Rotational motion deblurring of a rigid object from a single image

Most previous motion deblurring methods restore the degraded image assuming a shift-invariant linear blur filter. These methods are not applicable if the blur is caused by spatially variant motions. In this paper, we model the physical properties of a 2-D rigid body movement and propose a practical framework to deblur rotational motions from a single image. Our main observation is that the transparency cue of a blurred object, which represents the motion blur formation from an imaging perspective, provides sufficient information in determining the object movements. Comparatively, single image motion deblurring using pixel color/gradient information has large uncertainties in motion representation and computation. Our results are produced by minimizing a new energy function combining rotation, possible translations, and the transparency map using an iterative optimizing process. The effectiveness of our method is demonstrated using challenging image examples. anteed since the convolution with a blur kernel is noninvertible. To tackle this problem, additional image priors, such as the global gradient distribution from clear images [7], are proposed. Some approaches use multiple images or additional visual cues [2, 20] to constrain the kernel estimation. (a) (b

Joint Blind Motion Deblurring and Depth Estimation of Light Field

Removing camera motion blur from a single light field is a challenging task since it is highly ill-posed inverse problem. The problem becomes even worse when blur kernel varies spatially due to scene depth variation and high-order camera motion. In this paper, we propose a novel algorithm to estimate all blur model variables jointly, including latent sub-aperture image, camera motion, and scene depth from the blurred 4D light field. Exploiting multi-view nature of a light field relieves the inverse property of the optimization by utilizing strong depth cues and multi-view blur observation. The proposed joint estimation achieves high quality light field deblurring and depth estimation simultaneously under arbitrary 6-DOF camera motion and unconstrained scene depth. Intensive experiment on real and synthetic blurred light field confirms that the proposed algorithm outperforms the state-of-the-art light field deblurring and depth estimation methods

The World of Fast Moving Objects

The notion of a Fast Moving Object (FMO), i.e. an object that moves over a distance exceeding its size within the exposure time, is introduced. FMOs may, and typically do, rotate with high angular speed. FMOs are very common in sports videos, but are not rare elsewhere. In a single frame, such objects are often barely visible and appear as semi-transparent streaks. A method for the detection and tracking of FMOs is proposed. The method consists of three distinct algorithms, which form an efficient localization pipeline that operates successfully in a broad range of conditions. We show that it is possible to recover the appearance of the object and its axis of rotation, despite its blurred appearance. The proposed method is evaluated on a new annotated dataset. The results show that existing trackers are inadequate for the problem of FMO localization and a new approach is required. Two applications of localization, temporal super-resolution and highlighting, are presented

Correct spatially varying image blur by Projective Motion Richardson-Lucy Algorithm and Blur Image alignment

Master'sMASTER OF ENGINEERIN

Three-dimensional double helical DNA structure directly revealed from its X-ray fiber diffraction pattern by iterative phase retrieval

Coherent diffraction imaging (CDI) allows the retrieval of the structure of an isolated object, such as a macromolecule, from its diffraction pattern. CDI requires the fulfilment of two conditions: the imaging radiation must be coherent and the object must be isolated. We discuss that it is possible to directly retrieve the molecular structure from its diffraction pattern which was acquired neither with coherent radiation nor from an individual molecule, provided the molecule exhibits periodicity in one direction, as in the case of fiber diffraction. We demonstrate that by applying iterative phase retrieval methods to a fiber diffraction pattern, the repeating unit, that is, the molecule structure, can directly be reconstructed without any prior modeling. As an example, we recover the structure of the DNA double helix in three-dimensions from its two-dimensional X-ray fiber diffraction pattern, Photograph 51, acquired in the famous experiment by Raymond Gosling and Rosalind Franklin, at a resolution of 3.4 Angstrom

Motion-From-Blur: 3D Shape and Motion Estimation of Motion-Blurred Objects in Videos

We propose a method for jointly estimating the 3D motion, 3D shape, and appearance of highly motion-blurred objects from a video. To this end, we model the blurred appearance of a fast moving object in a generative fashion by parametrizing its 3D position, rotation, velocity, acceleration, bounces, shape, and texture over the duration of a predefined time window spanning multiple frames. Using differentiable rendering, we are able to estimate all parameters by minimizing the pixel-wise reprojection error to the input video via backpropagating through a rendering pipeline that accounts for motion blur by averaging the graphics output over short time intervals. For that purpose, we also estimate the camera exposure gap time within the same optimization. To account for abrupt motion changes like bounces, we model the motion trajectory as a piece-wise polynomial, and we are able to estimate the specific time of the bounce at sub-frame accuracy. Experiments on established benchmark datasets demonstrate that our method outperforms previous methods for fast moving object deblurring and 3D reconstruction.Comment: CVPR 2022 camera-read

