Example-based super-resolution algorithms, which predict unknown high-resolution image information using a relationship model learnt from known high-and low-resolution image pairs, have attracted considerable interest in the field of image processing. In this paper, we propose a multi-example feature-constrained back-projection method for image super-resolution. Firstly, we take advantage of a feature-constrained polynomial interpolation method to enlarge the low-resolution image. Next, we consider low-frequency images of different resolutions to provide an example pair. Then, we use adaptive kNN search to find similar patches in the low-resolution image for every image patch in the high-resolution low-frequency image, leading to a regression model between similar patches to be learnt. The learnt model is applied to the low-resolution high-frequency image to produce high-resolution high-frequency information. An iterative back-projection algorithm is used as the final step to determine the final high-resolution image. Experimental results demonstrate that our method improves the visual quality of the high-resolution image.
An earthquake of magnitude M4.1 occurred in Changqing County, Shandong Province (36.46°N, 116.65°E) on February 18, 2020. In this paper, we found from the image analysis of seismic activity and wave velocity ratio calculation before this earthquake that the magnitude M4.1 (No special markings on the magnitude of the earthquake: M for magnitude 5 (including magnitude 5) and above, M
L for below magnitude 5) in the 1-year scale before the Changqing M4.1 earthquake, inland Shandong and In the Bohai Sea, there is an orderly distribution of magnitude 2 earthquakes, multiple small earthquake sequences, a concentration with active of magnitude 3 earthquakes, the Changdao “earthquake window” “open” several times, and an abnormally low value of wave velocity ratio etc. Phenomena.
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