Abstract:

Owing to the influence of intense snow and the magnetic field in Antarctica, the images collected during field object image monitoring at the Antarctic Research Station have natural or internal noise that seriously affects the image quality and thus affects the monitoring results. Therefore, this study proposed a progressive multi-stage image denoising algorithm based on attention and feature fusion to improve the clarity and realism of the image, eliminate the remaining noise and preserve the details and structure of the image, and reduce the computational complexity of high-resolution feature maps. The algorithm was verified using the object body dataset of monitoring images taken in Antarctica. The experimental results showed that the peak signal-to-noise ratio and structure similarity index measure of the monitored images were 41.82 dB, 38.04 dB, 37.08 dB and 0.991, 0.952, 0.938, respectively, in the presence of salt and pepper noise, periodic noise, and Gaussian noise with standard deviation of 70. The algorithm performed better than mainstream denoising methods and had lower model complexity and stronger noise suppression ability and anti-interference ability. Therefore, the algorithm provides a more reliable technical means for managing the unmanned image monitoring technology in the Antarctic research station.

Key words: Antarctic, target object, image monitoring, deep learning, multi-stage image denoising