近年來影像強化演算法已經被廣泛的應用在各影像處理實務中，而在各種強化方式中，Retinex被認為是最能有效保持圖片細節的演算法。雖然有此優勢，但為了維持影像細節和環境光的平衡，傳統Retinex演算法最為人詬病的地方即是運算耗時的問題。此外，和大部分影像強化演算法一樣，傳統的Retinex演算法無法適應不均勻環境光的場景，在分析與計算的過程中容易發生過度強化等問題。為了改善以上問題，本論文提出以下幾點貢獻，首先，針對演算法最耗時的光場估計部分提出一優化演算法，可大幅的減少時間消耗；此外，我們還提出具可適性的影像恢復處理方式，此方式可以自動偵測圖片最亮和最暗的部分，根據偵測結果調整核心演算法以避免過度強化。最後，為了使本論文提出之演算法能有更快的處理速度，我們在最後一個章節提出硬體加速考量，希望藉由軟硬體協同設計來使演算法效能有最佳優化結果。實驗結果顯示，我們的演算法不僅可以增強影像中的細節、維持影像光源的自然性，並且可以適應各式光源場景，不會有過度強化的問題出現；而運算時間也大大降低。 Image enhancement plays an important role in digital image processing and has been applied to fields of science and engineering. Among all of the image enhancement algorithms, Retinex-based algorithms are considered most effective in maintaining details of images. Despite the advantages, Retinex-based algorithms are generally more complex and consume considerably more time to maintain the balance between image detail and ambient illumination. And like most image enhancement algorithms, conventional Retinex-based algorithms are prone to over-enhancement when processing images with non-uniform illumination. To deal with aforementioned issues, various approaches are proposed and explored in the thesis. First, an optimized illumination estimation, which is the most time-consuming part of conventional Retinex-based algorithms, is proposed to reduce the time consumption. Furthermore, we propose an adaptive restoration process, which autonomously detects the brightest and darkest parts of the image and make modifications to the core algorithm accordingly to avoid over-enhancement. We also explored the possibility of hardware acceleration in the final chapter. It is hoped that the optimal performance can be achieved by the hardware and software co-design. Experimental results show that proposed Retinex-based algorithm can enhance details of images while preserving perceived naturalness without over-enhancement. And the execution time is greatly reduced.