TW201812697A - Clustering method with a two-stage local binary pattern and an iterative image testing system thereof - Google Patents
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本發明係有關超解析度(super-resolution, SR)技術,特別是關於一種使用二階區域二元圖(two-stage local binary pattern, 2SLBP)的分群方法及疊代(iterative)影像測試系統。The present invention relates to super-resolution (SR) techniques, and more particularly to a grouping method and an iterative image testing system using a two-stage local binary pattern (2SLBP).
高解析度顯示器快速地發展,然而,仍有很多影像擷取裝置(例如監視裝置)會產生低解析度影像。為了填補兩者之間的差距,因此提出了超解析度(SR)技術。基於樣本的超解析度(example-based super resolution)為超解析度技術的一種,其自補塊(patch)資料庫中搜尋高解析度(HR)補塊,使用取得的高解析度補塊來替換低解析度(LR)輸入影像的低解析度次區塊,因而預測出高解析度影像。High-resolution displays are rapidly evolving, however, many image capture devices (such as surveillance devices) produce low-resolution images. In order to fill the gap between the two, a super-resolution (SR) technique was proposed. Sample-based super resolution is a kind of super-resolution technology. It searches for a high-resolution (HR) patch in a patch database and uses the obtained high-resolution patch. The low resolution sub-block of the low resolution (LR) input image is replaced, thus predicting a high resolution image.
在本說明書中,“高解析度”與“低解析度”為相對的用詞。因此,高解析度影像的解析度高於低解析度影像的解析度。In the present specification, "high resolution" and "low resolution" are relative terms. Therefore, the resolution of high-resolution images is higher than that of low-resolution images.
為了降低補塊資料庫的大小,通常使用迴歸(regression)技術將映射函數(mapping function)的係數或描述符(descriptor)儲存於補塊資料庫,而非直接儲存高解析度補塊本身。In order to reduce the size of the patch database, the regression function is usually used to store the coefficients or descriptors of the mapping function in the patch database instead of directly storing the high-resolution patch itself.
區域二元圖(LBP)為一種常用的描述符,其描述補塊資料庫當中補塊的區域幾何特性,用以將相同或相似視覺特性的補塊分群在一起。然而,傳統區域二元圖會產生許多的極罕發生群集(例如使用率小於全部的0.001%),因而造成儲存的浪費。因此亟需提出一種新穎的方法,用以改善分群時的缺點。The Area Binary Map (LBP) is a commonly used descriptor that describes the regional geometry of the patch blocks in the patch database to group the patches of the same or similar visual characteristics together. However, traditional regional binary maps produce many rare clusters (eg, usage rates less than 0.001% of total), resulting in wasted storage. Therefore, there is a need to propose a novel method to improve the shortcomings of grouping.
鑑於上述,本發明實施例的目的之一在於提出一種使用二階區域二元圖(2SLBP)的分群方法及疊代影像測試系統,用以減少極罕發生群集,因而避免儲存的浪費且強化超解析度影像處理的強度。In view of the above, one of the objects of the embodiments of the present invention is to provide a grouping method using a second-order region binary map (2SLBP) and an iterative image testing system, which is used to reduce clusters that rarely occur, thereby avoiding waste of storage and enhancing super-resolution. The intensity of image processing.
根據本發明實施例,劃分一影像為複數補塊。根據補塊的中心次區塊與相鄰次區塊,以產生梯度方向值;且量化梯度方向值,以產生量化梯度方向值。根據梯度方向值,以產生梯度強度值;且量化梯度強度值,以產生量化梯度強度值。串連量化梯度方向值與量化梯度強度值,以產生二階區域二元圖值,其作為指標以執行超解析度影像處理的分群。According to an embodiment of the invention, an image is divided into a plurality of patches. The gradient sub-block is generated according to the central sub-block and the adjacent sub-block of the patch; and the gradient direction value is quantized to generate a quantized gradient direction value. A gradient intensity value is generated based on the gradient direction value; and the gradient intensity value is quantized to generate a quantized gradient intensity value. The quantized gradient direction value and the quantized gradient intensity value are serially connected to generate a second-order region binary graph value, which is used as an index to perform clustering of super-resolution image processing.
