TWI512681B - Device and method for rain removal - Google Patents
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Description
本發明係有關於一種影像內雨紋去除裝置以及方法,特別係有關於一種影像分解為高頻影像以及低頻影像,並分別針對高頻影像以及低頻影像進行強化,最後利用高頻加強影像以及低頻加強影像取得輸出影像之影像內雨紋去除裝置以及方法。The invention relates to an image in-vivo rain pattern removing device and a method, in particular to decomposing an image into a high frequency image and a low frequency image, and respectively strengthening the high frequency image and the low frequency image, and finally using the high frequency to enhance the image and the low frequency. Enhance the image to obtain the image of the rain pattern removal device and method.
由於科技之進步,機動車輛之使用已日益普及,而行車過程之安全性亦越來越重要。於許多影像式安全警示系統中,係藉由判斷影像中之障礙物以提醒使用者與其保持安全距離。然而,在天氣惡劣之條件下,例如下大雨時,將可能造成影像之誤判,進而導致行車過程之危險性提高。因此,如何有效地將雨紋自影像中移除以保持影像之清晰度為目前所需解決之問題。Due to advances in technology, the use of motor vehicles has become increasingly popular, and the safety of the driving process is becoming more and more important. In many image security warning systems, the obstacles in the image are judged to alert the user to a safe distance. However, in bad weather conditions, such as heavy rain, it may cause misjudgment of images, which may lead to increased risk of driving. Therefore, how to effectively remove the rain pattern from the image to maintain the sharpness of the image is currently a problem to be solved.
為解決上述問題,本發明一實施例提供一種影像內雨紋去除方法,步驟包括:將原始影像分解為高頻影像以及低頻影像;根據既定景深範圍濾除原始影像對應於既定景深範圍之資訊以取得處理影像;根據原始影像、低頻影像以及處理影像取得 低頻加強影像;根據字典學習演算法取得對應於高頻影像之高頻加強影像;根據高頻加強影像以及低頻加強影像取得輸出影像。In order to solve the above problem, an embodiment of the present invention provides a method for removing rain marks in an image, the method comprising: decomposing the original image into a high frequency image and a low frequency image; and filtering out the information corresponding to the predetermined depth of field according to the predetermined depth of field range. Acquire processed images; obtain from original images, low-frequency images, and processed images The low frequency enhances the image; the high frequency enhanced image corresponding to the high frequency image is obtained according to the dictionary learning algorithm; and the output image is obtained according to the high frequency enhanced image and the low frequency enhanced image.
本發明另一實施例提供一種影像內雨紋去除裝置, 包括一影像擷取單元以及一處理單元。影像擷取單元擷取一原始影像。處理單元接收原始影像,並將原始影像分解為一高頻影像以及一低頻影像,其中根據一既定景深範圍濾除上述原始影像對應於上述既定景深範圍之資訊以取得一處理影像,根據上述原始影像、上述低頻影像以及上述處理影像取得一低頻加強影像,根據一字典學習演算法取得對應於上述高頻影像之一高頻加強影像,以及根據上述高頻加強影像以及上述低頻加強影像取得一輸出影像。Another embodiment of the present invention provides an image internal rain pattern removing device. The invention comprises an image capturing unit and a processing unit. The image capturing unit captures an original image. The processing unit receives the original image, and decomposes the original image into a high frequency image and a low frequency image, wherein the original image corresponding to the predetermined depth of field range is filtered according to a predetermined depth of field range to obtain a processed image according to the original image. And obtaining, by the low frequency image and the processed image, a low frequency enhanced image, obtaining a high frequency enhanced image corresponding to the high frequency image according to a dictionary learning algorithm, and obtaining an output image according to the high frequency enhanced image and the low frequency enhanced image .
100‧‧‧影像內雨紋去除裝置100‧‧‧Image internal rain pattern removal device
110‧‧‧影像擷取單元110‧‧‧Image capture unit
120‧‧‧雨滴偵測單元120‧‧‧ raindrop detection unit
130‧‧‧處理單元130‧‧‧Processing unit
S201-S212‧‧‧步驟流程S201-S212‧‧‧Step procedure
第1圖係顯示根據本發明一實施例所述之影像內雨紋去除裝置之示意圖。FIG. 1 is a schematic view showing an image internal rain pattern removing device according to an embodiment of the invention.
第2圖係顯示根據本發明一實施例所述之影像內雨紋去除方法之流程圖。FIG. 2 is a flow chart showing a method for removing rain marks in an image according to an embodiment of the invention.
