Blanco L, Mugnier L M
ONERA - The French Aerospace Lab, F-92322 Chatillon, France.
Opt Express. 2011 Nov 7;19(23):23227-39. doi: 10.1364/OE.19.023227.
Adaptive Optics corrected flood imaging of the retina has been in use for more than a decade and is now a well-developed technique. Nevertheless, raw AO flood images are usually of poor contrast because of the three-dimensional nature of the imaging, meaning that the image contains information coming from both the in-focus plane and the out-of-focus planes of the object, which also leads to a loss in resolution. Interpretation of such images is therefore difficult without an appropriate post-processing, which typically includes image deconvolution. The deconvolution of retina images is difficult because the point spread function (PSF) is not well known, a problem known as blind deconvolution. We present an image model for dealing with the problem of imaging a 3D object with a 2D conventional imager in which the recorded 2D image is a convolution of an invariant 2D object with a linear combination of 2D PSFs. The blind deconvolution problem boils down to estimating the coefficients of the PSF linear combination. We show that the conventional method of joint estimation fails even for a small number of coefficients. We derive a marginal estimation of the unknown parameters (PSF coefficients, object Power Spectral Density and noise level) followed by a MAP estimation of the object. We show that the marginal estimation has good statistical convergence properties and we present results on simulated and experimental data.
自适应光学校正的视网膜泛光成像技术已经应用了十多年，如今已成为一项成熟的技术。然而，由于成像的三维特性，原始的自适应光学泛光图像通常对比度较差，这意味着图像包含来自物体焦平面和离焦平面的信息，这也导致分辨率下降。因此，在没有适当的后处理（通常包括图像去卷积）的情况下，很难解释此类图像。视网膜图像的去卷积很困难，因为点扩散函数（PSF）并不为人所知，这就是所谓的盲去卷积问题。我们提出了一个图像模型，用于处理用二维传统成像仪对三维物体成像的问题，其中记录的二维图像是不变二维物体与二维PSF线性组合的卷积。盲去卷积问题归结为估计PSF线性组合的系数。我们表明，即使对于少量系数，传统的联合估计方法也会失败。我们推导了未知参数（PSF系数、物体功率谱密度和噪声水平）的边际估计，然后是物体的最大后验概率（MAP）估计。我们表明，边际估计具有良好的统计收敛特性，并给出了模拟数据和实验数据的结果。
