iccv iccv2013 iccv2013-174 knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Shicheng Zheng, Li Xu, Jiaya Jia
Abstract: We handle a special type of motion blur considering that cameras move primarily forward or backward. Solving this type of blur is of unique practical importance since nearly all car, traffic and bike-mounted cameras follow out-ofplane translational motion. We start with the study of geometric models and analyze the difficulty of existing methods to deal with them. We also propose a solution accounting for depth variation. Homographies associated with different 3D planes are considered and solved for in an optimization framework. Our method is verified on several natural image examples that cannot be satisfyingly dealt with by previous methods.
Reference: text
sentIndex sentText sentNum sentScore
1 hk/ leo j ia /pro j ect s / forwarddeblur / Abstract We handle a special type of motion blur considering that cameras move primarily forward or backward. [sent-5, score-0.838]
2 Solving this type of blur is of unique practical importance since nearly all car, traffic and bike-mounted cameras follow out-ofplane translational motion. [sent-6, score-0.684]
3 Introduction Motion blur is one ubiquitous problem in photo taking. [sent-12, score-0.461]
4 Previous deblurring approaches model the degradation in different ways. [sent-13, score-0.411]
5 For example, it is common to assume uniform blur with only in-plane translation or take into account camera rotation. [sent-14, score-0.683]
6 While prior models are effective on images produced under their respectively defined conditions, there are still a bunch of blurred images that find no solution in restoration using existing techniques. [sent-15, score-0.258]
7 Motion blur caused by out-of-plane translation falls into this set. [sent-16, score-0.566]
8 There are millions of, or even more, images that can be found easily on internet degenerated by forward or backward motion during image capture. [sent-17, score-0.281]
9 It is because out-ofplane translation represents one dominating type of camera motion in many commonly seen scenarios. [sent-18, score-0.348]
10 Most surveillance cameras placed on highway record moving vehicles, which could also produce this type ofmotion blur. [sent-20, score-0.247]
11 In addition, out-of-plane translational motion is common from wearable sport cameras and other smart devices, such as Google glasses. [sent-21, score-0.229]
12 In this paper, we focus on dealing with images blurred mainly by vehicle movement or alike, which makes it possible to safely ignore severe camera rotation for algorithmic tractability. [sent-29, score-0.399]
13 Our objective is further narrowed down to deblurring 3D-planes with the understanding of general geometric models in image formation and its present limitation. [sent-30, score-0.459]
14 We further explore parametric blur solution space and therefore handle photos taken from rapidly moving vehicles. [sent-37, score-0.457]
15 Stateof-the-arts are roughly categorized to spatially-invariant and spatially-variant configurations, based on different assumptions of the underlying blur model. [sent-40, score-0.428]
16 [2] proposed a blind deconvolution scheme applicable to natural images. [sent-44, score-0.246]
17 It employs a variational Bayesian approach to estimate the blur kernel through marginal probability maximization. [sent-45, score-0.534]
18 Another line of work is by extending the MAP framework to estimate latent images and blur kernels iteratively. [sent-47, score-0.467]
19 [26] sought an unnatural representation for blur kernel estimation and proposed a fast solver for restoration. [sent-57, score-0.568]
20 Non-Uniform Blind Deblurring The fact that blur caused by camera shake in images are usually non-uniformmotivates a series ofwork with method generalization to model spatially variant blur. [sent-60, score-0.677]
21 In-plane translation and orthogonal rotation are used to model camera shake in another way. [sent-68, score-0.36]
22 [6] assumed that blur is locally invariant and proposed a fast non-uniform framework based on efficient filter flow [7]. [sent-70, score-0.428]
23 [8] developed a hardware solution to record camera shake and restore blurred images. [sent-72, score-0.433]
24 Xu and Jia [25] used stereo images and incorporated depth into the deblurring framework. [sent-73, score-0.473]
25 Other Work Optical aberration can be regarded as a special case of non-uniform blur caused by imperfection of lens. [sent-77, score-0.54]
26 In [10, 16], optical blur was estimated for the lens. [sent-80, score-0.472]
27 [17] designed a set of bases to describe optical blur and proposed a blind deconvolution method to address optical aberration. [sent-82, score-0.762]
28 Image blur typically stems from two sources during exposure, i. [sent-86, score-0.428]
29 In modeling the geometric formation process of cameracaused motion blur, nearly all prior methods assume constant scene depth z under the condition that the scene is distant or front-parallel. [sent-90, score-0.265]
