Zero-Shot Ultrasound Nondestructive Testing Image Super-Resolution Based on Reflection Projection
Python
Zero-Shot Ultrasound Nondestructive Testing Image Super-Resolution Based on Reflection Projection
In ultrasonic nondestructive testing, the low resolution of ultrasound images possibly lead
to misinterpretation of defects in the image. At present, there is no spe-cial data set for
ultrasonic nondestructive testing images in super-resolution, and the performance of numerous
existing models depends on the learning of general data sets. In this paper, a zero-shot
super-resolution network based on reflection projection units is proposed. Ultrasound images
contain numerous image blocks with similar content, which are randomly extracted and down-sampled
to form training samples. Then the image features are extracted through the reflection projection
units in the network, and the information between the high and low-resolution image pairs is
fully excavated. Finally, the feature channel is reduced by the attention mechanism, and the
reconstructed image is output. Moreover, a combined loss function is used to optimize the
network parameters. The com-pared experiments show that the proposed method performs better
than the state of the art.
If you need to use this code, just change the relevant path. Fine tuning parameters may make your experiment better.
The contribution of this paper benefits from “Zero-Shot” Super-Resolution using Deep Internal Learning"
You can modify relevant parameters in "configs.py" to meet your needs.
such as
result_path = os.path.dirname(file) + '/results'
input_path = local_dir = os.path.dirname(file) + '/input'