Accurate Point Spread Function (PSF) estimation of coded aperture cameras is a key to deblur defocus images.
There are mainly two kinds of approaches to estimate PSF: blind-deconvolution-based methods, and
measurement-based methods with point light sources. Both these two kinds of methods cannot provide accurate
and convenient PSFs due to the limit of blind deconvolution or imperfection of point light sources. Inaccurate
PSF estimation introduces pseudo-ripple and ringing artifacts which influence the effects of image deconvolution.
In addition, there are many inconvenient situation for the PSF estimation.
This paper proposes a novel method of PSF estimation for coded aperture cameras. It is observed and verified
that the spatially-varying point spread functions are well modeled by the convolution of the aperture pattern
and Gaussian blurring with appropriate scales and bandwidths. We use the coded aperture camera to capture
a point light source to get a rough estimate of the PSF. Then, the PSF estimation method is formulated as the
optimization of scale and bandwidth of Gaussian blurring kernel to fit the coded pattern with the observed PSF.
We also investigate the PSF estimation at arbitrary distance with a few observed PSF kernels, which allows us to
fully characterize the response of coded imaging systems with limited measurements. Experimental results show
that our method is able to accurately estimate PSF kernels, which significantly make the deblurring performance