【摘要】：霧霾常會影響獲取圖像的質(zhì)量,單幅圖像去霧是一個具有挑戰(zhàn)性的不適定問題.針對傳統(tǒng)的去霧方法存在去霧結(jié)果顏色失真、適用范圍局限等問題,提出一種基于深度網(wǎng)絡的去霧算法——生成對抗映射網(wǎng)絡的多層感知去霧算法.在訓練階段中,利用生成對抗映射網(wǎng)絡里判別網(wǎng)絡與生成網(wǎng)絡間對抗式訓練機制,保證生成網(wǎng)絡中參數(shù)的最優(yōu)解;在測試還原過程中,先提取有霧圖像中霧氣相關特征,并利用訓練得到的生成網(wǎng)絡對提取特征進行多層感知映射,進而得到反映霧氣深度信息的透視率,最終運用得到的透視率實現(xiàn)了圖像去霧.實驗結(jié)果表明,與同類算法相比,該算法能較好地還原出場景中目標的真實色彩,并抑制部分噪聲,去霧效果明顯.
[Abstract]:Haze often affects the quality of image acquisition, and the single image de-fogging is a challenging ill-posed problem. In order to solve the problems of color distortion of de-fogging results and limitation of application range in traditional de-fogging methods, an algorithm based on depth network for de-fogging is proposed, which is called multi-layer perceptual de-fogging algorithm based on anti-mapping network. In the training stage, the antagonistic training mechanism between the discriminant network and the generated network is used to ensure the optimal solution of the parameters in the generated network. In the process of test and restore, firstly, the fog-related features in fog images are extracted, and the multi-layer perceptual mapping of the extracted features is carried out by using the trained generation network, and then the perspective of fog depth information is obtained. Finally, using the perspective of the image to achieve de-fogging. The experimental results show that compared with the same algorithm, the proposed algorithm can restore the real color of the target in the scene, and suppress some noise, and the fog removal effect is obvious.
【作者單位】： 蘭州理工大學電氣工程與信息工程學院;西安交通大學電子與信息工程學院;
【基金】：國家自然科學基金(61365003) 甘肅省基礎研究創(chuàng)新群體項目(1506RJIA031) 中國博士后科學基金(2014M550494)
【分類號】：TP391.41

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