreased significantly compared to a fully filled aperture system, which causes obvious blurring and loss of contrast in the collected image. Image restoration is the key to get the ideal clear image. In this paper, an appropriative non-blind deconvolution algorithm for image restoration of optical synthetic aperture systems is proposed. A synthetic aperture convolutional neural network (CNN) is trained as a denoiser prior to restoring the image. By improving the half-quadratic splitting algorithm, the image restoration process is divided into two subproblems: deconvolution and denoising. The CNN is able to remove noise in the gradient domain and the learned gradients are then used to guide the image deconvolution step. Compared with several conventional algorithms, scores of evaluation indexes of the proposed method are the highest. When the signal to noise ratio is 40 dB, the average peak signal to noise ratio is raised from 23.7 dB of the degraded images to 30.8 dB of the restored images. The structural similarity index of the results is increased from 0.78 to 0.93. Both quantitative and qualitative evaluations demonstrate that the proposed method is effective.
由同相圆形子镜组成的光学合成孔径成像系统，能够极大地提高空间望远镜的空间分辨率。由于子镜的色散和稀疏性，与全填充孔径系统相比，调制传递函数显著降低，这导致采集图像中出现明显的模糊和对比度损失。图像复原是获得理想清晰图像的关键。本文提出了一种适用于光学合成孔径系统图像复原的非盲反卷积算法。在图像复原之前，训练一个合成孔径卷积神经网络（CNN）作为去噪器。通过改进半二次分裂算法，将图像复原过程分为两个子问题：反卷积和去噪。CNN能够在梯度域中去除噪声，然后将学习到的梯度用于指导图像反卷积步骤。与几种传统算法相比，该方法的多个评价指标得分最高。当信噪比为40dB时，平均峰值信噪比从退化图像的23.7dB提高到复原图像的30.8dB。结果的结构相似性指数从0.78提高到0.93。定量和定性评价均表明该方法是有效的。