30 Under the constant z assumption, blur image capture can be regarded as a sharp image l, which is formed in a very short time interval, moves in a longer duration. [sent-92, score-0.523]
31 i (1) × where b denotes the blurred image and iindexes the transformed sharp image. [sent-95, score-0.328]
32 (2) indicates that a view can be modeled as a homography warping of the latent sharp image l. [sent-108, score-0.358]
33 (3) × The problem of deblurring is actually to compute weight wi for each pose Hi, physically corresponding to duration of 11446666 each pose. [sent-110, score-0.517]
34 There is a mapping between homography Hi and warping matrix Pi, where Pi is the N N matrix and each row is formed by the coefficiiesn tth oef N N bil ×ine Nar m minatterirxp oanladtion. [sent-111, score-0.253]
35 Only the ratio of tx/z, ty/z, tz/z is required to parameterize homography H. [sent-118, score-0.251]
36 Under constant depth z, the family of homography in the above blur process forms a 6D parameter space. [sent-119, score-0.712]
37 H and Uniform Deblurring In the uniform blur model, θx, θy, θz and tz are all set to zeros. [sent-120, score-0.692]
38 Effectiveness of these methods in approximating blur caused by typical hand-held camera shake was verified [11]. [sent-125, score-0.677]
39 It is however unknown how to blindly estimate the blur kernel and latent image simultaneously, given the large solution space. [sent-127, score-0.573]
40 Importance of Out-of-Plane Translation In nearly all prior deblurring methods, out-of-plane translation is ignored, assuming no movement orthogonal to the image plane. [sent-128, score-0.543]
41 As noted in Section 1, this type of translation, however, is dominant in ubiquitous forward motion situations. [sent-129, score-0.348]
42 In what follows, we discuss if motion caused by out-ofplane translation can be modeled by previous methods. [sent-130, score-0.256]
43 This image is blurred with forward motion only, i. [sent-133, score-0.503]
44 2(b), visually indicating that the blur is spatially variant in a radial shape. [sent-138, score-0.428]
45 Our method actually handles a more general problem where blur occurs by translation with components existing along all axes and the motion is not necessarily perpendicular to the image plane. [sent-141, score-0.704]
46 Almost all practical single-image motion deblurring methods with (a) Dot ed pat ern(b) Forward blur Figure 2. [sent-144, score-0.957]
47 implementation available online assume distant or frontparallel scene, which is actually not appropriate for general forward motion. [sent-146, score-0.231]
48 It is because requiring objects completely undergo translational motion perpendicular to the camera sensor plane is overly restrictive. [sent-147, score-0.46]
49 For example, traffic surveillance cameras are normally placed higher than vehicles or beside highway. [sent-148, score-0.276]
50 This strategy balances system practicality and problem tractability, making the method a reasonable one for forward motion deblurring. [sent-151, score-0.281]
51 Their projection on the blurred image is constrained, availing following optimization. [sent-154, score-0.222]
52 For a 3D plane denoted as π = (n, d), where n is the normal vector and d is the offset to the camera center, any point X on the plane satisfies XTπ = 0. [sent-155, score-0.518]
53 n kTewdo by a homography as H = K(R +tndT)K−1, (4) where R refers to rotational motion and t denotes translation. [sent-159, score-0.34]
54 Because we aim to deal with images primarily produced by car or traffic surveillance cameras, the rotation matrix is set to an identity I. [sent-163, score-0.282]
55 Each blurred surface is a weighed sum of a few transformed planes li defined in Eq. [sent-172, score-0.398]
56 Given tx, ty, tz and n, we can uniquely determine a homography, which also corresponds to a N N warping matrix P described in Eq. [sent-176, score-0.236]
57 P PIn d tehscisr regard, we transform originally very difficult whole-image deblurring to a plane-wise tractable problem, counting in non-frontal 3D planes. [sent-178, score-0.411]
58 We thus propose constructing 3D homography space t = (tx, ty, tz)T and sample each tx, ty, and tz discretely to predefine a few camera poses. [sent-181, score-0.523]
59 Put differently, our method uses a series of discrete Hin to present the original continuous homography space, each homography or status is determined by a corresponding n and by a pose ti indexed by i. [sent-183, score-0.479]
60 3 shows an example for demonstrating the specialty of forward motion blur. [sent-185, score-0.328]
61 We use the dotted pattern to visualize point trajectories and homography basis, which make this kind of blur formation easy to comprehend. [sent-186, score-0.698]
62 (a) is to show that forward motion blurred images, such as that in (b), can generally find a few planes. [sent-187, score-0.537]
