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Adaptive deblurring of surveillance video sequences that deteriorate over time

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Original languageEnglish
Title of host publicationImage Processing (ICIP), 2013 20th IEEE International Conference on
Number of pages5
Publication statusPublished - 1 Sep 2013


We present a method for restoring the recordings obtained from surveillance cameras whose quality deteriorates due to dirt or water that gathers on the camera's lens. The method is designed to operate in the surveillance setting and makes use of good quality frames from the beginning of the recorded sequence to remove the blur at later stages caused by the dirty lens. A background subtraction method allows us to obtain a stable background of the scene. Based on this background, a multiframe blind deconvolution algorithm is used to estimate the Point Spread Function (PSF) of the blur. Once the PSF is obtained it can be used to deblur the entire scene. This restoration method was tested on both synthetic and real data with improvements of 15 dB in PSNR being achieved by using clean frames from the beginning of the recorded sequence.

    Research areas

  • adaptive signal processing, deconvolution, image restoration, image sequences, optical transfer function, video recording, video signal processing, video surveillance, PSF, adaptive deblurring, background subtraction method, blur removal, camera lens, multiframe blind deconvolution algorithm, point spread function, recording restoration, surveillance cameras, surveillance setting, surveillance video sequences, Cameras, Deconvolution, Dictionaries, Equations, Image restoration, Lenses, Surveillance, Blind Image Deconvolution, Point Spread Function, Video De-blurring

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