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Snow noise removing algorithm free of reference detection

A snowflake noise and algorithm technology, applied in the field of image processing, can solve the problem of time-consuming and labor-intensive subjective methods

Inactive Publication Date: 2014-08-20
WUHAN UNIV OF SCI & TECH
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Problems solved by technology

Subjective methods are time-consuming and labor-intensive, and are not suitable for multiple camera detection occasions

Method used

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  • Snow noise removing algorithm free of reference detection
  • Snow noise removing algorithm free of reference detection
  • Snow noise removing algorithm free of reference detection

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Embodiment Construction

[0030] Such as figure 1 As shown, in order to detect and eliminate snowflake noise, the present invention first classifies the pixels with adjacent positions and similar gray levels (colors) into one block, and judges the noise points by judging the size of the block area, and outputs the original value of the non-noise points, and takes The adaptive mean of the signal points replaces the snowflake noise points, and compares the filtering result with the original image grayscale deviation to detect the snowflake noise. by figure 2 For example, for an image containing noise, apply a no-reference snowflake noise detection and elimination algorithm, which includes steps:

[0031] 5. Input the image f, and divide the image into sub-blocks;

[0032] 6. Form a noise block candidate set by screening sub-blocks;

[0033] 7. Adaptive filtering is adopted for each block in the noise block candidate set to generate an estimated image of a clear image;

[0034] 8. Determine whether t...

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Abstract

Provided is a snow noise removing algorithm free of reference detection. First, pixel points adjacent in position and similar in gray scale (color) are classified into a block, and isolated blocks with the area smaller than a threshold value are judged to be noise points; the noise points are filtered, and non-noise points are directly output; when the number of signal points within a 7*7 neighborhood region around the noise points is zero, the noise points are not processed, and otherwise, a gray scale self-adaptation weighting average value of the signal points in the neighborhood region serves as a new gray scale value of the pixel points. The deviation between the filtering result and the original image pixel gray scale is subjected to statistics, snow noise is detected, when the deviation is larger than a certain value, it is thought that snow noise exists, the filtering result is output, otherwise, it is thought that no snow noise exists, and an original image is output.

Description

Technical field [0001] The invention belongs to the technical field of image processing, in particular to an algorithm for detecting and eliminating snowflake noise in video. Background technique [0002] Snowflake noise is an isolated point that has a large grayscale difference (color difference) with the surrounding background. There are five main reasons for the snowflake noise in the image captured by the camera: (1) the circuit is noisy, (2) power frequency interference, (3) high frequency interference, (4) the magnification is relatively large, (5) the magnetic head needs to be cleaned . [0003] Circuit noise consists of thermal noise and shot noise. Thermal noise is mainly generated by the random thermal motion of free electrons inside the conductor. Shot noise is generated by semiconductors within active devices such as amplifiers. When the camera end and the monitoring equipment end are grounded at the same time, due to the existence of ground resistance and ca...

Claims

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Application Information

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IPC IPC(8): G06T5/00
Inventor 陈黎刘佳祥田菁
Owner WUHAN UNIV OF SCI & TECH
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