Perception Evaluation -- A new solar image quality metric based on the multi-fractal property of texture features
This addresses the need for reliable image quality metrics in solar image processing, but it is incremental as it builds on existing texture and deep learning methods.
The authors tackled the problem of evaluating image quality for solar observations by proposing a new reduced-reference metric based on multi-fractal texture features, showing it provides robust estimates across different solar scenes.
Next-generation ground-based solar observations require good image quality metrics for post-facto processing techniques. Based on the assumption that texture features in solar images are multi-fractal which can be extracted by a trained deep neural network as feature maps, a new reduced-reference objective image quality metric, the perception evaluation is proposed. The perception evaluation is defined as cosine distance of Gram matrix between feature maps extracted from high resolution reference image and that from blurred images. We evaluate performance of the perception evaluation with simulated and real observation images. The results show that with a high resolution image as reference, the perception evaluation can give robust estimate of image quality for solar images of different scenes.