有關本發明之系統以及方法適用之其他範圍將於接 下來所提供之詳述中清楚易見。必須了解的是下列之詳述以及具體之實施例,當提出有關影像內雨紋去除裝置以及方法之示範實施例時,僅作為描述之目的以及並非用以限制本發明之範圍。Other ranges applicable to the system and method of the present invention will be The details provided below are clearly visible. It is to be understood that the following detailed description, as well as specific embodiments, are intended to be illustrative of the embodiments of the present invention, and are not intended to limit the scope of the invention.
請參閱第1圖。第1圖係顯示根據本發明一實施例所 述之影像內雨紋去除裝置之示意圖。如第1圖所示,影像內雨紋去除裝置100包括影像擷取單元110、雨滴偵測單元120以及處理單元130。影像擷取單元110用以擷取一原始影像。雨滴偵測單元120用以感測目前是否在下雨,並輸出一感測結果。處理單元130用以接收原始影像,並根據感測結果判斷是否下雨。若處理單元130判斷目前正在下雨,則執行影像內雨紋去除方法,以取得較清晰之原始影像。Please refer to Figure 1. Figure 1 shows an embodiment in accordance with the present invention. A schematic diagram of the rain pattern removal device in the image. As shown in FIG. 1 , the image internal rain pattern removing apparatus 100 includes an image capturing unit 110 , a raindrop detecting unit 120 , and a processing unit 130 . The image capturing unit 110 is configured to capture an original image. The raindrop detecting unit 120 is configured to sense whether it is currently raining and output a sensing result. The processing unit 130 is configured to receive the original image and determine whether it is raining according to the sensing result. If the processing unit 130 determines that it is currently raining, the method of removing the rain pattern in the image is performed to obtain a clear original image.
根據本發明一實施例,若目前正在下雨,則處理單 元130使用引導濾波器(guided filter)將原始影像I分解為高頻影像IHF 以及低頻影像ILF ,並利用基於攝影學之景深資訊(Depth of Field,DoF)之顯著區域偵測技術取得對應於原始影像I之處理影像IDoF 。值得注意的是,於其它實施例中亦可使用能將影像分解為高頻影像以及低頻影像之其他類型濾波器,例如左右對稱濾波器等。接著,處理單元130以基於攝影學之景深資訊之顯著區域偵測技術取得對應於原始影像I之處理影像IDoF ,並分別針對高頻影像IHF 以及低頻影像ILF 進行影像強化。According to an embodiment of the present invention, if it is currently raining, the processing unit 130 decomposes the original image I into a high frequency image I HF and a low frequency image I LF using a guided filter, and uses the depth of field information based on photography. The significant area detection technique of (Depth of Field, DoF) obtains the processed image I DoF corresponding to the original image I. It should be noted that other types of filters capable of decomposing images into high frequency images and low frequency images, such as left and right symmetric filters, may also be used in other embodiments. Then, the processing unit 130 obtains the processed image I DoF corresponding to the original image I by using the salient region detection technology based on the depth information of the photography, and performs image enhancement on the high frequency image I HF and the low frequency image I LF , respectively.
關於低頻影像ILF 之部分,由於透過引導濾波器所取 得之低頻影像可能會過濾掉原始影像過多之非雨紋資訊,因此處理單元130首先藉由設定一閥值取得處理過後之處理影像I’DoF ,再根據處理過後之處理影像I’DoF 取得一矩陣α ,以加強原始影像之低頻部分。接著,處理單元130更根據原始影像I、低頻影像ILF 以及矩陣α 取得對應於低頻影像ILF 之低頻強化影像I’LF 。Regarding the part of the low frequency image I LF , since the low frequency image obtained by the guiding filter may filter out the excessive non-rainprint information of the original image, the processing unit 130 first obtains the processed image I' after processing by setting a threshold value. DoF , and then obtain a matrix α according to the processed image I'DoF after processing to enhance the low frequency part of the original image. Next, the processing unit 130 further obtains the low-frequency enhanced image I′ LF corresponding to the low-frequency image I LF according to the original image I, the low-frequency image I LF , and the matrix α .
