發(fā)布時(shí)間：2018-08-19 20:43

【摘要】：針對(duì)單幅圖像下,基于稀疏表示的去雨算法存在殘差較大而導(dǎo)致圖像恢復(fù)效果不理想的問(wèn)題,提出了一種優(yōu)化圖像高頻部分幾何分量的去雨方法.首先采用平滑濾波做圖像分解,得到雨圖像的高頻部分;然后結(jié)合稀疏表示與近鄰傳播算法分離出圖像高頻部分的雨分量,用圖像的高頻部分減去雨分量并做平滑處理,以此作為幾何分量;此外,對(duì)稀疏表示過(guò)程得到的字典進(jìn)行再分類,完善雨分量與非雨分量的區(qū)分,最后完成圖像恢復(fù).實(shí)驗(yàn)結(jié)果表明,該方法能有效利用圖像的幾何信息來(lái)解決紋理恢復(fù)誤差較大的問(wèn)題,實(shí)現(xiàn)更精確的紋理恢復(fù)和雨分量去除.
[Abstract]:Aiming at the problem that the rain-removing algorithm based on sparse representation has large residuals which lead to unsatisfactory image restoration effect, a rain-removing method is proposed to optimize the geometric components of the high-frequency part of the image. Firstly, the high frequency part of rain image is obtained by using smoothing filter, then the rain component of high frequency part of image is separated by sparse representation and nearest neighbor propagation algorithm, and the rain component is subtracted from the high frequency part of image and smoothed. In addition, the dictionary obtained from the sparse representation process is reclassified to perfect the distinction between rain component and non-rain component, and finally the image restoration is completed. The experimental results show that the method can effectively use the geometric information of the image to solve the problem of large texture recovery error and achieve more accurate texture recovery and rain component removal.
【作者單位】： 天津大學(xué)電氣自動(dòng)化與信息工程學(xué)院;
【基金】：國(guó)家自然科學(xué)基金資助項(xiàng)目(61472274);國(guó)家自然科學(xué)基金重點(diǎn)項(xiàng)目(61632081)~~
【分類號(hào)】：TP391.41

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