Design and Estimation of Coded Exposure Point Spread Functions

Abstract-We address the problem of motion deblurring using coded exposure. This approach allows for accurate estimation of a sharp latent image via wellposed deconvolution and avoids lost image content that cannot be recovered from images acquired with a traditional shutter. Previous work in this area has used either manual user input or alpha matting approaches to estimate the coded exposure Point Spread Function (PSF) from the captured image. In order to automate deblurring and to avoid the limitations of matting approaches, we propose a Fourier-domain statistical approach to coded exposure PSF estimation that allows us to estimate the latent image in cases of constant velocity, constant acceleration, and harmonic motion. We further demonstrate that previously used criteria to choose a coded exposure PSF do not produce one with optimal reconstruction error, and that an additional 30 percent reduction in Root Mean Squared Error (RMSE) of the latent image estimate can be achieved by incorporating natural image statistics

Recent Progress in Image Deblurring

This paper comprehensively reviews the recent development of image deblurring, including non-blind/blind, spatially invariant/variant deblurring techniques. Indeed, these techniques share the same objective of inferring a latent sharp image from one or several corresponding blurry images, while the blind deblurring techniques are also required to derive an accurate blur kernel. Considering the critical role of image restoration in modern imaging systems to provide high-quality images under complex environments such as motion, undesirable lighting conditions, and imperfect system components, image deblurring has attracted growing attention in recent years. From the viewpoint of how to handle the ill-posedness which is a crucial issue in deblurring tasks, existing methods can be grouped into five categories: Bayesian inference framework, variational methods, sparse representation-based methods, homography-based modeling, and region-based methods. In spite of achieving a certain level of development, image deblurring, especially the blind case, is limited in its success by complex application conditions which make the blur kernel hard to obtain and be spatially variant. We provide a holistic understanding and deep insight into image deblurring in this review. An analysis of the empirical evidence for representative methods, practical issues, as well as a discussion of promising future directions are also presented.Comment: 53 pages, 17 figure

Learning Wavefront Coding for Extended Depth of Field Imaging

Depth of field is an important factor of imaging systems that highly affects the quality of the acquired spatial information. Extended depth of field (EDoF) imaging is a challenging ill-posed problem and has been extensively addressed in the literature. We propose a computational imaging approach for EDoF, where we employ wavefront coding via a diffractive optical element (DOE) and we achieve deblurring through a convolutional neural network. Thanks to the end-to-end differentiable modeling of optical image formation and computational post-processing, we jointly optimize the optical design, i.e., DOE, and the deblurring through standard gradient descent methods. Based on the properties of the underlying refractive lens and the desired EDoF range, we provide an analytical expression for the search space of the DOE, which is instrumental in the convergence of the end-to-end network. We achieve superior EDoF imaging performance compared to the state of the art, where we demonstrate results with minimal artifacts in various scenarios, including deep 3D scenes and broadband imaging

Opium for the Masses: How Foreign Free Media Can Stabilize Authoritarian Regimes

A common claim in the democratization literature is that foreign free media undermine authoritarian rule. No reliable micro-level evidence on this topic exists, however, since independent survey research is rarely possible in authoritarian regimes and self-selection into media consumption complicates causal inferences. In this case study of the impact of West German television on political attitudes in communist East Germany, we address these problems by making use of previously secret survey data and a natural experiment. While most East Germans were able to tune in to West German broadcasts, some of them were cut off from West German television due to East Germany's topography. We exploit this plausibly exogenous variation to estimate the impact of West German television on East Germans' political attitudes using instrumental variable estimators. Contrary to conventional wisdom, East Germans who watched West German television were more satisfied with life in East Germany and the communist regime. To explain this surprising finding, we demonstrate that West German television's role in transmitting political information not available in the state-controlled communist media was insignificant and that television primarily served as a means of entertainment for East Germans. Archival material on the reaction of the East German regime to the availability of West German television corroborates our argument.instrumental variables; causal inference; local average response function; media effects; East Germany; democratization

Coded aperture and coded exposure photography : an investigation into applications and methods

This dissertation presents an introduction to the field of computational photography, and provides a survey of recent research. Specific attention is given to coded aperture and coded exposure theory and methods, as these form the basis for the experiments performed