US 12,243,188 B2
Image processing method and device, computer storage medium and terminal
Dehui Kong, Guangdong (CN); Ke Xu, Guangdong (CN); Xiao Zhang, Guangdong (CN); Hong Wang, Guangdong (CN); Bin Han, Guangdong (CN); Ning Wang, Guangdong (CN); Xin Liu, Guangdong (CN); Guoning Lu, Guangdong (CN); and Fang Zhu, Guangdong (CN)
Assigned to SANECHIPS TECHNOLOGY CO., LTD., Guangdong (CN)
Appl. No. 17/618,534
Filed by SANECHIPS TECHNOLOGY CO., LTD, Guangdong (CN)
PCT Filed Apr. 16, 2020, PCT No. PCT/CN2020/085080
§ 371(c)(1), (2) Date Dec. 13, 2021,
PCT Pub. No. WO2020/248706, PCT Pub. Date Dec. 17, 2020.
Claims priority of application No. 201910516867.5 (CN), filed on Jun. 14, 2019.
Prior Publication US 2022/0245761 A1, Aug. 4, 2022
Int. Cl. G06K 9/00 (2022.01); G06T 3/4046 (2024.01); G06T 3/4053 (2024.01)
CPC G06T 3/4053 (2013.01) [G06T 3/4046 (2013.01)] 9 Claims
OG exemplary drawing
 
1. An image processing method, comprising:
determining convolution kernels of at least two sizes for feature extraction;
performing sparsity constraint for the determined convolution kernels of at least two sizes for feature extraction through a preset objective function; and
performing feature extraction on one convolutional layer of an image based on the convolution kernels of at least two sizes subjected to the sparsity constraint, wherein at least two sizes of the convolution kernels for feature extraction comprise at least two of following sizes: 3×3, 5×5, 7×7, or 9×9,
wherein the determining the convolution kernels of at least two sizes for feature extraction comprises:
determining at least two sizes of the convolution kernels for feature extraction according to a preset strategy of size selection;
determining a number of convolution kernels of each size of the at least two sizes according to a preset allocation strategy after the at least two sizes of the convolution kernels for feature extraction are determined,
wherein the determining the number of convolution kernels of each size of the at least two sizes according to the preset allocation strategy comprises:
determining the number of convolution kernels of each size of the at least two sizes by taking a scale factor as an allocation basis;
wherein the taking the scale factor as the allocation basis comprises: a proportion of small-size convolution kernels for feature extraction increases along with an increase of the scale factor,
wherein the scale factor is a pixel ratio at which the image changes from a high resolution to a low resolution.