TWI807627B - Image edge detecting method and image edge processing device - Google Patents
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Abstract
Description
本發明係指一種圖像邊緣偵測方法及圖像邊緣偵測裝置,尤指一種應用於圖像降噪以及圖像銳化的圖像邊緣偵測方法及圖像邊緣偵測裝置。 The present invention refers to an image edge detection method and image edge detection device, especially an image edge detection method and image edge detection device applied to image noise reduction and image sharpening.
現有的圖像處理技術包含藉由邊緣偵測方法判斷圖像中的邊緣是否具有方向,例如一種邊緣偵測方法可根據邊緣梯度判斷邊緣是否具有方向。然而,當邊緣偵測方法遇到弱邊緣或強紋理時,可能判斷不準確,進而影響圖像銳化和圖像降噪等所需的邊緣訊息,而導致邊緣模糊或邊緣雜訊明顯。 Existing image processing techniques include judging whether an edge in an image has a direction by an edge detection method, for example, an edge detection method can judge whether an edge has a direction according to an edge gradient. However, when the edge detection method encounters weak edges or strong textures, the judgment may be inaccurate, which will affect the edge information required for image sharpening and image noise reduction, resulting in blurred edges or obvious edge noise.
具體而言,傳統的邊緣偵測技術根據索伯(Sobel)算子(Operator)計算圖像不同方向的梯度,並且將梯度的幅度較大的區域認為有方向邊緣。然而,當有方向紋理的梯度不夠大,上述方法可能將紋理辨識為邊緣而造成誤判,或者,當有方向邊緣較弱,上述方法也難以檢測到有方向邊緣。因此,現有技術有改進的必要。 Specifically, the traditional edge detection technology calculates gradients in different directions of an image according to a Sobel operator, and regards regions with larger gradients as having directional edges. However, when the gradient of the directional texture is not large enough, the above method may identify the texture as an edge and cause misjudgment, or, when the directional edge is weak, the above method is also difficult to detect the directional edge. Therefore, there is a need for improvement in the prior art.
有鑑於此,本發明之主要目的即在於提供一種圖像邊緣偵測方法及 圖像邊緣偵測裝置,以有效偵測圖像中有方向的邊緣。 In view of this, the main purpose of the present invention is to provide a kind of image edge detection method and An image edge detection device to effectively detect directional edges in an image.
本發明實施例揭露一種圖像邊緣偵測方法,用以處理包含有複數個像素之一圖像,該方法包含有以一第一方向之梯度算子及一第二方向之梯度算子,分別對該圖像進行一卷積運算,以獲得一第一方向梯度資料及一第二方向梯度資料,其中該第一方向垂直於該第二方向;根據該第一方向梯度資料及該第二方向梯度資料,在該圖像之一目標像素之一鄰近區域內計算梯度統計,以得到一梯度統計量;以及根據該梯度統計量,決定對應於該目標像素之一邊緣顯著度。 The embodiment of the present invention discloses an image edge detection method for processing an image comprising a plurality of pixels. The method includes performing a convolution operation on the image with a gradient operator in a first direction and a gradient operator in a second direction to obtain gradient data in a first direction and gradient data in a second direction, wherein the first direction is perpendicular to the second direction; according to the gradient data in the first direction and the gradient data in the second direction, gradient statistics are calculated in an adjacent region of a target pixel of the image to obtain a gradient statistic; The edge saliency of one of the target pixels.
