{'fraction_non_alphanumeric': 0.010845986984815618, 'fraction_numerical': 0.0021691973969631237, 'mean_word_length': 4.80503144654088, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Adaptive deblurring of surveillance video sequences that deteriorate over time

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.