{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T06:44:30Z","timestamp":1780555470964,"version":"3.54.1"},"reference-count":33,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2021,7,14]],"date-time":"2021-07-14T00:00:00Z","timestamp":1626220800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Infrared sensing technology is more and more widely used in the construction of power Internet of Things. However, due to cost constraints, it is difficult to achieve the large-scale installation of high-precision infrared sensors. Therefore, we propose a blind super-resolution method for infrared images of power equipment to improve the imaging quality of low-cost infrared sensors. If the blur kernel estimation and non-blind super-resolution are performed at the same time, it is easy to produce sub-optimal results, so we chose to divide the blind super-resolution into two parts. First, we propose a blur kernel estimation method based on compressed sensing theory, which accurately estimates the blur kernel through low-resolution images. After estimating the blur kernel, we propose an adaptive regularization non-blind super-resolution method to achieve the high-quality reconstruction of high-resolution infrared images. According to the final experimental demonstration, the blind super-resolution method we proposed can effectively reconstruct low-resolution infrared images of power equipment. The reconstructed image has richer details and better visual effects, which can provide better conditions for the infrared diagnosis of the power system.<\/jats:p>","DOI":"10.3390\/s21144820","type":"journal-article","created":{"date-parts":[[2021,7,14]],"date-time":"2021-07-14T21:56:51Z","timestamp":1626299811000},"page":"4820","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Blur Kernel Estimation and Non-Blind Super-Resolution for Power Equipment Infrared Images by Compressed Sensing and Adaptive Regularization"],"prefix":"10.3390","volume":"21","author":[{"given":"Hongshan","family":"Zhao","sequence":"first","affiliation":[{"name":"School of Electrical & Electronic Engineering, North China Electric Power University, Baoding 071003, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bingcong","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Electrical & Electronic Engineering, North China Electric Power University, Baoding 071003, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lingjie","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Electrical & Electronic Engineering, North China Electric Power University, Baoding 071003, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Jalil, B., Leone, G.R., Martinelli, M., Moroni, D., Pascali, M.A., and Berton, A. 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