Identification of blur parameters from motion blurred images
01 Nov 1997-Graphical Models and Image Processing (Academic Press, Inc.)-Vol. 59, Iss: 5, pp 310-320
TL;DR: The method proposed here identifies the direction and the extent of the PSF of the blur and evaluates its shape which depends on the type of motion during the exposure, which permits fast high resolution restoration of the blurred image.
About: This article is published in Graphical Models and Image Processing.The article was published on 1997-11-01 and is currently open access. It has received 176 citations till now. The article focuses on the topics: Image restoration & Motion estimation.
Figures (12)
FIG. 8. (a) The image phone mechanically blurred horizontally by low frequency vibrations; (b) a 3-pixel blur extent was identified by a clear minimum in the identification function. 
FIG. 1. (a) A 3-pixel image; (b) The blurred image using an 8-pixel square function. 
FIG. 10. An image manually blurred by motion of the camera. 
FIG. 16. Identification results for different motion types implemented on valley image. (a1, b1, c1) the blurring PSFs; (a2, b2, c2) the ACFs of the PSFs; (a3, b3, c3) the average of the ACFs of the blurred image derivative lines. 
FIG. 3. The ACF of row 15 in the image derivative of Fig. 2. The blur extent (8 pixels) is equal to the location of the minimum of the function relative to the center. 
FIG. 11. (a) The motion direction (558) is indicated by the location of the minimum in the motion direction identification function; (b) 18 pixels blur extent is identified by the ACF of the blurred image derivative. 
FIG. 12. Restored image using the identified parameters in the Wiener filter. 
FIG. 13. An image manually blurred by motion of the camera. 
FIG. 4. The motion direction identification for the 3-pixel image. 
FIG. 14. (a) Zero degrees motion direction is identified in the direction identification function; (b) 8 pixels blur extent is identified by the ACF of the blurred image derivative. 
FIG. 15. Restored image using the identified parameters in the Wiener filter. 
FIG. 6. Implementation for a real image. (a) The original valley image; (b) image blurred by horizontal accelerated motion function forming 12-pixel blur extent with 1.07 relative homogeneity of the PSF, and an additive zero mean normally distributed random noise causing 40 dB SNR; (c) the horizontal image derivative; (d) the average of the ACFs of the image derivative lines.
Citations
More filters
TL;DR: This work proposes a straightforward method to restore motion-blurred images given only the blurred image itself, and identifies the point-spread function (PSF) of the blur and uses it to restore the blur image.
Abstract: We deal with the problem of restoration of images blurred by relative motion between the camera and the object of interest. This problem is common when the imaging system is in moving vehicles or held by human hands, and in robot vision. For correct restoration of the degraded image, it is useful to know the point-spread function (PSF) of the blurring system. We propose a straightforward method to restore motion-blurred images given only the blurred image itself. The method first identifies the PSF of the blur and then uses it to restore the blurred image. The blur identification here is based on the concept that image characteristics along the direction of motion are affected mostly by the blur and are different from the characteristics in other directions. By filtering the blurred image, we emphasize the PSF correlation properties at the expense of those of the original image. Experimental results for image restoration are presented for both synthetic and real motion blur.
175 citations
Patent•
24 May 2007
TL;DR: In this article, a motion detector causes the sensor to cease capture of an image when the degree of movement in acquiring the image exceeds a threshold, and an image re-constructor corrects the selected image with associated motion parameters.
Abstract: An image acquisition sensor of a digital image acquisition apparatus is coupled to imaging optics for acquiring a sequence of images. Images acquired by the sensor are stored. A motion detector causes the sensor to cease capture of an image when the degree of movement in acquiring the image exceeds a threshold. A controller selectively transfers acquired images for storage. A motion extractor determines motion parameters of a selected, stored image. An image re-constructor corrects the selected image with associated motion parameters. A selected plurality of images nominally of the same scene are merged and corrected by the image re-constructor to produce a high quality image of the scene.
151 citations
27 Aug 2013
TL;DR: It is shown that a straightforward maximum a posteriory estimation combined with very sparse priors and an efficient numerical method can produce results, which compete with much more complicated state-of-the-art methods.
Abstract: Single image blind deconvolution aims to estimate the unknown blur from a single observed blurred image and recover the original sharp image. Such task is severely ill-posed and typical approaches involve some heuristic or other steps without clear mathematical explanation to arrive at an acceptable solution. We show that a straightforward maximum a posteriory estimation combined with very sparse priors and an efficient numerical method can produce results, which compete with much more complicated state-of-the-art methods.
114 citations
TL;DR: This paper proposes a novel method for recognizing faces degraded by blur using deblurring of facial images and shows and explains how combining the proposed facial deblur inference with the local phase quantization (LPQ) method can further enhance the performance.
