Ultrasonic image processing based on fusion super-resolution reconstruction of familiar models

Abstract Ultrasound image technology is to measure the energy and time of arrival of the reflected echo after the pulse acoustic signal is sent out by the ultrasonic wave. Usually, the distance between the ultrasonic source and the reflector is measured. Super-resolution image reconstruction aims to recover high-resolution images from one or more low-resolution images. Super-resolution image reconstruction is a software approach to solve the problem of low-resolution images by overcoming the limitations of hardware. In this paper, an image super-resolution reconstruction method based on sparse representation model and multi-feature fusion is proposed. Sparse dictionary is used to learn and reconstruct the luminance details of ultrasonic images, and edge interpolation is used to improve the edge clarity. Experimental results show that the proposed method is superior to Bicubic interpolation, SCSR and JOR in PSNR, and is 0.35 dB higher than RAISR. On the FSIM index, this method is also slightly better than other comparison methods, which can get better reconstruction results. Visual effects and numerical evaluation results of reconstructed images are better than several comparison methods.