根據本發明另一實施例,基於二階區域二元圖以進行超解析度影像處理的疊代影像測試系統包含內插裝置,接收低解析度的輸入影像,以產生內插像素於輸入影像的補塊當中;分群裝置,產生二階區域二元圖值作為指標;映射裝置,其儲存複數函數表,每一函數表包含複數映射函數,根據指標以取得其中一個映射函數;及預測裝置,根據所取得的映射函數,將內插像素映射至強化像素,藉以產生高解析度輸出影像。此外,含有強化像素的補塊被回饋至分群裝置、映射裝置與預測裝置至少一次,以產生更新的強化像素,因而改善輸出影像。According to another embodiment of the present invention, an iterative image testing system based on a second-order region binary image for super-resolution image processing includes an interpolation device that receives a low-resolution input image to generate an interpolation pixel for the input image. In the block; the grouping device generates the second-order region binary graph value as an index; the mapping device stores the complex function table, each function table includes a complex mapping function, according to the index to obtain one of the mapping functions; and the prediction device, according to the obtained The mapping function maps the interpolated pixels to the enhanced pixels to produce a high resolution output image. In addition, the patch containing the enhancement pixels is fed back to the grouping device, the mapping device, and the prediction device at least once to generate updated enhancement pixels, thereby improving the output image.
第一圖的流程圖顯示本發明實施例的使用二階區域二元圖(two-stage local binary pattern, 2SLBP)的分群方法100。分群方法100可對超解析度(例如基於樣本的超解析度)的影像信號執行影像處理(例如訓練與測試)。分群方法100的步驟可藉由電子電路(例如數位影像處理器)來執行,且超解析度影像處理可使用硬體、軟體或其組合來實施。The flowchart of the first figure shows a grouping method 100 using a two-stage local binary pattern (2SLBP) according to an embodiment of the present invention. The grouping method 100 can perform image processing (eg, training and testing) on image signals of super-resolution (eg, sample-based super-resolution). The steps of the grouping method 100 can be performed by an electronic circuit, such as a digital image processor, and the super-resolution image processing can be implemented using hardware, software, or a combination thereof.
於步驟11,欲進行分群的影像被劃分為預設大小(例如7x7)的補塊(patch)。第二A圖例示大小為7x7的補塊。第二A圖還顯示了中心次區塊21(例如大小為3x3),其中心像素211位於補塊的中央。In step 11, the image to be grouped is divided into patches of a preset size (for example, 7x7). The second A diagram illustrates a patch of size 7x7. The second A diagram also shows a central sub-block 21 (e.g., 3x3 in size) with its center pixel 211 in the center of the patch.
於步驟12,產生梯度(gradient)方向值。在一實施例中,產生相鄰次區塊與中心次區塊21之間的平均差值,以代表相鄰次區塊與中心次區塊21之間的梯度方向。首先,計算相鄰次區塊的平均值,且計算中心次區塊21的平均值。接著,相鄰次區塊的平均值減去中心次區塊21的平均值,以產生平均差值。在一實施例中,則是產生相鄰次區塊與中心次區塊21之間的加權(weighted)平均差值。At step 12, a gradient direction value is generated. In one embodiment, the average difference between the adjacent sub-blocks and the central sub-block 21 is generated to represent the gradient direction between the adjacent sub-blocks and the central sub-block 21. First, the average value of the adjacent sub-blocks is calculated, and the average value of the center sub-blocks 21 is calculated. Next, the average of the adjacent sub-blocks is subtracted from the average of the central sub-blocks 21 to produce an average difference. In one embodiment, a weighted average difference between adjacent sub-blocks and central sub-block 21 is generated.
第二B圖更顯示了相鄰次區塊22(其大小為3x3),其中心像素221位於中心次區塊21的中上方。相鄰次區塊22與中心次區塊21之間的平均差值以箭號來表示,用以表示該二次區塊22與21之間的梯度方向。The second B-picture further shows the adjacent sub-block 22 (which is 3x3 in size) with its center pixel 221 located above the center sub-block 21. The average difference between the adjacent sub-block 22 and the central sub-block 21 is indicated by an arrow to indicate the gradient direction between the secondary blocks 22 and 21.