本發明實施例另揭露一種圖像邊緣偵測裝置,用以處理包含有複數個像素之一圖像,其包含有一卷積電路,包含有一第一卷積電路單元以及一第二卷積電路單元,用來分別以一第一方向之梯度算子及一第二方向之梯度算子,分別對該圖像進行一卷積運算,以獲得一第一方向梯度資料及一第二方向梯度資料,其中該第一方向垂直於該第二方向;一統計電路,用來根據該第一方向梯度資料及該第二方向梯度資料,在該圖像之一目標像素之一鄰近區域內計算梯度統計,以得到一梯度統計量;以及一判斷電路,用來根據該梯度統計量,決定對應於該目標像素之一邊緣顯著度。 The embodiment of the present invention further discloses an image edge detection device for processing an image comprising a plurality of pixels, which includes a convolution circuit, including a first convolution circuit unit and a second convolution circuit unit, which are used to respectively perform a convolution operation on the image with a gradient operator in the first direction and a gradient operator in the second direction to obtain gradient data in the first direction and gradient data in the second direction, wherein the first direction is perpendicular to the second direction; Gradient statistics are calculated in a neighboring region of the pixel to obtain a gradient statistic; and a judging circuit is used to determine an edge saliency corresponding to the target pixel according to the gradient statistic.
10:圖像邊緣偵測方法 10: Image edge detection method
102-112:步驟 102-112: Steps
20,50:圖像邊緣偵測裝置 20,50: Image edge detection device
202,502:卷積電路 202,502: convolutional circuits
2022:第一卷積電路單元 2022: The first convolutional circuit unit
2024:第二卷積電路單元 2024: The second convolutional circuit unit
204,504:乘積電路 204,504: product circuits
206,506:統計電路 206,506: Statistical circuits
208,508:判斷電路 208,508: judgment circuit
210:處理電路 210: processing circuit
5022,5024:卷積電路單元 5022,5024: convolutional circuit unit
f(Je):邊緣顯著度函式 f(Je): edge saliency function
I:圖像 I: Image
Ixx(i,j)、Iyy(i,j)、Ixy(i,j):梯度統計量 Ixx (i,j), Iyy (i,j), Ixy (i,j): gradient statistics
Je:邊緣顯著度 Je: edge salience
Y:通道 Y: channel
UV:通道 UV: channel
YUV:圖像 YUV: Image
第1圖為本發明實施例之一圖像邊緣偵測方法之示意圖。 FIG. 1 is a schematic diagram of an image edge detection method according to an embodiment of the present invention.
第2圖為本發明實施例之一圖像邊緣偵測裝置之示意圖。 FIG. 2 is a schematic diagram of an image edge detection device according to an embodiment of the present invention.
第3圖為本發明實施例之一映射函數之一映射曲線之示意圖。 FIG. 3 is a schematic diagram of a mapping curve of a mapping function according to an embodiment of the present invention.
第4圖為本發明實施例之一應用場景之示意圖。 FIG. 4 is a schematic diagram of an application scenario of an embodiment of the present invention.
第5圖為本發明另一實施例之圖像邊緣偵測裝置之示意圖。 Fig. 5 is a schematic diagram of an image edge detection device according to another embodiment of the present invention.
請同時參考第1圖及第2圖,第1圖為本發明實施例之一圖像邊緣偵測方法10之示意圖,第2圖為本發明實施例之一圖像邊緣偵測裝置20之示意圖。圖像邊緣偵測裝置20可用來執行圖像邊緣偵測方法10以處理包含有複數個像素之一圖像I。圖像邊緣偵測裝置20包含有一卷積電路202、一乘積電路204、一統計電路206、一判斷電路208及一處理電路210。