63 Previous methods, even for non-uniform deblurring, mostly consider the case n = (0, 0, 1), whereas our method handles all of them as well as planes with all non-zero elements in normal n. [sent-190, score-0.271]
64 For planes with normals (1, 0, 0) and (0, 1, 0) (top two rows in (c)), a column and a row of pixels do not blur at all. [sent-202, score-0.571]
65 It is because these points are infinitely distant or the plane passes the camera center. [sent-203, score-0.303]
66 Color Mixing Issue The blur formation represented as b = ? [sent-204, score-0.476]
67 color mixing arising in forward motion blur, which is cau? [sent-207, score-0.281]
68 In this regard, one pixel in the blurred image is not a summation (or integration) of a few isolated unblurred pixels, but rather a combination of several patches, as illustrated in Fig. [sent-211, score-0.222]
69 It is with tz = 0 and all other li are with tz < 0, corresponding to down-scaled versions of l. [sent-214, score-0.437]
70 To practically model the resulting blurred image b and avoid aliasing, we regard b as the sum of latent images li blurred by Gaussian filter, whose standard deviation is determined by tz corresponding to each li. [sent-215, score-0.756]
71 The final blur model is finely expressed as b= ? [sent-220, score-0.428]
72 i where Gi is a BTTB (block-Toeplitz with Toeplitz-block) matrix representing the Gaussian blur kernel in a matrix form. [sent-222, score-0.534]
73 We describe in the next section our deblurring algorithm based on this model. [sent-224, score-0.411]
74 Solving for w and lwith a fixed n corresponds to a non-uniform deblurring problem. [sent-228, score-0.411]
75 w is known as blur kernel, since it records the duration of each camera pose, conceptually similar to 2D uniform blur PSFs. [sent-233, score-1.045]
76 We use the local uniform assumption [6] for acceleration considering smoothly changing blur kernels under depth variation on 3D planes. [sent-270, score-0.549]
77 Image Update In uniform deblurring, extra steps with shock filter [1] are generally employed to help kernel estimation. [sent-275, score-0.246]
78 The scale-invariant property of L0-sparsity is vital to guide blur kernel estimation. [sent-282, score-0.534]
79 A plane normal is parameterized in the spherical coordinate system as n = (n1, n2, n3)T = (cosαsinβ, sinα sinβ, cosβ)T, (10) where α and β are polar and azimuthal angles respectively. [sent-293, score-0.327]
80 Given the input normal specified by angles α0 and β0, we update them within α0 15◦ and β0 15◦, with each interval 5◦. [sent-303, score-0.225]
81 ± 11446699 ± lel lines on a blurred plane are drawn to find two vanishing points. [sent-310, score-0.394]
82 A plane with vanishing line v has it normal determined as n = KTv. [sent-325, score-0.334]
83 But they are not reliable enough on blurred images. [sent-329, score-0.222]
84 To develop a robust automatic plane detection method for forward motion blur will be our future work. [sent-330, score-0.839]
85 Sampling Details We regularly sample tx, ty, and tz to get our homography basis Hin, accounting for out-of-plane and in-plane translation. [sent-331, score-0.464]
86 To this end, we use the same blurred image illustrated in Fig. [sent-337, score-0.222]
87 image a87nd) blur kernel, and updates this normal iteratively. [sent-345, score-0.59]
88 Their respective blur kernel and normal estimates are visualized in (g)-(h). [sent-348, score-0.731]
89 The deblurred image thus contains visual artifacts and leftover blur (see the close-ups). [sent-350, score-0.467]
90 Our method produces a more compelling result from the single image input, shown in (f), with the corresponding 3D plane and blur kernel visualized in (g) and (h). [sent-363, score-0.699]
91 Our result is shown in (f), with the associated plane and kernel visualized in (g) and (h). [sent-372, score-0.271]
92 Conclusion In this paper, we focused on addressing a special and important type of motion deblurring problem, namely for11447700 (a) Input(b) Whyte et al. [sent-377, score-0.608]
93 The initial and final deblurring results are shown in (c) and (d). [sent-384, score-0.411]
94 ward/backward blur removal, which generally arises for traffic surveillance or vehicle cameras. [sent-392, score-0.618]
95 Its specialty lies on modeling depth variation and pixel blending with high diThe corresponding kernel and normal results are shown in (g) and (h). [sent-393, score-0.377]
96 We presented a method based on 3D plane models, which only needs rough plane normal initialization. [sent-401, score-0.422]
97 Recording and playback of camera shake: benchmarking blind deconvolution with a real-world database. [sent-506, score-0.342]
98 Rotational motion deblurring of a rigid object from a single image. [sent-565, score-0.529]
99 Richardson-lucy deblurring for scenes under a projective motion path. [sent-571, score-0.569]
100 Richardson-lucy deblurring for scenes under a projective motion path. [sent-579, score-0.569]
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