To meet the requirement of clear texture of coronary angiography images in interventional surgery, this article proposes a super-resolution image reconstruction method based on the omnidirectional deep weighted and lightweight network. Firstly, the local convolution module is designed to reduce the dimension of the feature map to reduce its parameter quantity and speed up the processing speed of the model. Then, the self-attention mechanism module is used to fuse the channel and spatial information of the image to obtain the rich high-frequency detail features of the image. In addition, to further extract the deep feature information of the image, a cascade and weight matching layer attention structure is designed to assign different weights to the features of different depths of the image to realize the super-resolution reconstruction of the image. Finally, to make the method have a stronger generalization ability in real interventional coronary angiography images, a coronary angiography image dataset (CAID) is constructed for training and testing the network model. The experimental results show that, compared with the Omni-SR algorithm, the proposed algorithm reduces the number of parameters by 32. 3% and the running time by 17. 74% . Meanwhile, the quality of the reconstructed image is better than other comparison algorithms in terms of objective indicators and subjective feelings. The average values of PSNR and SSIM are increased by 0. 72 dB and 0. 0122 on the CAID dataset, and 0. 13 dB and 0. 004 4 on the public dataset, respectively.