Please refer to FIG. 1 and FIG. 2 at the same time. FIG. 1 is a schematic diagram of an image
在步驟104中,圖像邊緣偵測方法10以一第一方向之梯度算子(Operator)及一第二方向之梯度算子,分別對圖像I進行一卷積運算,以獲得一第一方向梯度資料Gx及一第二方向梯度資料Gy,其中第一方向垂直於第二方向。
In
具體而言,卷積電路202之第一卷積電路單元2022以及第二卷積電路單元2024可針對圖像I的所有像素,分別以第一方向之梯度算子及第二方向之梯度算子進行卷積運算。在一實施例中,第一卷積電路單元2022可對圖像I以x方向梯度運算子Kx進行卷積運算,以得到第一方向梯度資料Gx,而第二卷積電路單元2024可對圖像I以y方向梯度運算子Ky進行卷積運算,以得到第二方向梯度資料Gy,並且上述x方向(即第一方向)與y方向(即第二方向)相互垂直。
Specifically, the first
舉例來說,當目標像素A在圖像I中的座標為(i,j),圖像邊緣偵測裝置20可根據圖像邊緣偵測方法10之步驟104得到目標像素A之第一方向梯度資料
Gx以及第二方向梯度資料Gy,如下列式(1)-(4):Kx=[-1 0 1]...(1)
For example, when the coordinates of the target pixel A in the image I are (i, j), the image
Ky=[-1 0 1] T ...(2) Ky = [-1 0 1] T ... (2)
其中,Kx為x方向的梯度運算子、Ky為y方向的梯度運算子,Gx(i,j)代表目標像素A與x方向梯度運算子Kx進行卷積後的結果、Gy(i,j)代表目標像素A與y方向梯度運算子Ky進行卷積後的結果。 Among them, Kx is the gradient operator in the x direction, Ky is the gradient operator in the y direction, Gx(i,j) represents the result of convolution between the target pixel A and the gradient operator Kx in the x direction, and Gy(i,j) represents the result of convolution between the target pixel A and the gradient operator Ky in the y direction.
由於現有的圖像邊緣偵測技術在決定圖像的邊緣方向性時,僅採用單一像素的梯度訊息,無法有效地辨識圖像中的紋理或弱邊緣,進而影響辨識圖像邊緣的準確性。因此,本發明實施例之圖像邊緣偵測方法10進一步採用目標像素A的鄰近區域的像素梯度訊息,以用於辨識圖像邊緣,其中鄰近區域可包含目標像素A之至少一相鄰像素。在一實施例中,鄰近區域可以是相鄰於目標像素A的5*5或7*7個像素單元的鄰近像素。
Since the existing image edge detection technology only uses the gradient information of a single pixel when determining the edge directionality of the image, it cannot effectively identify the texture or weak edge in the image, which further affects the accuracy of image edge identification. Therefore, the image
在步驟106中,圖像邊緣偵測方法10根據第一方向梯度資料Gx及第二方向梯度資料Gy,在圖像I之目標像素A之鄰近區域內計算梯度統計,以得到一梯度統計量。
In
實施上,可先利用乘積電路204,根據對於目標像素A的第一方向梯度資料Gx以及第二方向梯度資料Gy,計算第一方向梯度資料Gx的平方Gxx=GxGx、第二方向梯度資料Gy的平方Gyy=GyGy以及第一方向梯度資料Gx
與第二方向梯度資料Gy之乘積Gxy=GxGy。
In practice, the
接著,統計電路206可根據一加權係數、第一方向梯度資料Gx的平方Gxx、第二方向梯度資料Gy的平方Gyy以及第一方向梯度資料Gx與第二方向梯度資料Gy之乘積Gxy,計算目標像素A鄰近區域內的梯度統計量,其中加權係數可以是一高斯加權或一均值加權。
Then, the
當加權係數w為高斯加權係數時,可以如式(5)所示:
當加權係數w為均值係數時,可以如式(6)所示:w(m,n)=1...(6) When the weighting coefficient w is the mean coefficient, it can be shown in formula (6): w(m,n)=1...(6)
其中,σ為一常態分布的標準偏差,m、n的值為目標像素A的鄰近區域的範圍。例如,當鄰近區域為5*5像素單元時,則m、n分別為-2至2之間的整數(即-2、-1、0、1、2);當鄰近區域為7*7像素單元時,則m、n分別為-3至3之間的整數(即-3、-2、-1、0、1、2、3)。 Wherein, σ is the standard deviation of a normal distribution, and the values of m and n are the range of the adjacent area of the target pixel A. For example, when the adjacent area is a 5*5 pixel unit, m and n are integers between -2 and 2 (ie -2, -1, 0, 1, 2); when the adjacent area is a 7*7 pixel unit, then m and n are integers between -3 and 3 (ie -3, -2, -1, 0, 1, 2, 3).
