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Please use this identifier to cite or link to this item:
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| Title: | 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 |
| Authors: | 陳彥蓉
Chen, Yen-Rong |
| Contributors: | 彭彥璁
Peng, Yan-Tsung
陳彥蓉
Chen, Yen-Rong |
| Keywords: | 影像處理
影像還原
直方圖
深度學習
Image processing
Image restoration
Histogram
Deep learning |
| Date: | 2022 |
| Issue Date: | 2022-12-02 15:20:32 (UTC+8) |
| Abstract: | 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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| Description: | 碩士
國立政治大學
資訊科學系
109753204 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0109753204 |
| Data Type: | thesis |
| DOI: | 10.6814/NCCU202201675 |
| Appears in Collections: | [資訊科學系] 學位論文
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