Image Deblurring Aided by Low-Resolution Events [论文解读]

本文深入解读了商汤科技关于利用低分辨率事件辅助高分辨率图像去模糊的论文。研究提出了一种新的处理流程,包括初步去模糊、超分辨率重建和二次去模糊。实验表明,结合事件数据的超分辨率方法能有效提高图像去模糊的效果。

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Image Deblurring Aided by Low-Resolution Events [论文解读]

商汤的一个工作,解决低空间分辨率events+高空间分辨率image的deblur问题。

摘要

核心问题:解决rgb图像和events图像空间分辨率不匹配导致的deblur结果质量不佳的问题
拿到的数据:理想情况下不存在对齐问题的高空间分辨率rgb+低空间分辨率events
建立一个新的pipeline:1. 先粗略进行image +events deblur;2. 然后image-blur,image-sharp0,events进SR模型进行超分;3. image-blur,events-SR 进deblur模型得到最终的image-sharp1

pipeline
如果对于dvs相机生成events的原理还不懂的话,可以参考这篇文章Event-Based Vision: A Survey,看看dvs相机的种类和原理。本文也简单介绍了一下dvs相机的原理,如下式
l o g ( I x y ( t ) + b ) − l o g ( I x y ( t − Δ t ) + b ) = p c . ( 1 ) log(I_{xy}(t)+b)-log(I_{xy}(t-\Delta t)+b) = p c. (1) log(Ixy(t)+b)log(Ixy

【作 者】Per Christian Hansen 【出版社】Society for Industrial and Applied Mathematic 【出版日期】October 29, 2006 【ISBN】0898716187 9780898716184 【形态项】9.8 x 6.7 x 0.3 inches 【语 言】English 【价 格】$63.00 Deblurring Images: Matrices, Spectra, and Filtering (Fundamentals of Algorithms 3) (Fundamentals of Algorithms) By Per Christian Hansen Publisher: Society for Industrial and Applied Mathematic Number Of Pages: 130 Publication Date: 2006-10-29 ISBN-10 / ASIN: 0898716187 ISBN-13 / EAN: 9780898716184 Binding: Paperback “The book’s focus on imaging problems is very unique among the competing books on inverse and ill-posed problems. …It gives a nice introduction into the MATLAB world of images and deblurring problems.” — Martin Hanke, Professor, Institut für Mathematik, Johannes-Gutenberg-Universität. When we use a camera, we want the recorded image to be a faithful representation of the scene that we see, but every image is more or less blurry. In image deblurring, the goal is to recover the original, sharp image by using a mathematical model of the blurring process. The key issue is that some information on the lost details is indeed present in the blurred image, but this “hidden” information can be recovered only if we know the details of the blurring process. Deblurring Images: Matrices, Spectra, and Filtering describes the deblurring algorithms and techniques collectively known as spectral filtering methods, in which the singular value decomposition—or a similar decomposition with spectral properties—is used to introduce the necessary regularization or filtering in the reconstructed image. The concise MATLAB® implementations described in the book provide a template of techniques that can be used to restore blurred images from many applications. This book’s treatment of image deblurring is unique in two ways: it includes algorithmic and implementation details; and by keeping the formulations in terms of matrices, vectors, and matrix computations, it makes the material accessible to a wide range of readers. Students and researchers in engineering will gain an understanding of the linear algebra behind filtering methods, while readers in applied mathematics, numerical analysis, and computational science will be exposed to modern techniques to solve realistic large-scale problems in image processing. With a focus on practical and efficient algorithms, Deblurring Images: Matrices, Spectra, and Filtering includes many examples, sample image data, and MATLAB codes that allow readers to experiment with the algorithms. It also incorporates introductory material, such as how to manipulate images within the MATLAB environment, making it a stand-alone text. Pointers to the literature are given for techniques not covered in the book. Audience This book is intended for beginners in the field of image restoration and regularization. Readers should be familiar with basic concepts of linear algebra and matrix computations, including the singular value decomposition and orthogonal transformations. A background in signal processing and a familiarity with regularization methods or with ill-posed problems are not needed. For readers who already have this knowledge, this book gives a new and practical perspective on the use of regularization methods to solve real problems. Preface; How to Get the Software; List of Symbols; Chapter 1: The Image Deblurring Problem; Chapter 2: Manipulating Images in MATLAB; Chapter 3: The Blurring Function; Chapter 4: Structured Matrix Computations; Chapter 5: SVD and Spectral Analysis; Chapter 6: Regularization by Spectral Filtering; Chapter 7: Color Images, Smoothing Norms, and Other Topics; Appendix: MATLAB Functions; Bibliography; Index
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