因此,梯度統計量Ixx(i,j)、Iyy(i,j)、Ixy(i,j)可以如式(7)、(8)、(9)所示:
值得注意的是,圖像邊緣偵測方法10之步驟104及步驟106僅描述針對單一目標像素A計算目標像素A的鄰近像素的梯度統計量Ixx(i,j)、Iyy(i,j)、Ixy(i,j)。實際上,本發明實施例之圖像邊緣偵測裝置20根據圖像邊緣偵測方法10對圖像I中的所有像素計算對應的梯度統計量Ixx(i,j)、Iyy(i,j)、Ixy(i,j)。
It should be noted that the
在此情形下,本發明實施例的圖像邊緣偵測方法10將目標像素A以及鄰近於目標像素A的相鄰區域的像素的梯度訊息作為決定圖像的邊緣方向性的判斷依據,以提升圖像邊緣方向性判斷的準確性。
In this case, the image
在步驟108中,圖像邊緣偵測方法10根據梯度統計量Ixx(i,j)、Iyy(i,j)、Ixy(i,j),決定對應於目標像素A之一邊緣顯著度Je。
In
具體而言,邊緣顯著度Je可根據梯度統計量Ixx(i,j)、Iyy(i,j)、Ixy(i,j)之二階統計量之一共變異數矩陣(covariance matrix)決定。例如,梯度統計量Ixx(i,j)、Iyy(i,j)、Ixy(i,j)的一共變異數矩陣Cxy可以如式(10)所示:
當共變異數矩陣Cxy之一主方向之特徵值(eigenvalue)λ1與一次方向之特徵值λ2之一差距越大時,表示目標像素A之邊緣顯著度越大,其中主方向與次方向相互正交。由於梯度統計量Ixx(i,j)、Iyy(i,j)、Ixy(i,j)是根據目標像素A以及其鄰近區域的像素的梯度資訊所獲得,而當λ1≫λ2時,對應的鄰近區域可能為邊緣區域。 When the difference between the eigenvalue λ 1 of the main direction and the eigenvalue λ 2 of the primary direction of the covariance matrix Cxy is larger, it means that the edge saliency of the target pixel A is greater, wherein the main direction and the secondary direction are orthogonal to each other. Since the gradient statistics Ixx (i, j), Iyy (i, j), and Ixy (i, j) are obtained according to the gradient information of the target pixel A and the pixels in its adjacent area, when λ 1 ≫ λ 2 , the corresponding adjacent area may be an edge area.
在一實施例中,本發明實施例之圖像邊緣偵測方法10即可將邊緣顯著度Je設置為:
當λ1與λ2的差異越大時,則邊緣顯著度越大;當λ1與λ2相同時,則邊緣顯著度Je=2。除此之外,式(11)也可推導成為等效式(12):
實施上,可藉由判斷電路208來判斷邊緣顯著度Je,判斷電路208可利用式(11)或式(12)計算邊緣顯著度Je。
In practice, the
在步驟110中,圖像邊緣偵測方法10將對應於圖像I之邊緣顯著度Je進行正規化,並且以一映射方式決定圖像I之一邊緣資料。
In
由於邊緣顯著度Je的範圍為[2,∞),因此在實際使用時需要正規化至0至1之間,並進行適當的映射,以符合應用場景的需求。本發明實施例之圖像邊緣偵測方法10將對應於圖像之邊緣顯著度Je進行正規化(Normalized),並且以一映射方式決定圖像之一邊緣資料Jec,其可表示為:Jec=f(Je)...(13)
Since the range of the edge saliency Je is [2,∞), it needs to be normalized to be between 0 and 1 in actual use, and be properly mapped to meet the requirements of the application scenario. The image
在一實施例中,圖像邊緣偵測方法10可透過一映射函數f(.)將邊緣顯著度Je調整到適當的值,上述映射方式可以是一簡單線性映射、一查找表映射、一指數映射或一對數映射等。
In one embodiment, the image
當映射方式為簡單線性映射時,邊緣資料Jec可表示為:Jec=min(max(a(Je-b),0),1)...(14) When the mapping method is simple linear mapping, the edge data Jec can be expressed as: Jec =min(max( a ( Je - b ),0),1)...(14)
