As more and more images are obtained by astronomical observations, a fast image quality evaluation algorithm is required for data processing pipelines. The image quality evaluation algorithm should be able to recognize blur or noise levels according to scientists’ requirements and further mask parts of images with low qualities. In this paper, we introduce a deep learning based image quality evaluation and fast masking algorithm. Our algorithm uses an auto-encoder neural network to obtain blur or noise levels and we further use blur or noise levels to generate mask maps for input images. Tested with simulated and real data, our algorithm could provide reliable results with small amount of images as the training set. Our algorithm could be used as a reliable image mask algorithm for different image processing pipelines.
The blurred range of astronomical image data we observe is usually uncertain, Due to the complex space environment, random noise, unpredictable atmospheric turbulence and other external factors. We usually use ground-based large aperture optical telescopes to observe astronomical images, which are mainly affected by atmospheric turbulence. Therefore, the restoration of astronomical images under the influence of arbitrary atmospheric turbulence is of great significance for the theoretical development and technological progress of astronomy. In this paper, a novel astronomical image restoration algorithm is proposed, which connects the deep learning based image restoration algorithm with the data generation method. The algorithm could effectively restore images within predefined blur or noise levels. We use long exposure galaxy images and short exposure Solar images to test the algorithm. We find that a well trained algorithm can restore these images.
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