在以電腦視覺為基礎的戶外監視系統即時應用中，很容易受到霧氣等不佳氣候條件影響，使影像畫面能見度降低，讓畫面內的物體模糊不清，無法得到完整且清晰的影像資訊，造成後續影像分析、應用上的困難。因此能夠將有霧影像恢復為清晰影像畫面的影像除霧方法一直是一項重要影像前處理技術。本文提出了一種複雜度低且適合以VLSI硬體電路實現的除霧演算法，可以獲取畫值較佳的無霧靜態影像以及無霧視訊。
本論文所提出的演算法是基於大氣散射模型以及暗通道先驗方法，可以提取畫面整體的大氣光值和透射圖。為避免使用單個全域大氣光值進行除霧時會導致區塊畫面出現過亮或過暗的問題，在演算法中採用了區域大氣光值估計的方法來獲得同樣性質的區塊內最佳的區域大氣光值。為了確保還原後整體影像畫面亮度一致，避免產生區塊效應，將以全域大氣光值做為基礎，動態調整區域大氣光值。此外，為防止除霧後的視訊影像會在播放時產生閃爍現象—即兩個相鄰影格的畫面亮度發生了劇烈變化，將會在進行全域大氣光值估計的部分設立畫面亮度調節器，減輕相鄰影格間劇烈的亮度變化，同時不干擾原本影片內容的亮暗趨勢，因此適用於此前尚未研究過的視訊除霧應用。
為同時滿足靜態和視訊影像的即時應用需求，本論文針對該演算法設計相對應的超大型積體電路架構。通過使用TSMC 0.13um製程，本論文提出的除霧電路可以有大約200 Mpixels / s的處理速度，足以即時處理30 fps的Full HD影片。

In the real-time outdoor applications of computer vision-based surveillance systems, haze removal that can recover clear images from foggy ones is an essential preprocessing technique for object detection. In this dissertation, an efficient haze removal method suitable for hardware design is proposed to obtain high quality fog-free static images and videos. Based on the atmosphere scattering model and the dark channel prior method, the atmospheric light of the whole image and the transmission map could be extracted.
Instead of using a single global atmospheric light to restore foggy image, a local atmospheric light estimation method is applied in the proposed design to achieve optimal results. To ensure that the overall image is consistent without block artifacts, dynamic adjustment of local atmospheric light is calculated based on global atmospheric light. Furthermore, an adjuster is applied to the video for preventing “flicker” which means that the brightness changes dramatically between two neighbor frames in the video. Hence, the proposed algorithm is suitable for video defogging applications, which have not been dealt with before.
To achieve the requirement of real-time applications for both static and dynamic images, an implementation of very-large-scale integration (VLSI) architecture for the proposed algorithm is presented. By using TSMC 0.13-um technology, the design yielded a processing rate of approximately 200 Mpixels/s, which is rapid enough to facilitate Full HD resolution at 30 fps in real-time.

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