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Small-size image smoothing filtering detection algorithm based on quantization difference co-occurrence matrix

A technology of co-occurrence matrix and differential image, which is applied in the field of image processing and can solve problems such as identification that needs to be studied.

Pending Publication Date: 2021-12-28
PLA STRATEGIC SUPPORT FORCE INFORMATION ENG UNIV PLA SSF IEU
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Although there have been some achievements in the detection of median filtering on small-size images, the identification of other smoothing filters on small-size images remains to be studied.

Method used

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  • Small-size image smoothing filtering detection algorithm based on quantization difference co-occurrence matrix
  • Small-size image smoothing filtering detection algorithm based on quantization difference co-occurrence matrix
  • Small-size image smoothing filtering detection algorithm based on quantization difference co-occurrence matrix

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Embodiment 1

[0057] Based on the above research and development background, such as Figure 4 As shown, the embodiment of the present invention provides a smoothing filter detection feature extraction method based on quantized differential co-occurrence matrix, which is characterized in that it includes:

[0058] S101: For the image I to be feature extracted n , calculate the differential image of its 8 differential directions (p, q) where p,q∈{-1,0,1}; for each of the difference images Perform quantization to obtain 8 corresponding quantized difference images

[0059] Specifically, the image I to be feature extracted is calculated according to formula (4) n Difference image in 8 difference directions (p,q)

[0060]

[0061] Among them, n x,y represents the image I n The middle coordinate is the gray value of the (x, y) pixel, Represents the difference image The middle coordinate is the difference value of (x, y) pixel;

[0062] According to the formula (5) for each of ...

Embodiment 2

[0089] On the basis of above-mentioned embodiment 1, as Figure 5 As shown, the embodiment of the present invention also provides a small-size image smoothing and filtering detection algorithm based on a quantized differential co-occurrence matrix, including two stages of training and testing, and realizes the original image and various types of smoothing and filtering images by using a multi-classifier model. The detection specifically includes the following steps:

[0090] Training phase:

[0091] S201: Construct four types of training image sets, respectively: original image set (I ORI ), the median filtered image set (I MF ), the mean filter image set (I AF ) and the Gaussian filtered image set (I GF );

[0092] S202: For each type of training image set, assign a corresponding type label to each training image, and extract the detection feature of each training image according to the above-mentioned smoothing filter detection feature extraction method based on quantiz...

Embodiment 3

[0097] Traditional image mosaic region localization algorithms based on inconsistencies in local features generally divide the image into non-overlapping image blocks, then perform local feature detection on each image block, and finally determine image blocks that are inconsistent with global features. This positioning method will affect the positioning accuracy due to the limitation of the image block size, because the divided image blocks do not overlap. When the image block size is too small, the recognition accuracy is not ideal due to too few statistical samples; when the image block size is too large When , the positioning accuracy of the edge of the stitching area will be reduced. In view of the above-mentioned defects of the existing method, the embodiment of the present invention also provides a method for locating an image stitching area based on block smoothing filter detection, which includes the following steps:

[0098] S301: Convert the image to be detected int...

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Abstract

The invention provides a smoothing filtering detection feature extraction method based on a quantization difference co-occurrence matrix, a small-size image smoothing filtering detection algorithm and an image splicing area positioning method. The feature extraction method comprises the following steps: for an image In to be subjected to feature extraction, calculating to obtain difference images of the image In, quantizing each difference image, obtaining quantized difference images, and carrying out truncation processing on each difference image and a difference value in each quantized difference image; respectively calculating corresponding co-occurrence matrixes for each image and each image after truncation processing, and respectively recording the co-occurrence matrixes as a difference co-occurrence matrix and a quantization difference co-occurrence matrix; respectively carrying out average processing on the differential co-occurrence matrixes in the horizontal / vertical direction and the diagonal direction to obtain two co-occurrence matrixes Mh / v and Md; averaging the quantized difference co-occurrence matrixes in the horizontal / vertical direction and the diagonal direction to obtain two co-occurrence matrixes M'h / v and M'd; and combining the four co-occurrence matrixes Mh / v, M'h / v, Md and M'd into a feature W as a detection feature of the image In.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to a small-size image smoothing filter detection algorithm based on quantized differential co-occurrence matrix. Background technique [0002] The rapid development of digital image processing technology makes image editing, tampering and even forgery extremely easy. Some processing methods originally used to modify digital images are used by forgers to tamper with images. Therefore, it is particularly necessary to identify the processing history of digital images. The history of image processing mainly includes smoothing, sharpening, contrast enhancement, JPEG compression, etc. Among them, smoothing, as a common technology in digital image processing, is widely used in image denoising, blurring and beautification, but it is also often used by image forgers to cover up tampering Therefore, detecting whether the image has undergone smoothing and filtering is a strong eviden...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/45G06T7/00G06K9/62
CPCG06T7/45G06T7/0002G06T2207/20081G06T2207/20021G06F18/254G06F18/259
Inventor 刘粉林淡州阳巩道福李震宇杨春芳齐保军谭磊王艺龙卢昊宇杜寒松莫成渝
Owner PLA STRATEGIC SUPPORT FORCE INFORMATION ENG UNIV PLA SSF IEU
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