Abstract: This paper proposes a novel method for recognizing faces degraded by blur using deblurring of facial images. The main issue is how to infer a Point Spread Function (PSF) representing the process of blur on faces. Inferring a PSF from a single facial image is an ill-posed problem. Our method uses learned prior information derived from a training set of blurred faces to make the problem more tractable. We construct a feature space such that blurred faces degraded by the same PSF are similar to one another. We learn statistical models that represent prior knowledge of predefined PSF sets in this feature space. A query image of unknown blur is compared with each model and the closest one is selected for PSF inference. The query image is deblurred using the PSF corresponding to that model and is thus ready for recognition. Experiments on a large face database (FERET) artificially degraded by focus or motion blur show that our method substantially improves the recognition performance compared to existing methods. We also demonstrate improved performance on real blurred images on the FRGC 1.0 face database. Furthermore, we show and explain how combining the proposed facial deblur inference with the local phase quantization (LPQ) method can further enhance the performance.
101 citations
Additional excerpts
...Ç...
[...]
TL;DR: An image-based method for vehicle speed detection is presented, using a single image captured with vehicle motion for speed measurement according to the imaging geometry, camera pose, and blur extent in the image.
86 citations
Cites background from "Identification of blur parameters f..."
...The above image degradation model for linear motion [26,27], however, cannot be directly used to model the motion blur caused by an object moving in front of a still background....
[...]
References
More filters
IBM1
TL;DR: In this article, the use of the fast Fourier transform in power spectrum analysis is described, and the method involves sectioning the record and averaging modified periodograms of the sections.
Abstract: The use of the fast Fourier transform in power spectrum analysis is described. Principal advantages of this method are a reduction in the number of computations and in required core storage, and convenient application in nonstationarity tests. The method involves sectioning the record and averaging modified periodograms of the sections.
9,705 citations
Book•
01 Jan 1976
TL;DR: The rapid rate at which the field of digital picture processing has grown in the past five years had necessitated extensive revisions and the introduction of topics not found in the original edition.
Abstract: The rapid rate at which the field of digital picture processing has grown in the past five years had necessitated extensive revisions and the introduction of topics not found in the original edition.
4,231 citations
Book•
16 Nov 2012TL;DR: The article introduces digital image restoration to the reader who is just beginning in this field, and provides a review and analysis for the readers who may already be well-versed in image restoration.
Abstract: The article introduces digital image restoration to the reader who is just beginning in this field, and provides a review and analysis for the reader who may already be well-versed in image restoration. The perspective on the topic is one that comes primarily from work done in the field of signal processing. Thus, many of the techniques and works cited relate to classical signal processing approaches to estimation theory, filtering, and numerical analysis. In particular, the emphasis is placed primarily on digital image restoration algorithms that grow out of an area known as "regularized least squares" methods. It should be noted, however, that digital image restoration is a very broad field, as we discuss, and thus contains many other successful approaches that have been developed from different perspectives, such as optics, astronomy, and medical imaging, just to name a few. In the process of reviewing this topic, we address a number of very important issues in this field that are not typically discussed in the technical literature.
1,588 citations
TL;DR: In this paper, the frequency response of a two-dimensional spatially invariant linear system through which an image has been passed and blurred is estimated for the cases of uniform linear camera motion.
Abstract: This paper is concerned with the digital estimation of the frequency response of a two-dimensional spatially invariant linear system through which an image has been passed and blurred. For the cases of uniform linear camera motion and an out-of-focus lens system it is shown that the power cepstrum of the image contains sufficient information to identify the blur. Methods for deblurring are presented, including restoration of the density version of the image. The restoration procedure consumes only a modest amount of computation time. Results are demonstrated on images blurred in the camera.
489 citations
Book•
11 Nov 1984
TL;DR: A canonical set of image processing problems that represent the class of functions typically required in most image processing applications is presented and the best techniques for this particular problem and how they work are addressed.
Abstract: Contributors Preface 1 Image Enhancement I Introduction II Enhancement Techniques III Further Comments IV Bibliographic Notes References 2 Image Restoration I Statement of the Problem II Direct Techniques of Image Restoration III Indirect Techniques of Image Restoration IV Identification of the Point Spread Function V Assessment of Techniques VI Bibliographical Notes References 3 Image Detection and Estimation I Introduction II Detecting Known Objects III Detecting Random Objects IV Estimating Random Curves V Conclusions VI Bibliographical Notes References 4 Image Reconstruction from Projections I Introduction II Computational Procedures for Image Reconstruction III The Theory of Filtered-Backprojection Algorithms IV The Theory of Algebraic Algorithms V Aliasing Artifacts VI Bibliographical Notes References 5 Image Data Compression I Introduction II Spatial Domain Methods III Transform Coding IV Hybrid Coding and Vector DCPM V Interframe Coding VI Coding of Graphics VII Applications VIII Bibliography References 6 Image Spectral Estimation I Introduction II Background III Techniques IV Summary V Bibliographical Notes References 7 Image Analysis I Introduction II Image Segmentation III Region Description and Segmentation IV Bibliographical Notes References 8 Image Processing Systems I Introduction II Current Context of Image Processing III System Hardware Architecture IV Image Processing Display Hardware V Image Processing Software VI Issues Involved in Evaluating Image Processing Systems VII Conclusion References Author Index Subject Index
199 citations