Ultrasonic Image Optimization based on Double Constraint Algorithm

Abstract

The study of ultrasonic image deblurring is of high research value and practical significance, which can provide significantly useful medical pathological information for doctors and patients. Ultrasonic images are prone to speckle noise or blur. Real-time deblurring of ultrasonic images is a common problem in medical circles in China and globally. In this paper, based on the causes of ultrasound image degradation and its degradation model, the problem of ultrasound image deblurring is studied in depth. A new regularization constraint model is given, and the algorithm of ultrasound image deblurring with double constraints is effective. Through the experiment, the image quality evaluation index is used to evaluate the experimental results. The improved algorithm proposed in this paper improves the peak signal-to-noise ratio by 7%, the structural similarity value by 6%, the visual information fidelity by 14%, the feature similarity value by 4%, the resolution gain by 4%, and the new blind image quality evaluation value by 22%. The key information of the image can be kept faithfully. Compared with the traditional methods, the proposed method enhances the identification of the focus area in ultrasound images, provides a fast, efficient and complete deblurring method for medical image analysis, and faithfully retains and restores the required information in ultrasound images.