Article in Russian
For citation: Dronnikova S.A., Gurov I.P. Image quality enhancement by processing of video frames with different exposure time.
Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2017, vol. 17, no. 3, pp. 424–430 (in Russian). doi: 10.17586/2226-1494-2017-17-3-424-430
Abstract
Subject of Research. We consider a method of image quality enhancement, when an image is registered with long exposure time under uncontrollable camera shake. Blur compensation is implemented by deconvolution computational algorithm with the point-spread function determining the image blur. Method. The main step of the deblurring algorithm consists in evaluation of a point-spread function involving the second underexposed noisy image frame as an initial approximation in iterative algorithm of point-spread function estimation. The subsequent blurred image deconvolution with the estimated point-spread function provides obtaining of enhanced image. Main Results. We have proposed new procedures for refinement of point-spread function estimates based on separation of values under hysteresis threshold application with the use of adopted Canny algorithm as well as modification in scale space. Practical Relevance. Obtained results can be used to enhance image quality in cases when camera moves during the image capture process including scientific research and computer vision systems.
Keywords: Image quality, deblurring, point-spread function, deconvolution
References
1. Lee J.-H., Shin I.-Y., Lee H.-G., Kim T.-Y., Ho Y.-S. Anti-shaking algorithm for the mobile phone camera in dim light conditions.
Lecture Notes in Computer Science, 2009, vol. 5879, pp. 968–973. doi:
10.1007/978-3-642-10467-1_90
2. Gonzales R.C.,Woods R.E. Digital Image Processing. 2nd ed. Upper Saddle River, Prentice Hall, 2002, 793 p.
3. Krahmer F., Lin Y., McAdoo B., Ott K., Wang J., Widemann D. Blind image deconvolution: motion blur estimation. IMA Preprints Series, 2006, vol. 21, pp. 33–35.
4. Levin A. Blind motion deblurring using image statistics. Advances in Neural Information Processing Systems, 2006, pp. 840–848.
5. Fergus R., Singh B., Hertzmann A., Roweis S.T., Freeman W.T. Removing camera shake from a single photograph.
ACM Transactions on Graphics, 2006, vol. 25, no. 3, pp. 787–794. doi:
10.1145/1141911.1141956
6. Sroubek F., Flusser J. Multichannel blind deconvolution of spatially misaligned images.
IEEE Transactions on Image Processing, 2005, vol. 14, no. 7, pp. 874–883. doi:
10.1109/TIP.2005.849322
7. Lim S.H., Silverstein D.A. Method for deblurring an image. US Patent Application, pub. no. 2006/0187308.
8. Rav-Acha A., Peleg S. Two motion-blurred images are better than one.
Pattern Recognition Letters, 2005, vol. 26, no. 3, pp. 311–317. doi:
10.1016/j.patrec.2004.10.017
9. Yuan L., Sun J., Quan L., Shum H.-Y. Image deblurring with blurred/noisy image pairs.
Proc. ACM SIGGRAPH Conference on Computer Graphics. San Diego, USA, 2007. doi:
10.1145/1275808.1276379
10.Richardson W.H. Bayesian-based iterative method of image restoration. J. Opt. Soc. Am., 1972, vol. 62, no. 1, pp. 55–59.
11.Pratt W.K. Digital Image Processing. NY, Wiley, 1978.
12.Kirsch A.
An Introduction to the Mathematical Theory of Inverse Problems. NY, Springer, 2011. doi:
10.1007/978-1-4419-8474-6
13.Verlan A.F., Sizikov V.S. The Integral Equations: Methods, Algorithms, Programs. Kiev, Naukova dumka, 1986, 544 p.
14.Ramlau R. Modified Landweber method for inverse problems. Numerical Functional Analysis and Optimization, 1999, vol. 20, no. 1, pp. 79–98.
Canny J. A computational approach to edge detection.
IEEE Transactions on Pattern Analysis and Machine Intelligence, 1986, vol. PAMI-8, no. 6, pp. 679–698. doi:
10.1109/TPAMI.1986.4767851