關於高頻影像IHF 之部分,處理單元130根據字典學習 (Dictionary Learning)演算法取得對應於高頻影像IHF 之字典DHF 。由於對應於高頻影像IHF 之字典DHF 可能仍包含部分雨紋,且雨紋之部分較接近灰階值,故處理單元130根據基於顏色特徵之特徵擷取(Eigen Color feature extraction)演算法將上述雨紋濾除以取得對應於字典DHF 之第一非雨紋區域高頻影像IHF_NR1 。Regarding the portion of the high frequency image I HF , the processing unit 130 obtains the dictionary D HF corresponding to the high frequency image I HF according to a dictionary learning algorithm. Since the dictionary D HF corresponding to the high frequency image I HF may still contain part of the rain pattern, and the part of the rain pattern is closer to the gray scale value, the processing unit 130 performs the Eigen Color feature extraction algorithm based on the color feature. The rain pattern is filtered to obtain a first non- rainprint area high frequency image I HF — NR1 corresponding to the dictionary D HF .
此外,處理單元130更根據字典DHF 之梯度直方圖(Histogram of oriented gradient,HoG)取得對應於字典DHF 之含雨紋字典DR 以及不含雨紋字典DNR 。其中,利用梯度直方圖之相關演算法係為習知技術,故在此不加以詳述以精簡說明。接著,根據基於景深資訊之顯著區域偵測技術重建不含雨紋字典DNR 以取得對應於不含雨紋字典DNR 之第二非雨紋區域高頻影像IHF_NR2 。關於含雨紋字典DR 之部分,由於可能仍包含部分原始影像I之細節,故處理單元130係透過字典學習演算法將含雨紋字典DR 重建為含雨紋高頻影像IHF_R 。接著,處理單元130更根據基於景深資訊之顯著區域偵測技術取得對應於含雨紋高頻影像IHF_R 之含雨紋高頻景深影像DoFHF_R ,並將含雨紋高頻景深影像DoFHF_R 以及處理影像IDoF 相乘以留下包含誤判之原始影像I之細節之修正影像DoF’HF_R 。於取得修正影像DoF’HF_R 後,處理單元130將修正影像DoF’HF_R 與含雨紋高頻影像IHF_R 相乘以取得雨紋區域高頻影像IHF_R 。Further, the processing unit 130 further dictionary D HF according to the gradient histogram (Histogram of oriented gradient, HoG) to obtain the corresponding dictionary D HF rain pattern dictionary containing free D R and rain pattern dictionary D NR. Among them, the correlation algorithm using the gradient histogram is a conventional technique, so it will not be described in detail here to simplify the description. Then, the rain-free dictionary D NR is reconstructed according to the significant region detection technology based on the depth information to obtain the second non- rainprint region high frequency image I HF — NR2 corresponding to the rainless dictionary D NR . Rain on the part of the pattern dictionary containing D R, since the details may still contain the original image I, so that the processing unit 130 are reconstructed into an image containing a high frequency through the rain groove I HF_R dictionary learning algorithms rain pattern dictionary containing D R. Then, the processing unit 130 obtains the rain- fed high-frequency depth image DoF HF_R corresponding to the rain-like high-frequency image I HF_R according to the significant region detection technology based on the depth information, and the rain- fed high-frequency depth image DoF HF_R and The processed image I DoF is multiplied to leave a corrected image DoF' HF_R containing the details of the false positive original image I. After obtaining the corrected image DoF' HF_R , the processing unit 130 multiplies the corrected image DoF' HF_R by the rain-like high-frequency image I HF_R to obtain the rain-grain region high-frequency image I HF_R .
最後,處理單元130根據第一非雨紋區域高頻影像IHF_NR1 、第二非雨紋區域高頻影像IHF_NR2 以及雨紋區域高頻影像IHF_R 取得高頻加強影像I’HF 。Finally, the processing unit 130 according to the first high-frequency region of the non-image pattern rain I HF_NR1, the second non-rain-out area and a high-frequency image I HF_NR2 rain-out area to obtain a high-frequency image I HF_R high frequency emphasis image I 'HF.
當處理單元130完成所有高頻影像IHF 以及低頻影像ILF 之影像強化過程後,將高頻加強影像I’HF 以及低頻強化影像I’LF 相加以取得最後之輸出影像IRN 。After the processing unit 130 completes the image enhancement process of all the high frequency image I HF and the low frequency image I LF , the high frequency enhanced image I′ HF and the low frequency enhanced image I′ LF are added to obtain the final output image I RN .
