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    政大機構典藏 > 資訊學院 > 資訊科學系 > 學位論文 >  Item 140.119/155970
    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/155970


    Title: 應用於白平衡校正的輔助雙模態跨域變換器
    ABCDFormer: Auxiliary Bimodal Cross-Domain Transformer for White Balance Correction
    Authors: 邱鈺臻
    Chiu, Yu-Cheng
    Contributors: 彭彥璁
    Peng, Yan-Tsung
    邱鈺臻
    Chiu, Yu-Cheng
    Keywords: 白平衡
    多模態
    變換器
    White balance
    Multiple modalities
    Transformer
    Date: 2025
    Issue Date: 2025-03-03 14:03:26 (UTC+8)
    Abstract: 對於sRGB影像實現準確的白平衡(WB)是一項具有挑戰性的任務,需要修正來自不同光源的色溫變化並消除色偏,以生成自然且中性的顏色。現有的白平衡方法通常因後期處理中的全局色彩調整以及當前數據集中有限的色彩多樣性而受限,導致在色偏明顯的影像中表現不佳。為了解決這些限制,我們提出了一種輔助雙模態跨域變換器(Auxiliary Bimodal Cross-Domain Transformer, ABCDFormer),通過整合來自多模態與多領域的互補知識來提升白平衡校正效果。ABCDFormer結合了兩個輔助模型,用於提取全域顏色和色度直方圖訊息,進一步豐富目標模型對sRGB輸入的處理。此外,我們引入了一個互動通道注意力模組(Interactive Channel Attention, ICA),以促進跨模態知識轉移,將精煉的顏色特徵嵌入到影像表示中,實現更精確的校正。在公共白平衡基準數據集上的大量實驗表明,ABCDFormer優於現有的最先進方法。
    Achieving accurate white balance (WB) for sRGB images is a challenging task, requiring the correction of color temperature variations from diverse light sources and the elimination of color casts to produce natural, neutral colors. Existing WB methods often struggle due to the limitations of global color adjustments applied during post-sRGB processing and the restricted color diversity in current datasets, resulting in suboptimal performance, especially for images with significant color shifts. To address these limitations, we propose an Auxiliary Bimodal Cross-Domain Transformer (ABCDFormer), which enhances WB correction by leveraging complementary knowledge from multiple modalities and domains. ABCDFormer integrates two auxiliary models to extract global color and chromaticity histograms, enriching the target model’s sRGB input processing. Additionally, an Interactive Channel Attention (ICA) module is introduced to facilitate cross-modality knowledge transfer, embedding refined color features into image representations for more precise corrections. Extensive experiments on benchmark WB datasets demonstrate that ABCDFormer outperforms state-of-the-art methods.
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    Description: 碩士
    國立政治大學
    資訊科學系
    111753202
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0111753202
    Data Type: thesis
    Appears in Collections:[資訊科學系] 學位論文

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