PHYSICS-INFORMED DEEP DEBLURRING: OVER-PARAMETERIZED VS. UNDER-PARAMETERIZED

Image deblurring is a classic problem in inverse computational imaging, where the blur is characterized by a kernel, known as the point spread function (PSF). For a complicated PSF, the resulting blurred image is usually incomprehensible to the human eye, such as in the case of lensless images. In this paper, we design and compare the reconstruction performance of under-parameterized and over-parameterized networks for the inverse imaging problem of lensless image reconstruction. We perform an untrained iterative reconstruction against a physics-informed loss function that requires knowledge of the forward imaging process. We conduct an extensive performance evaluation using multiple evaluation metrics and explore the obtained results contrastively by identifying the strengths and weaknesses of under-parameterization. Also, we provide reconstructions obtained using our PSF captured with a random diffuser.