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| 题名: | 使用多曝光值輸入的影像去反射
Image Reflection Removal using Multiple Exposure Inputs |
| 作者: | 蕭竑軒
Hsiao, Hung-Hsuan |
| 贡献者: | 彭彥璁
Peng, Yan-Tsung
蕭竑軒
Hsiao, Hung-Hsuan |
| 关键词: | 影像去反射
曝光值
影像還原
Image reflection removal
Exposure values
Image restoration |
| 日期: | 2025 |
| 上传时间: | 2025-03-03 14:03:03 (UTC+8) |
| 摘要: | 本論文旨在解決於單一影像去反射 (SIRR) 的問題。在日常生活中，當我們隔著具有反射和透明度屬性的介質如窗戶、玻璃等物品拍照 時，所拍攝到的影像通常具有多餘的反射現象，這些反射的區域會遮 擋到或模糊了我們實際想要拍攝的背景，除了會影響視覺品質外，還可能降低下游電腦視覺任務的性能。單一影像去反射的目的為移除不 想要的反射部分，並還原至乾淨的背景。現今已有基於深度學習為的 方法透過在模型中加入VGG 特徵、邊緣先驗和語言資訊的方式順利 得將反射去除。然而，這些方法卻有所限制，因為它們直接使用繁複 的模型將反射影像輸入映射回乾淨的背景，而沒有正視根本原因。為了解決這個問題，我們利用從輸入產生的多曝光值 (EVs) 影像中萃取 的多尺度特徵，進而提出一個多尺度、多曝光的去反射網路。由於降低曝光值後影像含有較弱的反射，有利於反射與背景層的解耦，因此 多曝光值、多尺度的設計可利用這些特徵來解決問題，經實驗表明， 本論文提出的模型即使在反射型態千變萬化的真實世界場景中也能將之去除，其表現也優於最先進的去反射方法。
This thesis focuses on the problem of Single image reflection removal (SIRR). When taking photographs through reflective materials, there is often an unwanted reflection area in the image. The purpose of SIRR is to remove the undesired reflection part and restore the original scene. Existing deep learning-based methods have achieved success by incorporating VGG features, edge priors and linguistic information into the model. However, these methods often have limitations, as they directly map inputs to clean background using sophisticated models without confronting the root cause. To address the ill-posedness of this problem, we propose a multi-scale, multi-exposure reflection removal network to leverages hierarchical multi-scale features extracted from images at multiple exposure values (EVs) generated from the input. As low EV images contain weaker reflections, the multi-EVs, multi-scale design can leverage these features to simplify the ill-posed problem and remove reflection even in real-world scenarios where reflection patterns random and complex. |
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| 描述: | 碩士
國立政治大學
資訊科學系
111753132 |
| 資料來源: | http://thesis.lib.nccu.edu.tw/record/#G0111753132 |
| 数据类型: | thesis |
| 显示于类别: | [資訊科學系] 學位論文
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