Abstract
This paper aims to extract the exact defect characteristics of the thin film surface of the lithium battery by sparse decomposition algorithm. An appropriate atomic function was selected and the sparse decomposition iteration was conducted on the defect images in the overcomplete dictionary. This value from observation method was taken as the empirical value and applied as the iteration termination condition of the sparse decomposition. Then, the denoised defect images were obtained. The results reveal that the sparse decomposition has a far superior denoising performance to that of the median filtering technique, and can better restore the thin film defects of the lithium battery.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
