水下影像增強和還原給圖像處理領域帶來了巨大的挑戰。當光線穿透水時，由於水深和固體顆粒的影響，而產生散射和吸收效應，導致影像可能產生模糊、霧霾和顏色失真，尤其是藍色和綠色色調。這些要素將顯著的左右人類的視覺和水下機器人的影像品質。雖然目前已經許多用於水下圖像改進和恢復的算法被提出，但許多算法依賴於先驗知識或針對性的圖像還原方法。儘管這些方法提供了一定的改進，但它們通常僅限於特定場景，並且由於參數不易取得而難以優化，從而阻礙了實際可用性。此外，現有的水下圖像處理深度學習模型往往相當複雜且計算量大。在本論文中，我們提出了一種利用生成對抗訓練和實際水下影像數據訓練的方法。我們提出的方法採用了受 DCE-Net 啟發的生成器網絡架構，並通過compensation block進行了增強以改善輸出圖像。我們將論文的結果進行與其他先進方法的比較與分析，在影像品質的表現，相較於其他的處理方法我們的成果能夠達到更舒適自然的色調和更佳的視覺效果，而在量化分數的評比上，在峰值信噪比(PSNR)和執行時間都能夠取得較優的分數。

Underwater image enhancement and restoration present considerable challenges in the field of image processing. When light penetrates water, it undergoes scattering and absorption effects due to water depth and solid particles, leading to varying degrees of blurring, haze, and color distortion, especially in blue and green hues. The visual quality of underwater robotics and human perception can be significantly impacted by these factors. While numerous algorithms have been developed for underwater image improvement and restoration, many rely on prior knowledge or direct image restoration methods. Although such approaches offer certain improvements, they are often limited to specific scenes and challenging to optimize due to parameter selection, hindering practical usability. Furthermore, existing deep learning models for underwater image processing tend to be complex and computationally intensive. This study introduces a novel approach that utilizes generative adversarial training and real underwater image data for the training process. Our proposed method incorporates a generator network architecture inspired by DCE-Net, enhanced with a compensation block to improve the output images. We compare our results with other advanced methods in terms of image quality, showcasing superior visual effects and more natural colors. Our method demonstrates superior performance in quantitative evaluations, with notably higher scores in peak signal-to-noise ratio (PSNR) and execution time compared to others.

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