本研究提出一組兼顧影像還原的準確度，又能夠強化影像的亮度、對比、與細節等視覺品質的演算法組合。此組合包含新的去霧與強化演算法。去霧演算法是一種影像還原演算法；因此，還原的準確度是評測去霧演算法效能優劣的重要指標。傳統的影像強化法通常只顧及上述的視覺品質而無法確保還原的準確度。從誤差的角度而言，去霧影像常常在顏色上過度飽和；與此同時，受到霧氣影響而變得朦朧的區域卻無法完全清晰化。然而，傳統的影像強化法常常放大上述的誤差，造成相應的還原準確度下降。因此，本研究重新分析去霧演算法，展示去霧演算法的細微調整如何影響其結果的亮度與對比；並藉此建立符合本研究目標的演算法。我們的去霧演算法以對比與亮度為主要導向，因此更易於校調去霧影像的品質；而影像強化法能在不損及還原準確度的前提下提高影像的視覺品質。除此之外，本研究中所提出的去霧演算法，其時間複雜度屬於 O(nlog⁡(n))；而影像強化法的複雜度也僅僅是 O(n)，並且可以搭配任何去霧演算法使用。

In this dissertation, a combination of algorithms that are capable to keep the restoration accuracy while enhancing the luminance, contrast, and detail of dehazed images are proposed. The combination includes a new haze-removal algorithm and a new enhancement method. Haze-removal algorithms are one kind of image-restoration algorithms; the restoration accuracy is one of the most important subjects to evaluate the algorithms. Conventional enhancement methods usually focus on the quality associated with the human visual perception rather than the restoration accuracy. From the perspective of errors, dehazed images usually suffer from the over-saturated and hazy regions at the same time; however, the conventional methods usually amplify at least one of the corresponding errors, harming corresponding restoration accuracy. Therefore, haze-removal algorithms are analyzed in this study to demonstrate how adjustments of the algorithms affect the luminance and contrast of corresponding dehazed images; in this way, we design algorithms that meet the above-mentioned demands. The proposed haze-removal algorithm is luminance and contrast-oriented and is more intuitive to be adjusted; besides, our enhancement method is eligible to keep the restoration accuracy while boosting the quality of dehazed images. Moreover, the time complexity of the proposed haze-removal algorithms and enhancement method is O(nlog⁡(n)) and O(n), respectively, and the enhancement method can collaborate with any haze-removal algorithm.

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