光線在水中傳播時,會受水中懸浮微粒的影響,造成散射、吸收等現象,導致水下能見度降低,並使水下影像產生霧化以及對比度不足的問題。另一方面,當可見光進入水層後,會因為波長不同的關係,而逐漸被水吸收,造成水下影像呈現偏藍、綠的色偏現象。本研究中,我們提出一個新的水下自適應修復演算法,其中包含兩大步驟:能見度修復和真實度修復。在能見度修復部分,我們觀察發現,水下影像之霧化現象與一般霧化影像皆有低對比度與色散問題。因此,本文採用霧化模型,並使用暗黑頻道(Dark channel)技術來除霧,以增加影像能見度。在真實度修復部分,我們先對RGB(Red, Green, Blue)頻道做線性轉換,以去除過於突出之色彩,均化各個色彩頻道亮度值。然後使用CLAHE(Contrast limited adaptive histogram equalization)方法增強影像之區域對比度,使得明暗區域細節突顯。最後,在HSI(Hue, Saturation, Intensity)色彩空間,對S及I頻道做直方圖伸展,增加影像之真實度。實驗結果顯示,本研究成功地修復了水下影像,並有效地改善不同類別水下影像之能見度及真實度。
When light is transmitted in water from a subject to an observer, it is scattered and absorbed by the unstable environment such as suspended particles and turbid water. Due to these phenomena, underwater images usually have poor quality including low contrast, blurring, darkness, and color diminishing. In this thesis, we propose a new underwater image restoration algorithm that consists of two major phases: visibility restoration and fidelity restoration. In the first phase, underwater images are observed similar to haze images because they have the same problems of low contrast and color shifting. This motivated us to use the haze removal technique, namely, dark channel prior, to dehaze underwater images. Subsequently, in the second phase, we equalize the color mean in each RGB (Red, Green, Blue) channel to balance the color. We then use the CLAHE (Contrast limited adaptive histogram equalization) method to enhance the local contrast of the image in the CIELAB color space. Finally, we perform histogram stretching on the S and I channels of the HSI (Hue, Saturation, Intensity) color space to make the image more natural. Preliminary results indicated that the proposed method effectively improved visibility and fidelity of underwater images.
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