Algorithm of Spatio-Temporal Combining Video Denoising Based on Structural Similarity

An algorithm of spatio-temporal combining video denoising based on structural similarity is proposed for the video surveillance system. By motion detection to multi-frame images with block structural similarity, this algorithm can adaptively distinguish the still regions and motion regions of video image. Temporal weighted average filter to the still regions and spatial ANL filter to the motion regions are used separately. Experimental results show that the proposed algorithm can improve the image quality and greatly reuduce the computation time.

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IP Video Surveillance System

Advances in Multimedia and Interactive Technologies - Video Surveillance Techniques and Technologies ◽

10.4018/978-1-4666-4896-8.ch022 ◽

2014 ◽

pp. 264-289

Keyword(s):

Image Quality ◽

Video Surveillance ◽

Motion Detection ◽

Surveillance System ◽

Video Surveillance System ◽

Light Sources ◽

Hard Drives ◽

Set Up ◽

Definition Of ◽

Network Camera

The chapter is a summary of IP surveillance systems: basic functions, the advantages of network video, customizing surveillance applications, and possible legal concerns. The most important step one can take before installing IP surveillance system is to define goals and requirements. Once these are determined, the video system can be set up. The required goals to be determined are the following: definition of the video surveillance system needs (installation plan, area of coverage, camera positioning, illumination conditions determination, camera cabling, the recording server positioning), network camera and/or video encoder selection (image quality, lens selection, network camera selection, Power over Ethernet [PoE], video motion detection, audio, accessories selection, testing), hardware (switches, additional light sources, power supplies, additional server for video management software, hard drives), software (software package selection, licenses, image quality and frame rate requirements, IP address range calculation, hard disk usage calculation, camera configuration, video motion detection settings, user access definition), and maintenance.

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Adaptive Video Denoising Based on Spatio-Temporal Combination

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.321-324.1230 ◽

2013 ◽

Vol 321-324 ◽

pp. 1230-1233

Author(s):

Hong Wei Di ◽

Kai Han Zhang ◽

Hui Gao

Keyword(s):

Hypothesis Test ◽

Linear Regression Analysis ◽

Interval Estimation ◽

Weighted Average ◽

Adaptive Threshold ◽

Threshold Function ◽

Video Denoising ◽

Adaptive Video ◽

Spatio Temporal ◽

Average Filter

An algorithm of adaptive video denoising base on spatio-temporal combination is demonstrated. The adaptive threshold function is obtained through unary linear regression analysis combining interval estimation and hypothesis test. By motion detection to multi-frame images, still regions and motion regions of video image are distinguished through the adaptive threshold. Temporal weighted average filter to the still regions and spatial ANL filter to the motion regions are used separately. Experimental results show that the proposed algorithm works well.

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