融合暗原色先驗和稀疏表示的水下圖像復原

王鑫; 朱行成; 寧晨; 呂國芳

doi:10.11999/JEIT170381

融合暗原色先驗和稀疏表示的水下圖像復原

doi: 10.11999/JEIT170381

1.
(河海大學計算機與信息學院南京 211100)
2.
(南京師范大學物理科學與技術(shù)學院南京 210000)

基金項目:

國家自然科學基金面上項目(61374019)，國家自然科學基金青年基金(61603124)，教育部中央高?；究蒲袠I(yè)務費專項資金(2015B19014)，江蘇省333高層次人才培養(yǎng)工程，江蘇省六大人才高峰高層次人才項目(XYDXX-007)

計量
- 文章訪問數(shù): 1500
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- PDF下載量: 251
- 被引次數(shù): 0
出版歷程
- 收稿日期: 2017-04-25
- 修回日期: 2017-09-12
- 刊出日期: 2018-02-19

Combination of Dark-channel Prior with Sparse Representation for Underwater Image Restoration

1.
(College of Computer and Information, Hohai University, Nanjing 211100, China)
2.
(School of Physics and Technology, Nanjing Normal University, Nanjing 210000, China)

Funds:

The National Natural Science Foundation of China (61374019, 61603124), The Fundamental Research Funds for the Central Universities (2015B19014), 333 High-Level Talent Training Program of Jiangsu Province, Six Talents Peak Project of Jiangsu Province (XYDXX-007)

摘要

摘要: 由于水下圖像成像過程中受光的散射、噪聲干擾等因素影響，致使圖像質(zhì)量嚴重退化。為了去除模糊和抑制噪聲，改善水下圖像質(zhì)量，該文提出一種融合暗原色先驗和稀疏表示的水下圖像復原新方法。該方法首先利用暗原色先驗理論計算水下圖像的暗原色，然后基于稀疏表示理論對暗原色進行去噪和優(yōu)化，基于改進后的暗原色計算水體透射率和光照強度以計算最終復原結(jié)果，可以同時達到去模糊和去噪的良好效果。實驗結(jié)果表明，提出的方法有效提高了圖像的平均梯度和信息熵等圖像像素，從而改善了圖像的質(zhì)量。
- 水下圖像復原 /
- 暗原色先驗 /
- 稀疏表示
Abstract: Due to the influences of scattering of the light and interference of the noise, underwater image quality is always degraded severely. In order to remove the blur and suppress the noise, and improve the quality of underwater image, a novel underwater image restoration method based on the combination of dark-channel prior with sparse representation is proposed. This method adopts the dark-channel prior theory to calculate the dark-channel image at first, and then uses sparse representation to denoise and optimize the dark-channel image. Based on the improved dark-channel image, the more precise water transmissivity and light intensity can be achieved to compute the final restoration result, effectively eliminating the image blur as well as noise. The experimental results show that the proposed method can effectively improve the image factors, such as average gradient and entropy, so as to compensate the degraded image.
- Underwater image restoration /
- Dark-channel prior /
- Sparse representation

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參考文獻(22)

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