主動矩陣式有機發光二極體(Active-Matrix Organic Light Emitting Diodes)顯示技術在近年來已成為智慧型裝置的主流顯示器，然而主動矩陣式有機發光二極體顯示器是智慧型裝置上主要耗電的元件之一，特別是在顯示高亮度的影像內容時，將使主動矩陣式有機發光二極體顯示器產生大量功率消耗。為了校正具有高環境光源的高曝光影像，在此研究中，我們提出一個基於深度學習技術的限制功率曝光校正神經網路，此神經網路採用一個亮度引導機制且基於U-Net架構的生成網路，透過全域與局部辨別網路進行對抗式學習，為了將高曝光區域分布轉換為正常曝光區域分布，同時確保生成的影像在主動矩陣式有機發光二極體顯示器上不產生額外的功率消耗，我們進一步在生成網路中加入了功率限制，目的是約束生成網路產生比輸入影像更高的亮度。實驗結果顯示，本研究能有效轉換高曝光區域分布至正常曝光區域分布並限制其功率，更詳細的說，此方法在常見的資料集驗證中，在相近的功率節省率下，提出的方法與現有的過度曝光校正省電技術相比，能增強影像的飽和度與對比度，提供更佳的影像品質。

The Active-Matrix Organic Light Emitting Diodes (AMOLED) technology has become the mainstream of displays in recent years. However, it will generate a lot of power consumption on AMOLED displays when displaying high-brightness content. To address this problem, an exposure correction mechanism is needed to remove high-brightness ambient light in the image. In this thesis, we propose a Power-Constrained Exposure Correction (PCEC) network based on a Generative Adversarial Network (GAN) architecture, the PCEC network utilizes a U-Net-based generator with brightness-guided, and adopts the global-local discriminator architecture for adversarial learning. To transform the distribution of high-exposed regions into the distribution of normal-exposed regions while avoid generating additional power consumption on AMOLED displays, we add a power-constraint to the generator to restrict the increasing brightness. The experimental results show that the proposed method can effectively correct high-exposed regions as well as reducing power. At a similar power saving rate, the proposed method can enhance the saturation and contrast of the image and provide better visual quality compared with the existing over-exposure correction power saving technologies.

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