Low-Quality Ultrasound Image Enhancement Using Spatially Variable Neural Implicit Network

Ultrasound images are widely used because they are easy to use and non-invasive. However, differences in acquisition equipment and frequency settings can result in varying image quality. To address this issue, we propose an ultrasound image enhancement method based on spatially variable neural implicit networks. Our approach consists of several steps: First, the encoder of an arbitrary-scale super-resolution network processes low-quality ultrasound images to generate a feature matrix. Concurrently, the parameter generation network decomposes the low-quality image into a parameter matrix. The pixel prediction module of the arbitrary-scale super-resolution network then uses the parameter matrix to map encoded features to pixel values. To further align the predicted pixel values with the distribution of high-quality ultrasound images and reduce interference from artifacts, we design a spectrum discriminator network based on the fast Fourier transform. This network is highly effective at capturing high-quality information and minimizing the impact of redundant information. In both qualitative and quantitative comparisons with current state-of-the-art image enhancement methods and arbitrary magnification techniques, our approach demonstrates superior performance.