Modeling Non-Stationary Asymmetric Lens Blur By Normal Sinh-Arcsinh Model

Department of Electrical EngineeringImages acquired by a camera show lens blur due to imperfection in the optical system. Lens blur is non-stationary in a sense the amount of blur depends on pixel locations in a sensor. Lens blur is also asymmetric in a sense the amount of blur is different in the radial and tangential directions, and also in the inward and outward radial directions. This paper presents parametric blur kernel models based on the normal sinh-arcsinh distribution function. The proposed models can provide flexible shapes of blur kernels with different symmetry and skewness to model complicated lens blur accurately. Blur of single focal length lenses is estimated and the accuracy of the models is compared with existing parametric blur models. Advantage of the proposed models is demonstrated through deblurring experiments.ope

Modeling nonstationary lens blur using eigen blur kernels for restoration

Images acquired through a lens show nonstationary blur due to defocus and optical aberrations. This paper presents a method for accurately modeling nonstationary lens blur using eigen blur kernels obtained from samples of blur kernels through principal component analysis. Pixelwise variant nonstationary lens blur is expressed as a linear combination of stationary blur by eigen blur kernels. Operations that represent nonstationary blur can be implemented efficiently using the discrete Fourier transform. The proposed method provides a more accurate and efficient approach to modeling nonstationary blur compared with a widely used method called the efficient filter flow, which assumes stationarity within image regions. The proposed eigen blur kernel-based modeling is applied to total variation restoration of nonstationary lens blur. Accurate and efficient modeling of blur leads to improved restoration performance. The proposed method can be applied to model various nonstationary degradations of image acquisition processes, where degradation information is available only at some sparse pixel locations. (C) 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreemen

Learning Lens Blur Fields

Optical blur is an inherent property of any lens system and is challenging to model in modern cameras because of their complex optical elements. To tackle this challenge, we introduce a high-dimensional neural representation of blur$-$$\textit{the lens blur field}$$-$and a practical method for acquiring it. The lens blur field is a multilayer perceptron (MLP) designed to (1) accurately capture variations of the lens 2D point spread function over image plane location, focus setting and, optionally, depth and (2) represent these variations parametrically as a single, sensor-specific function. The representation models the combined effects of defocus, diffraction, aberration, and accounts for sensor features such as pixel color filters and pixel-specific micro-lenses. To learn the real-world blur field of a given device, we formulate a generalized non-blind deconvolution problem that directly optimizes the MLP weights using a small set of focal stacks as the only input. We also provide a first-of-its-kind dataset of 5D blur fields$-$for smartphone cameras, camera bodies equipped with a variety of lenses, etc. Lastly, we show that acquired 5D blur fields are expressive and accurate enough to reveal, for the first time, differences in optical behavior of smartphone devices of the same make and model

ADMM in Krylov Subspace and Its Application to Total Variation Restoration of Spatially Variant Blur

In this paper we propose an efficient method for a convex optimization problem which involves a large nonsymmetric and non-Toeplitz matrix. The proposed method is an instantiation of the alternating direction method of multipliers applied in Krylov subspace. Our method offers significant advantages in computational speed for the convex optimization problems involved with general matrices of large size. We apply the proposed method to the restoration of spatially variant blur. The matrix representing spatially variant blur is not block circulant with circulant blocks (BCCB). Efficient implementation based on diagonalization of BCCB matrices by the discrete Fourier transform is not applicable for spatially variant blur. Since the proposed method can efficiently work with general matrices, the restoration of spatially variant blur is a good application of our method. Experimental results for total variation restoration of spatially variant blur show that the proposed method provides meaningful solutions in a short time.clos

Introduction to Particle and Astroparticle Physics : Multimessenger Astronomy and its Particle Physics Foundations -2/E

This book introduces particle physics, astrophysics and cosmology starting from experiment. It provides a unified view of these fields, which is needed to answer our questions to the Universe–a unified view that has been lost somehow in recent years due to increasing specialization. This is the second edition of a book we published only three years ago, a book which had a success beyond our expectations. We felt that the recent progress on gravitational waves, gamma ray and neutrino astrophysics deserved a new edition including all these new developments: multimessenger astronomy is now a reality. In addition, the properties of the Higgs particle are much better known now than three years ago. Thanks to this second edition we had the opportunity to fix some bugs, to extend the material related to exercises, and to change in a more logical form the order of some items. Last but not least, our editor encouraged us a lot to write a second edition. Particle physics has recently seen the incredible success of the so-called standard model. A 50-year long search for the missing ingredient of the model, the Higgs particle, has been concluded successfully, and some scientists claim that we are close to the limit of the physics humans may know