接著請參閱第2圖。第2圖係顯示根據本發明一實施
例所述之影像內雨紋去除方法之流程圖。於此實施例中,本發明所述之影像內雨紋去除方法係以單張影像為單位進行分析以及處理。首先,於步驟S201,使用引導濾波器將原始影像I分解為高頻影像IHF
以及低頻影像ILF
。值得注意的是,於其它實施例中亦可使用能將影像分解為高頻影像以及低頻影像之其他類型濾波器,例如左右對稱濾波器(bilateral filter)等。於步驟S202,利用下列之函數以基於攝影學之景深資訊之顯著區域偵測技術取得對應於原始影像I之處理影像IDoF
:
由於透過引導濾波器所取得之低頻影像可能會過濾掉原始影像過多之非雨紋資訊,因此於步驟S203,根據下列之函數利用原始影像I、低頻影像ILF 以及矩陣α 取得對應於低頻影像ILF 之低頻強化影像I’LF :I' LF =αI+(1-α)ILF 其中,α(i,j )[0,1],係根據處理過後之處理影像I’DoF (藉由設定一閥值,將處理影像IDoF 中大於或等於該閥值之值保留,而小於該閥值之值設為0,舉例來說,將閥值設定為0.2,處理影像IDoF 中大 於或等於0.2之值保留,而小於0.2之值設為0)推導取得,目的係用以加強原始影像之低頻部分。接著,進入步驟S204,根據字典學習演算法取得對應於高頻影像IHF 之字典DHF 。進入步驟S205,根據基於顏色特徵之特徵擷取演算法取得對應於字典DHF 之第一非雨紋區域高頻影像IHF_NR1 。由於雨紋資訊之色彩資訊較接近於灰階值,故可利用上述之特性重新過濾遺漏之雨紋資訊。Since the low frequency image obtained by the guiding filter may filter out the non-rainprint information of the original image, in step S203, the original image I, the low frequency image I LF and the matrix α are used to obtain the corresponding low frequency image I according to the following function. LF of the low frequency image strengthening I 'LF: I' LF = αI + (1-α) I LF where, α (i, j) [0,1], according to the processed image I' DoF after processing (by setting a threshold, the value of the processing image I DoF is greater than or equal to the threshold value, and the value less than the threshold is set to 0 For example, the threshold is set to 0.2, the value of greater than or equal to 0.2 in the processed image I DoF is retained, and the value less than 0.2 is set to 0). The purpose is to derive the low frequency portion of the original image. Next, proceeding to step S204, a dictionary D HF corresponding to the high frequency image I HF is obtained based on the dictionary learning algorithm. Proceeding to step S205, the first non- rainprint region high frequency image I HF_NR1 corresponding to the dictionary D HF is obtained according to the feature extraction algorithm based on the color feature. Since the color information of the rainweed information is closer to the grayscale value, the above-mentioned characteristics can be used to re-filter the missing rainweave information.
於步驟S206,根據字典DHF 之梯度直方圖(Histogram of oriented gradient,HoG)取得對應於字典DHF 之含雨紋字典DR 以及不含雨紋字典DNR 。其中,利用梯度直方圖之相關演算法係為習知技術,故在此不加以詳述以精簡說明。於步驟S207,根據基於景深資訊之顯著區域偵測技術重建不含雨紋字典DNR 以取得對應於不含雨紋字典DNR 之第二非雨紋區域高頻影像IHF_NR2 。In step S206, the dictionary D HF according to the gradient histogram (Histogram of oriented gradient, HoG) to obtain the corresponding dictionary D HF rain pattern dictionary containing free D R and rain pattern dictionary D NR. Among them, the correlation algorithm using the gradient histogram is a conventional technique, so it will not be described in detail here to simplify the description. In step S207, the rain-free dictionary D NR is reconstructed according to the significant area detection technology based on the depth information to obtain the second non- rainprint area high frequency image I HF — NR2 corresponding to the rain-free dictionary D NR .
接著,進入步驟S208,透過字典學習演算法將含雨 紋字典DR 重建為含雨紋高頻影像IHF_R 。。於步驟S209,根據基於景深資訊之顯著區域偵測技術取得對應於含雨紋高頻影像IHF_R 之含雨紋高頻景深影像DoFHF_R 。於步驟S210,根據含雨紋高頻影像IHF_R 、含雨紋高頻景深影像DoFHF_R 以及處理影像IDoF 取得雨紋區域高頻影像IHF_R 。接著,進入步驟S211,根據第一非雨紋區域高頻影像IHF_NR1 、第二非雨紋區域高頻影像IHF_NR2 以及雨紋區域高頻影像IHF_R 取得高頻加強影像I’HF 。最後,於步驟S212,根據高頻加強影像I’HF 以及低頻強化影像I’LF 取得最後之輸出影像IRN 。Next, proceeding to step S208, the rain- stained dictionary D R is reconstructed into a rain- stained high-frequency image I HF_R by a dictionary learning algorithm. . In step S209, the rain- fed high-frequency depth image DoF HF_R corresponding to the rain-like high-frequency image I HF_R is obtained according to the salient depth region detection technology based on the depth of field information. In step S210, according to the image containing the high frequency pattern I HF_R rain, rain grain containing the high-frequency and depth of the image-processed image DoF HF_R I DoF high frequency region to obtain the image pattern rain I HF_R. Next, proceeding to step S211, the high-frequency enhanced image I' HF is obtained from the first non- rainprint area high frequency image I HF_NR1 , the second non-rain area high frequency image I HF_NR2, and the rain pattern high frequency image I HF_R . Finally, in step S212, the final output image I RN is obtained from the high frequency enhanced image I' HF and the low frequency enhanced image I' LF .