동적 환경 디블러링을 위한 새로운 모델, 알로기즘, 그리고 해석에 관한 연구

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2016. 8. 이경무.Blurring artifacts are the most common flaws in photographs. To remove these artifacts, many deblurring methods which restore sharp images from blurry ones have been studied considerably in the field of computational photography. However, state-of-the-art deblurring methods are based on a strong assumption that the captured scenes are static, and thus a great many things still remain to be done. In particular, these conventional methods fail to deblur blurry images captured in dynamic environments which have spatially varying blurs caused by various sources such as camera shake including out-of-plane motion, moving objects, depth variation, and so on. Therefore, the deblurring problem becomes more difficult and deeply challenging for dynamic scenes. Therefore, in this dissertation, addressing the deblurring problem of general dynamic scenes is a goal, and new solutions are introduced, that remove spatially varying blurs in dynamic scenes unlike conventional methods built on the assumption that the captured scenes are static. Three kinds of dynamic scene deblurring methods are proposed to achieve this goal, and they are based on: (1) segmentation, (2) sharp exemplar, (3) kernel-parametrization. The proposed approaches are introduced from segment-wise to pixel-wise approaches, and pixel-wise varying general blurs are handled in the end. First, the segmentation-based deblurring method estimates the latent image, multiple different kernels, and associated segments jointly. With the aid of the joint approach, segmentation-based method could achieve accurate blur kernel within a segment, remove segment-wise varying blurs, and reduce artifacts at the motion boundaries which are common in conventional approaches. Next, an \textit{exemplar}-based deblurring method is proposed, which utilizes a sharp exemplar to estimate highly accurate blur kernel and overcomes the limitations of the segmentation-based method that cannot handle small or texture-less segments. Lastly, the deblurring method using kernel-parametrization approximates the locally varying kernel as linear using motion flows. Thus the proposed method based on kernel-parametrization is generally applicable to remove pixel-wise varying blurs, and estimates the latent image and motion flow at the same time. With the proposed methods, significantly improved deblurring qualities are achieved, and intensive experimental evaluations demonstrate the superiority of the proposed methods in dynamic scene deblurring, in which state-of-the-art methods fail to deblur.Chapter 1 Introduction 1 Chapter 2 Image Deblurring with Segmentation 7 2.1 Introduction and Related Work 7 2.2 Segmentation-based Dynamic Scene Deblurring Model 11 2.2.1 Adaptive blur model selection 13 2.2.2 Regularization 14 2.3 Optimization 17 2.3.1 Sharp image restoration 18 2.3.2 Weight estimation 19 2.3.3 Kernel estimation 23 2.3.4 Overall procedure 25 2.4 Experiments 25 2.5 Summary 27 Chapter 3 Image Deblurring with Exemplar 33 3.1 Introduction and Related Work 35 3.2 Method Overview 37 3.3 Stage I: Exemplar Acquisition 38 3.3.1 Sharp image acquisition and preprocessing 38 3.3.2 Exemplar from blur-aware optical flow estimation 40 3.4 Stage II: Exemplar-based Deblurring 42 3.4.1 Exemplar-based latent image restoration 43 3.4.2 Motion-aware segmentation 44 3.4.3 Robust kernel estimation 45 3.4.4 Unified energy model and optimization 47 3.5 Stage III: Post-processing and Refinement 47 3.6 Experiments 49 3.7 Summary 53 Chapter 4 Image Deblurring with Kernel-Parametrization 57 4.1 Introduction and Related Work 59 4.2 Preliminary 60 4.3 Proposed Method 62 4.3.1 Image-statistics-guided motion 62 4.3.2 Adaptive variational deblurring model 64 4.4 Optimization 69 4.4.1 Motion estimation 70 4.4.2 Latent image restoration 72 4.4.3 Kernel re-initialization 73 4.5 Experiments 75 4.6 Summary 80 Chapter 5 Video Deblurring with Kernel-Parametrization 87 5.1 Introduction and Related Work 87 5.2 Generalized Video Deblurring 93 5.2.1 A new data model based on kernel-parametrization 94 5.2.2 A new optical flow constraint and temporal regularization 104 5.2.3 Spatial regularization 105 5.3 Optimization Framework 107 5.3.1 Sharp video restoration 108 5.3.2 Optical flows estimation 109 5.3.3 Defocus blur map estimation 110 5.4 Implementation Details 111 5.4.1 Initialization and duty cycle estimation 112 5.4.2 Occlusion detection and refinement 113 5.5 Motion Blur Dataset 114 5.5.1 Dataset generation 114 5.6 Experiments 116 5.7 Summary 120 Chapter 6 Conclusion 127 Bibliography 131 국문 초록 141Docto