인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 저자정보
피인용 0
초록·키워드
In the digital age, visual art images serve as important carriers of information transmission and aesthetic expression, and its integrity and quality are crucial. To repair damaged or degraded art images, a regularized low-rank matrix restoration algorithm is designed to repair visual art images. A low-rank matrix recovery method based on regularized singular values is proposed by incorporating regularization strategies and singular value entropy functions. This algorithm repairs visual art images of different types and styles, and evaluates its restoration effects. From the experimental results, the relative error of the low-rank matrix restoration algorithm based on regularized singular value function was 0.001, the running time was 28.54 seconds, and the F1 value was 92.51. The algorithm had a relatively high peak signal-to-noise ratio on different images, with an average of 0.93. The results indicate that the low-rank matrix restoration algorithm based on regularized singular value function has good image quality and small difference from the original image. The regularized low-rank matrix restoration algorithm can effectively repair visual art images and improve image quality and observability. The research provides solid theoretical support for image restoration, presents strong guidance for algorithm design and improvement, and displays useful reference and guidance for other related fields.
본문·목차
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