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A method for removing small area noises in a binary image based on a stack theory

A binary image, small-area technology, applied in image enhancement, image data processing, instruments, etc., to achieve short running time, low complexity, and good denoising effect

Inactive Publication Date: 2014-03-19
DALIAN OCEAN UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

However, so far, there is no relevant report on the removal of small area noise in binary images based on stack theory.

Method used

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  • A method for removing small area noises in a binary image based on a stack theory
  • A method for removing small area noises in a binary image based on a stack theory
  • A method for removing small area noises in a binary image based on a stack theory

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Embodiment Construction

[0019] Based on the stack theory to remove such as figure 1 For the method of small-area noise in the binary image shown, the threshold is set to 5000, and the steps are as follows:

[0020] a. When the stack is empty, sequentially scan each pixel in the binary image that is not stacked;

[0021] b. If the current pixel is black, push the current pixel into the stack and mark that the pixel has been pushed into the stack;

[0022] c. At this time, the stack is not empty, pop the top of the stack and incrementally record the area of ​​the connected area, and at the same time scan the eight points around the marked pixels into the stack in sequence;

[0023] d. Repeat steps b and c to scan the entire connected area, and judge whether the connected area is smaller than the set threshold. If yes, remove the connected area; otherwise, keep it.

[0024] Repeat steps a~d until the entire binary image is scanned once to remove all noise.

[0025] Because the number of pixels (gener...

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Abstract

The invention discloses a method for removing small area noises in a binary image based on a stack theory, and the method can reduce operation complexity, decrease operation time and has relatively high robustness. The method comprises the following steps: a, when a stack is empty, each pixel which does not enter the stack in the binary image is scanned sequentially; b, if a present pixel is black, the present pixel is put into the stack and it is marked that the pixel which is already put into the stack; c, in the moment, the stack is not empty; a stack top is enabled to be out of the stack and areas of communicated regions are recorded in a progressive increasing mode, and eight points around the pixel which is marked to be in the stack are sequentially scanned; d, the step b and the step c are cycled to complete scanning of the whole communicated region; whether the communicated region is smaller than a set threshold is determined; if the communicated region is smaller than the set threshold, the communicated region is removed; and if the communicated region is not smaller than the set threshold, the communicated region is saved.

Description

technical field [0001] The invention relates to a method for removing small-area noise in a binary image, in particular to a method for removing small-area noise in a binary image based on stack theory, which can reduce running complexity, reduce running time, and has better robustness. Background technique [0002] At present, for the method of removing small-area noise in binary images, the threshold area method described by Professor Yang Shuying in the course "VC++ Image Processing Programming" can be adopted and can achieve rational results. All the connected regions in the algorithm are numbered separately and the area of ​​each region is counted at the same time, and then the regions whose area is smaller than the threshold are divided and eliminated multiple times according to the label. Although the denoising effect of this method is good, the complexity is high (for an image with a size of 256×256, the complexity is equal to the number of scans multiplied by 256 2...

Claims

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Application Information

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IPC IPC(8): G06T5/00
Inventor 郭显久耿春云
Owner DALIAN OCEAN UNIV
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