綜上所述,根據本發明一實施例所提出之影像內雨 紋去除方法以及裝置,藉由將影像分為高頻影像以及低頻影像,並分別針對高頻影像以及低頻影像之部分進行影像之強化以取得 遺漏之細節部分,最後將強化後之高頻影像以及低頻影像進行合成以取得去除雨紋後之影像,如此除了保留原本高頻影像之影像細節外,更可自低頻影像救回誤判之部分,藉以得到更清晰之影像。In summary, the image internal rain according to an embodiment of the present invention The method and device for removing the image are obtained by dividing the image into a high-frequency image and a low-frequency image, and respectively performing image enhancement on the high-frequency image and the low-frequency image. In the details of the omission, the enhanced high-frequency image and low-frequency image are combined to obtain the image after the rain pattern is removed. In addition to retaining the image details of the original high-frequency image, the false positive part can be recovered from the low-frequency image. In order to get a clearer image.
以上敘述許多實施例的特徵,使所屬技術領域中具 有通常知識者能夠清楚理解本說明書的形態。所屬技術領域中具有通常知識者能夠理解其可利用本發明揭示內容為基礎以設計或更動其他製程及結構而完成相同於上述實施例的目的及/或達到相同於上述實施例的優點。所屬技術領域中具有通常知識者亦能夠理解不脫離本發明之精神和範圍的等效構造可在不脫離本發明之精神和範圍內作任意之更動、替代與潤飾。The features of many of the embodiments are described above in the art. Those who have ordinary knowledge can clearly understand the form of this specification. Those having ordinary skill in the art will appreciate that the objectives of the above-described embodiments and/or advantages consistent with the above-described embodiments can be accomplished by designing or modifying other processes and structures based on the present disclosure. It is also to be understood by those skilled in the art that <RTIgt; </ RTI> <RTIgt; </ RTI> <RTIgt; </ RTI> <RTIgt; </ RTI> <RTIgt;
100‧‧‧影像內雨紋去除裝置100‧‧‧Image internal rain pattern removal device
110‧‧‧影像擷取單元110‧‧‧Image capture unit
120‧‧‧雨滴偵測單元120‧‧‧ raindrop detection unit
130‧‧‧處理單元130‧‧‧Processing unit
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TW103129143A TWI512681B (en) | 2014-08-25 | 2014-08-25 | Device and method for rain removal |
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TWI512681B true TWI512681B (en) | 2015-12-11 |
TW201608524A TW201608524A (en) | 2016-03-01 |
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Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
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US7660517B2 (en) * | 2005-03-16 | 2010-02-09 | The Trustees Of Columbia University In The City Of New York | Systems and methods for reducing rain effects in images |
TW201025189A (en) * | 2008-12-25 | 2010-07-01 | Huper Lab Co Ltd | Method of video object segmentation in rainy situations |
WO2012066564A1 (en) * | 2010-11-15 | 2012-05-24 | Indian Institute Of Technology, Kharagpur | Method and apparatus for detection and removal of rain from videos using temporal and spatiotemporal properties. |
TW201337787A (en) * | 2012-03-08 | 2013-09-16 | Ind Tech Res Inst | Method and apparatus for rain removal based on a single image |
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Patent Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
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US7660517B2 (en) * | 2005-03-16 | 2010-02-09 | The Trustees Of Columbia University In The City Of New York | Systems and methods for reducing rain effects in images |
TW201025189A (en) * | 2008-12-25 | 2010-07-01 | Huper Lab Co Ltd | Method of video object segmentation in rainy situations |
WO2012066564A1 (en) * | 2010-11-15 | 2012-05-24 | Indian Institute Of Technology, Kharagpur | Method and apparatus for detection and removal of rain from videos using temporal and spatiotemporal properties. |
TW201337787A (en) * | 2012-03-08 | 2013-09-16 | Ind Tech Res Inst | Method and apparatus for rain removal based on a single image |
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