US 12,223,630 B2
Image data processing method, system and electronic device determining similar target image
Guangyun Wang, Beijing (CN); Qiao Ye, Beijing (CN); Ruibo Qi, Jiangsu (CN); Yuan Luo, Beijing (CN); Zhusong Mei, Beijing (CN); Bingqian Guo, Beijing (CN); Longmei Fang, Beijing (CN); Chengxiang Zhu, Jiangsu (CN); Guiling Bi, Jiangsu (CN); Yao Li, Jiangsu (CN); and Yin Zhang, Jiangsu (CN)
Assigned to Air Force Medical Center, PLA, Beijing (CN)
Filed by Air Force Medical Center, PLA, Beijing (CN)
Filed on Jul. 5, 2024, as Appl. No. 18/764,606.
Claims priority of application No. 202310835675.7 (CN), filed on Jul. 10, 2023.
Prior Publication US 2025/0022109 A1, Jan. 16, 2025
Int. Cl. G06T 5/00 (2024.01); G06T 5/50 (2006.01); G06T 5/73 (2024.01); G06V 10/36 (2022.01); G06V 10/74 (2022.01); G06V 10/764 (2022.01)
CPC G06T 5/73 (2024.01) [G06T 5/50 (2013.01); G06V 10/36 (2022.01); G06V 10/761 (2022.01); G06V 10/764 (2022.01); G06T 2207/30168 (2013.01)] 8 Claims
OG exemplary drawing
 
1. An image data processing method, comprising steps of:
S1, acquiring information of to-be-processed database data and a contrast image, and recording the to-be-processed database data as a target image set;
S2, performing sharpness calculation on each of target images in the target image set by a weighted gradient algorithm, setting a sharpness threshold, and removing, if sharpness of the target images is smaller than the sharpness threshold, the target images from the target image set,
wherein a method of performing sharpness calculation on the target images in the target image set by the weighted gradient algorithm is:
D(f)=αΣxΣy|f(x+2,y)−f(x,y)|2+βΣxΣy|G(x,y)|,
in which, D(f) is sharpness, f(x, y) is a gray-scale value of a corresponding pixel point (x, y) of a target image f, x and y respectively represent abscissa and ordinate of the pixel point, G(x, y) is convolution of Laplacian operator of the pixel point (x, y), α and β are weighted values, and α+β=1;
S3, performing sharpness enhancement processing on remaining target images in the target image set;
S4, performing, after the sharpness enhancement processing, extraction on the remaining target images in the target image set and the contrast image one by one, to obtain feature points, and comparing and analyzing the extracted feature points; and
S5, comparing and judging, based on the feature points obtained in S4, similarity between the remaining target images in the target image set and the contrast image by a similarity algorithm, creating classified storage spaces within a database, and inputting similar target images into the classified storage spaces, wherein the similar target images are corresponding target images having similarity within a set similarity threshold range.