當映射方式為查找表映射時,則可根據第3圖所示之曲線所對應的一查找表來進行,並且將邊緣顯著度Je截位(clip)至一定範圍後再進行查找表映射,在此情形下,邊緣資料Jec可表示為:Jec=LUT(min(max(Je,a),b))...(15) When the mapping method is look-up table mapping, it can be performed according to a look-up table corresponding to the curve shown in Figure 3, and the edge saliency Je is clipped to a certain range before performing look-up table mapping. In this case, the edge data Jec can be expressed as: Jec =LUT(min(max(Je,a),b))...(15)
實施上,可利用處理電路210來對邊緣顯著度Je進行正規化及映射處理。在一實施例中,處理電路210係包含一查表電路,查表電路藉由查找預先儲存於記憶體的查找表來完成映射處理。
In practice, the
值得注意的是,本發明實施例圖像邊緣偵測方法10可因應不同應用場景選擇不同的映射方式調整邊緣顯著度Je。舉例來說,當圖像I的感光度(ISO值)為一低感光度時(例如ISO 100),處理電路210的查表電路藉由查找一第一查找表來進行映射處理,而當圖像I的感光度(ISO值)為一高感光度時(例如ISO 3200),處理電路210的查表電路藉由查找一第二查找表來進行映射處理,第一查找表與第二查找表係對應不同的映射曲線。
It is worth noting that the image
請參考第4圖,第4圖為本發明實施例之一應用場景之示意圖。依據本發明實施例之圖像邊緣偵測方法10所產生的圖像I像素的邊緣資料Jec,可應用於對圖像I的一銳化(Sharpening)處理。
Please refer to FIG. 4, which is a schematic diagram of an application scenario of an embodiment of the present invention. The edge data Jec of the image I pixels generated by the image
銳化處理是用以提昇圖像的銳利度,一般來說,銳化處理是針對YUV圖像的Y通道(即亮度通道)進行處理。如第4圖所示,對圖像I進行處理時係先將Y通道分離出來。接著,對Y通道進行細節提取,以產生一紋理層、一邊緣層及一基礎層,而依據本發明實施例之圖像邊緣偵測方法10所產生的邊緣顯著度Je可用來做為調整圖像的邊緣層的參數。實施上,係利用邊緣顯著度Je經正規化及映射處理後所得到的邊緣資料Jec調整圖像的邊緣層。
The sharpening process is used to improve the sharpness of the image. Generally speaking, the sharpening process is performed on the Y channel (ie, the brightness channel) of the YUV image. As shown in Figure 4, when image I is processed, the Y channel is first separated. Next, the details of the Y channel are extracted to generate a texture layer, an edge layer and a base layer, and the edge saliency Je generated by the image
在一實施例中,邊緣資料Jec可與對應的邊緣層相乘(如第4圖所示,即邊緣資料Jec為邊緣層的一乘積係數)。在其它實施例中,邊緣資料Jec也可以加法、減法、或除法等邏輯運算方式來調整邊緣層。最後,經由本發明實施例之邊緣資料Jec調整後的圖像邊緣層與基礎層結合後得到銳化後的Y通道,再與經由顏色處理的UV通道合併,以輸出處理後的YUV圖像。 In one embodiment, the edge data Jec can be multiplied by the corresponding edge layer (as shown in FIG. 4 , that is, the edge data Jec is a multiplication coefficient of the edge layer). In other embodiments, the edge data Jec can also adjust the edge layer by logical operations such as addition, subtraction, or division. Finally, the image edge layer adjusted by the edge data Jec of the embodiment of the present invention is combined with the base layer to obtain a sharpened Y channel, which is then combined with the color-processed UV channel to output a processed YUV image.
