CN111915547A - Method for rapidly extracting noise points in image - Google Patents

Method for rapidly extracting noise points in image Download PDF

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CN111915547A
CN111915547A CN201910366985.2A CN201910366985A CN111915547A CN 111915547 A CN111915547 A CN 111915547A CN 201910366985 A CN201910366985 A CN 201910366985A CN 111915547 A CN111915547 A CN 111915547A
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matrix
noise
noise points
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刘辛越
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Beijing Creative Centure Information Technology Co ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10004—Still image; Photographic image
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
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    • G06T2207/20024—Filtering details

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Abstract

The invention provides a method for rapidly extracting noise points in an image. The invention firstly determines a 256 × 256 large matrix template, continuously moves downwards along the diagonal from the left top of the image, moves one 256 × 256 matrix at a time, moves the matrix at the left top of the image to the right by 256 pixels to reposition the starting point when the edge of the image is reached, and then moves downwards along the diagonal until noise points meeting the number requirement are extracted or all noise points in the upper right half corner of the image are extracted. The invention can quickly extract the noise data in the image to be used as a high-quality entropy source of the random number generator.

Description

Method for rapidly extracting noise points in image
Belongs to the technical field of:
the invention relates to the field of information processing, in particular to an image noise data extraction method, and relates to an image data processing method.
Background art:
during the formation, transmission, reception and processing of images, external and internal interferences inevitably exist due to the limitations of the actual performance of the transmission medium passing through and the performance of the receiving equipment, and therefore various noises are generated (conway, wangbo tree, a method of image filtering based on image enhancement [ J ]. university of wuhan (information science edition), 2009, 34 (7): 822-825.).
The processing of noise in the digital image field is called filtering, and common filtering methods include median filtering, mean filtering, gaussian filtering, bilateral filtering, and the like.
The mean filtering method is a method of selecting a template for a current pixel to be processed, the template being composed of a plurality of adjacent pixels, and replacing the value of the original pixel with the mean value of the template, as follows:
Figure BSA0000182539140000011
m is a matrix template weight coefficient, s is a matrix template, f (x, y) is a pixel gray value in the matrix template, g (x, y) is the average value of all pixel gray values in the matrix template, the gray value represents the brightness degree of an image, pure white is 255, and pure black is 0.
As shown in the following table, the average value of f (x, y) is calculated, and the matrix template weight coefficient M is 9, so that:
Figure BSA0000182539140000012
for example, if the gray-level value of a picture is shown in table (a) of fig. 1, after filtering the picture by the mean filtering method (M is 9), the gray-level value of the filtered picture can be obtained as shown in table (b) of fig. 1.
The local points in the natural image have correlation, and the brightness of the local points has the characteristic of smooth transition. If a noise point blurs a point, the gray value of the point is reduced, and the gray value of the point is smaller than the average gray value of the window in the neighborhood of the point. Therefore, based on the method of mean filtering, noise extraction can be performed on the image. The mean filtering method is simple and can well find noise points.
However, an image shot by the Android device is large in size, noise points are concentrated in the middle of the image, and if the images are sequentially accessed from the top of the image from left to right, the matrix template may need to be moved many times to acquire enough data, so that efficiency is affected.
Moreover, if the same area is continuously photographed, the obtained image data has a large degree of similarity, and thus the extracted noise data is also largely the same, which also affects the efficiency.
The invention content is as follows:
the invention aims to provide a method for rapidly extracting noise points in an image.
The invention discloses a method for quickly extracting noise points in an image, which comprises the following steps:
1. calculating the similarity between the current shot image and the shot image, and discarding the current image for shooting again when the similarity is more than 50;
2. starting from an (i, j) ═ 0, 0 pixel point of the current image, selecting a matrix A with the size of n multiplied by n, and if the matrix A is insufficient, fully selecting the matrix A;
3. within matrix a, a is divided into n m × m small matrices, within each small matrix B:
a) all points in matrix B are represented by coordinates (x, y), both x and y ranging from [0, m-1 ];
b) calculating g (x, y) according to an average filtering method, if g (x, y) is larger than the original gray value f (x, y), considering f (x, y) as a noise point, and making f (x, y) equal to g (x, y), otherwise, f (x, y) is not the noise point, and the value is not changed;
4. and continuously moving downwards along the diagonal line of the matrix A, moving one n multiplied by n matrix at a time, when the edge of the image is reached, moving the matrix at the top left part right by n pixels to reposition the initial point, and moving downwards along the diagonal line until noise points meeting the requirement of the number are extracted or all the noise points in the upper right half corner of the image are extracted.
The similarity of the images is calculated through a mean hash algorithm (Huangjia identity, image similarity algorithm comparison research based on hash, university college newspaper, 12 th 2017).
The mean hash algorithm is designed based on a pixel domain, the principle is simple, the realization speed is high, and the algorithm flow is as follows:
1) downscaling pictures to nxn, n in total2A plurality of pixels;
2) converting the nxn picture into a gray scale image, which is marked as Ga；
3) Calculating a gray-scale map GaPixel average of (2), denoted as pavg；
4) Traverse GaEach pixel p iniAnd p isiAnd pavgMaking a comparison if pi≥pavgIf not, then note down 1, otherwise note down 0, get n2The binary string of each bit is the picture aHash value, which is marked as Ha；
And calculating the Hamming distance between the hash values of the two pictures, wherein the pictures are more similar when the distance is smaller, and the difference of the pictures is larger when the distance is larger.
The invention improves the mean filtering method: if the matrix template is 3 × 3, the matrix template weight coefficient M is always 9, and the result g (x, y) of the average filtering calculation is determined to be a noise point if g (x, y) is greater than the original gray value f (x, y), and let f (x, y) equal to g (x, y), otherwise f (x, y) is not a noise point and its value is not changed.
And extracting noise from the image according to an improved mean filtering method, wherein an extracted noise point diagram 1 is shown.
The method can quickly extract the noise points in the image and can be used as a high-quality entropy source of a random number generator.
Description of the drawings:
FIG. 1 is a schematic diagram of noise point extraction by the method of the present invention
FIG. 2 is a schematic flow chart of the method of the present invention
The specific implementation mode is as follows:
after the image is shot, the features of the image are extracted firstly, and the features of the latest images are stored. And after the image is newly shot every time, comparing the image with the stored image characteristics, and if the similarity is large, shooting again.
Specifically, the method is based on a mean value hash algorithm, similarity comparison is carried out on the shot images, if the similarity is larger than 50 percent, the shot images are considered to be approximate images and need to be shot again, and if the similarity is smaller than 50 percent, the shot images are considered to be different images and noise extraction is carried out.
The invention assumes that the size of the image is height × width, as shown in fig. 2, the method of the invention firstly determines a 256 × 256 large matrix template, starts from the left top of the image, continuously moves downwards along the diagonal line, moves one 256 × 256 matrix at a time, moves the matrix at the left top of the beginning to the right by 256 pixels to reposition the starting point when reaching the image edge, and then moves downwards along the diagonal line until the noise points meeting the number requirement are extracted or all the noise points in the upper right half corner of the image are extracted.
The specific process is as follows:
1) selecting a matrix A with the size of 256 multiplied by 256 from (i, j) ═ 0, 0 pixel points of the image, and if the matrix A is insufficient, fully selecting the matrix A;
2) within matrix a, a is divided into 256 small matrices of 16 × 16, within each small matrix B:
a) all points in matrix B are represented by coordinates (x, y), both x and y ranging from [0, 15 ];
b) recording all the coordinates of the noise points by using a rewritten mean filtering method;
c) finding the middle point of all noise points, extracting the coordinate (x) of the middle point0，y0) X is to be0And y0Conversion to binary, in x0|y0Is output in the form of (1), wherein x0|y0That is, the data extracted from the small matrix B; if the matrix B has no noise points, extracting is not carried out;
3) moving the pixel point (i, j) downwards along the diagonal line of the image, namely (i, j) ═ i +256, j +256, if the pixel point (i, j) reaches the edge of the image, the pixel point (i, j) ═ i +256k, j) is re-ordered, k is the number of times of the point reaching the edge, until the upper right corner pixel of the image is completely read, if the number of bits required to be extracted is not met at the moment, the pixel point (i, j) is collected from the position (0, 0) again;
since the x and y coordinates of the small matrix B are both 4 bits, the data x extracted from B0|y 08 bits (1 byte) so that 0-256 bytes of noise data can be extracted from a 256 x 256 matrix a, and 0-256n bytes of noise data can be extracted from an image if there are n such a matrices for the image.
The noise data extracted from the image according to the method of the present invention can be used as a source of entropy in a random number generator to generate a sequence of random numbers with high entropy.

