1. NCCU Academic Hub
  2. 學術產出
  3. 資訊學院
  4. 期刊論文
  5. Item

學術產出-期刊論文

文章檢視/開啟

書目匯出

Google ScholarTM

政大圖書館

引文資訊

TAIR相關學術產出

題名 Image Impulse Noise Removal Using Cascaded Filtering Based on Overlapped Adaptive Gaussian Smoothing and Convolutional Refinement Networks
作者 彭彥璁
Peng, Yan-Tsung
Huang, Sha-Wo
貢獻者 資科系
關鍵詞 Denoising; cascaded filtering; adaptive Gaussian filtering; convolutional refinement networks
日期 2021-10
上傳時間 6-二月-2023 14:30:30 (UTC+8)
摘要 Impulse noise is often introduced to images when captured through image sensors due to sharp and sudden disturbances in the image signal, analog-to-digital converter errors, sensor temperature, etc., severely degrading their visual quality. Therefore, it is essential to develop an effective method to remove image noise. We propose a novel image denoising method for “salt-and-pepper” (SP) noise, using cascaded filtering based on overlapped adaptive Gaussian smoothing (OAGS) and the convolutional refinement networks (CRNs). First, the noisy input image can be preliminarily denoised by OAGS, where the noisy pixels are removed and recovered. The CRNs refine the result by restoring fine details for the denoised image. Through extensive experimental results, we demonstrate the proposed method substantially outperforms other state-of-the-art denoising methods, especially for high-density SP noise.
關聯 IEEE Open Journal of the Computer Society, Vol.2, pp.382-392
資料類型 article
DOI https://doi.org/10.1109/OJCS.2021.3117738
dc.contributor 資科系
dc.creator (作者) 彭彥璁
dc.creator (作者) Peng, Yan-Tsung
dc.creator (作者) Huang, Sha-Wo
dc.date (日期) 2021-10
dc.date.accessioned 6-二月-2023 14:30:30 (UTC+8)-
dc.date.available 6-二月-2023 14:30:30 (UTC+8)-
dc.date.issued (上傳時間) 6-二月-2023 14:30:30 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/143300-
dc.description.abstract (摘要) Impulse noise is often introduced to images when captured through image sensors due to sharp and sudden disturbances in the image signal, analog-to-digital converter errors, sensor temperature, etc., severely degrading their visual quality. Therefore, it is essential to develop an effective method to remove image noise. We propose a novel image denoising method for “salt-and-pepper” (SP) noise, using cascaded filtering based on overlapped adaptive Gaussian smoothing (OAGS) and the convolutional refinement networks (CRNs). First, the noisy input image can be preliminarily denoised by OAGS, where the noisy pixels are removed and recovered. The CRNs refine the result by restoring fine details for the denoised image. Through extensive experimental results, we demonstrate the proposed method substantially outperforms other state-of-the-art denoising methods, especially for high-density SP noise.
dc.format.extent 105 bytes-
dc.format.mimetype text/html-
dc.relation (關聯) IEEE Open Journal of the Computer Society, Vol.2, pp.382-392
dc.subject (關鍵詞) Denoising; cascaded filtering; adaptive Gaussian filtering; convolutional refinement networks
dc.title (題名) Image Impulse Noise Removal Using Cascaded Filtering Based on Overlapped Adaptive Gaussian Smoothing and Convolutional Refinement Networks
dc.type (資料類型) article
dc.identifier.doi (DOI) 10.1109/OJCS.2021.3117738
dc.doi.uri (DOI) https://doi.org/10.1109/OJCS.2021.3117738