Ultrasound Image Enhancement Using a Deep Learning Architecture
Ultrasound images have been used for detecting several diseases such as kidney stones and breast tumors. However, ultrasound images suffer from speckle noise and several artifacts, thus degrading the quality of the images. In this paper, we propose a new method for enhancing the quality of ultrasound images. This method is a multi frame super resolution approach, where it extracts a high resolution image from a set of low resolution images (down sampled, blurred, and shifted versions of a high resolution source image) The critical step in multi frame super resolution approaches is motion estimation, especially when there is noise in the images. To cope with this issue, we propose the use of a deep learning based method for motion estimation. Experimental results using synthetic and realistic sequences demonstrate that our proposed approach is feasible and effective for enhancing the quality of ultrasound images.
AISI