Claims (1)

1. A method for rapidly extracting noise points in an image comprises the following steps:
1) calculating the similarity between the current shot image and the shot image, and discarding the current image for shooting again when the similarity is more than 50;
2) starting from an (i, j) ═ 0, 0 pixel point of the current image, selecting a matrix A with the size of n multiplied by n, and if the matrix A is insufficient, fully selecting the matrix A;
3) within matrix a, a is divided into n m × m small matrices, within each small matrix B:
a) all points in matrix B are represented by coordinates (x, y), both x and y ranging from [0, m-1 ];
b) calculating g (x, y) according to an average filtering method, if g (x, y) is larger than the original gray value f (x, y), considering f (x, y) as a noise point, and making f (x, y) equal to g (x, y), otherwise, f (x, y) is not the noise point, and the value is not changed;
4) and continuously moving downwards along the diagonal line of the matrix A, moving one n multiplied by n matrix at a time, when the edge of the image is reached, moving the matrix at the top left part right by n pixels to reposition the starting point, and moving downwards along the diagonal line until noise points meeting the number requirement are extracted or all noise points in the upper right half corner of the image are extracted.
CN201910366985.2A 2019-05-07 2019-05-07 Method for rapidly extracting noise points in image Pending CN111915547A (en)

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CN104978715A (en) * 2015-05-11 2015-10-14 中国科学院光电技术研究所 Non-local mean image denoising method based on filtering window and parameter self-adaption
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US20060115159A1 (en) * 2004-11-30 2006-06-01 Astrit Rexhepi Boundary detection for images using coocurrence matrices
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