請參考第5圖,第5圖為本發明實施例之一圖像邊緣偵測裝置50之示意圖。圖像邊緣偵測裝置50亦可用以實施圖像邊緣偵測方法10。圖像邊緣偵測裝置50包含一卷積電路502、一乘積電路504、一統計電路506及一判斷電路508,其中卷積電路502包含有卷積電路單元5022、5024。
Please refer to FIG. 5 , which is a schematic diagram of an image edge detection device 50 according to an embodiment of the present invention. The image edge detection device 50 can also be used to implement the image
如第5圖所示,圖像I被輸入至圖像邊緣偵測裝置50,可經由卷積電路的卷積電路單元5022、5024分別對圖像I進行卷積運算(例如,卷積電路單元5022以x方向梯度運算子Kx對圖像I進行卷積運算,卷積電路單元5024以y方向梯度運算子Ky對圖像I進行卷積運算),以得到第一方向梯度資料Gx以及第二方向梯度資料Gy。為了配合的乘積電路504的需求及/或減少計算量,卷積電路502可包含有多個移位電路,以對卷積電路單元5022、5024之輸出進行移位處理(例
如,向右移位一位元(bit))。
As shown in FIG. 5, the image I is input to the image edge detection device 50, and the image I can be convoluted through the
接著,乘積電路504利用平方電路以及乘法電路,計算第一方向梯度資料Gx的平方Gxx、第二方向梯度資料Gy的平方Gyy以及第一方向梯度資料Gx與第二方向梯度資料Gy之乘積Gxy,並且由移位電路以及截位電路Clip[.]將其結果向右移兩位元,並截取-2048至2047位元,以配合統計電路506的需求及/或減少計算量。
Next, the
進一步地,統計電路506包含加總電路Σ(.)以及截位電路Clip[.],以根據鄰近於目標像素A的鄰近區域的梯度資料,計算局部區域內的梯度統計量Ixx(i,j)、Iyy(i,j)、Ixy(i,j)並進行截位處理。
Further, the
最後,判斷電路508可以邏輯運算電路、移位電路、截位電路Clip[.]及除法電路實現上述式(12),以得到對應的邊緣顯著度Je。在一實施例中,圖像邊緣偵測裝置50可進一步包含一處理電路(未繪示),用以對邊緣顯著度Je進行正規化及映射處理,以符合應用場景的需求。
Finally, the judging
值得注意的是,第5圖所標示的位元數為用來說明圖像邊緣偵測裝置50的各個電路之間的位元關係,而非用來限定圖像邊緣偵測裝置50的實施方式。此外,圖像邊緣偵測裝置50中的硬體電路及其連接關係僅為實施圖像邊緣偵測方法10的一種電路實施方式,而不以此為限制。
It should be noted that the number of bits indicated in FIG. 5 is used to illustrate the bit relationship between the various circuits of the image edge detection device 50 , rather than to limit the implementation of the image edge detection device 50 . In addition, the hardware circuit and its connections in the image edge detection device 50 are only a circuit implementation for implementing the image
綜上所述,本發明所提供的圖像邊緣偵測方法及圖像邊緣偵測裝置,以鄰近於目標像素的梯度統計訊息來判斷圖像的邊緣,進而有效提升偵測 圖像中的有方向的邊緣的精準度。 In summary, the image edge detection method and the image edge detection device provided by the present invention judge the edge of the image with the gradient statistical information adjacent to the target pixel, thereby effectively improving the detection The accuracy of oriented edges in the image.
以上所述僅為本發明之較佳實施例,凡依本發明申請專利範圍所做之均等變化與修飾,皆應屬本發明之涵蓋範圍。 The above descriptions are only preferred embodiments of the present invention, and all equivalent changes and modifications made according to the scope of the patent application of the present invention shall fall within the scope of the present invention.
10:圖像邊緣偵測方法 10: Image edge detection method
102-112:步驟 102-112: Steps
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