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.
초록·키워드
The field of image denoising has undergone significant advancements over the years. Recently, Convolutional Neural Networks (CNN) based denoising methods have shown remarkable performance in image denoising. Most of these adopt single-scale features, which may have limitations in denoising real-world images. Real-world noise is complex and non-Gaussian in nature. The multi-scale strategy of the Gaussian pyramid (GP) facilitates the attenuation of noise while preserving image details. Additionally, this multiscale architecture inherently reduces the data's dimensionality, resulting in decreased computational complexity. Over the past few decades, this method has been employed for image denoising; however, its application to real-world images remains computationally challenging. In this study, we implemented the GP method for denoising X-ray, MRI, non-medical images, and SIDD datasets. Furthermore, its denoising performance is compared with the wavelet transforms (Coiflet4, Haar, Daubechies, and Symlets). Quantitatively, GP achieves a significant improvement in PSNR, SSIM, and computational complexity compared to the wavelet method. PSNR of 36.8024 dB, SSIM of 0.9428, and computational complexity of 0.0046 s have been achieved, thereby offering an effective and practical solution for real-world image applications.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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