政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/142641

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/142641

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题名:	基於深度直方圖網路之水下影像還原模型 Underwater Image Restoration using Histogram-based Deep Networks
作者:	陳彥蓉 Chen, Yen-Rong
贡献者:	彭彥璁 Peng, Yan-Tsung 陳彥蓉 Chen, Yen-Rong
关键词:	影像處理影像還原直方圖深度學習 Image processing Image restoration Histogram Deep learning
日期:	2022
上传时间:	2022-12-02 15:20:32 (UTC+8)
摘要:	水下的環境複雜，能見度低，當我們拍攝水下物體或生物的照片時，總會產生模糊似霧或是水色失真的問題，導致看不清楚水下的狀況。由於光在水下傳播時的吸收、散射與衰減，導致水下圖像存在嚴重的色偏、模糊與低對比度的情況，因此我們提出了一個基於深度直方圖網路之水下影像還原的模型，應用深度學習的概念學習圖像的直方圖分布，學習好的水下圖像的直方圖分佈，來生成所需的直方圖，以增強圖像對比度和解決偏色問題。再者，我們結合了一個局部區塊優化的模型，進一步加強影像的視覺表現。此外，我們提出的網路結構設計，具有執行速度快的優點。透過實驗證明，我們提出的方法不僅可以完全地恢復水下圖像，而且在水下圖像恢復和增強的最新方法中表現良好。 The underwater environment is complex, and its visibility is low. When we take photos of underwater objects or creatures, there will always be blurry fog or water color distortion, making it difficult to see the underwater conditions. Due to the absorption, scattering, and attenuation of propagated light, underwater images are prone to severe color casts, blurriness, and low contrast. Therefore, we propose a model for underwater image restoration based on a deep histogram model, learning histogram distributions of good underwater images to produce the desired histogram for enhancing image contrast and resolving color cast problems. Furthermore, we combine a local optimization model to further increase the visual performance of the image. In addition, our proposed network structure design has the advantage of fast execution speed. Experimental results demonstrate that the proposed method performs favorably against state-of-the-art approaches for underwater image restoration and enhancement.
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描述:	碩士國立政治大學資訊科學系 109753204
資料來源:	http://thesis.lib.nccu.edu.tw/record/#G0109753204
数据类型:	thesis
DOI:	10.6814/NCCU202201675
显示于类别:	[資訊科學系] 學位論文

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