對於其他方向(例如右上、右、右下、中下、左下、左及左上),重複前述步驟22,直到所有方向或者預定方向數已達到(步驟13)。第二C圖更顯示了相鄰次區塊23(其大小為3x3),其中心像素231位於中心次區塊21的右上方。相鄰次區塊23與中心次區塊21之間的平均差值以箭號來表示,用以表示該二次區塊23與21之間的梯度方向。For other directions (eg, upper right, right, lower right, lower middle, lower left, left, and upper left), the foregoing step 22 is repeated until all directions or predetermined directions have been reached (step 13). The second C-picture further shows the adjacent sub-block 23 (which is 3x3 in size) with its center pixel 231 located at the upper right of the center sub-block 21. The average difference between the adjacent sub-block 23 and the central sub-block 21 is indicated by an arrow to indicate the gradient direction between the secondary blocks 23 and 21.
於步驟14,將步驟12所產生的平均差值予以量化(quantize),以產生區域多梯度位準圖(local multi-gradient level pattern, LMGP)值,或稱為量化梯度方向值。例如,將八個相鄰次區塊與中心次區塊21之間的平均差值予以量化,因而產生八位元數值。在一實施例中,平均差值被量化為0、1或2的三個量化位準其中之一,因而形成三進位(ternary)數值。平均差值及其量化可表示為如下:其中wo 、wc 及wi 為權重,Po 為相鄰次區塊22/23的像素值,Pc 為中心次區塊21的像素值,且θ為預設臨界值。In step 14, the average difference generated in step 12 is quantized to generate a local multi-gradient level pattern (LMGP) value, or a quantized gradient direction value. For example, the average difference between eight adjacent sub-blocks and the central sub-block 21 is quantized, thus producing an octet value. In one embodiment, the average difference is quantized to one of three quantization levels of 0, 1, or 2, thus forming a ternary value. The average difference and its quantification can be expressed as follows: Where w o , w c and w i are weights, P o is the pixel value of the adjacent sub-block 22/23, P c is the pixel value of the central sub-block 21, and θ is a preset critical value.
第三A圖例示中心次區塊21與相鄰次區塊22/23的平均差值。第三B圖顯示各梯度的相應平均差值,其中臨界值θ為30。第三C圖顯示各梯度的相應量化平均差值。所產生的區域多梯度位準圖(LMGP)值可表示成三進位數值01200100,相當於十進位數值1224。The third A diagram illustrates the average difference between the central sub-block 21 and the adjacent sub-block 22/23. The third B graph shows the corresponding average difference for each gradient, where the critical value θ is 30. The third C plot shows the corresponding quantized mean difference for each gradient. The resulting regional multi-gradient level map (LMGP) value can be expressed as a trigonometric value of 01200100, which corresponds to a decimal value of 1224.
於步驟15,產生梯度強度值(gradient magnitude value)以表示相鄰次區塊22/23與中心次區塊21之間的梯度強度。在一實施例中,產生平均差值的均方根(root mean square, RMS)值,亦即計算平均差值的平方(square)的數學平均值(arithmetic mean)再取其平方根(square root)。At step 15, a gradient magnitude value is generated to represent the gradient strength between the adjacent sub-block 22/23 and the central sub-block 21. In one embodiment, the root mean square (RMS) value of the average difference is generated, that is, the mathematical mean of the square of the mean difference is calculated and the square root is taken. .
於步驟16,將步驟15所得到的平均差值的均方根(RMS)值(亦即,梯度強度值)予以量化,以產生量化梯度強度值。在一實施例中,平均差值的均方根(RMS)值及其量化可表示為如下:其中pk 為相鄰次區塊22/23的平均值,pc 為中心次區塊21的平均值,且Ψ1 、Ψ2 、ΨT-1 為預設臨界值。In step 16, the root mean square (RMS) value (i.e., the gradient intensity value) of the average difference obtained in step 15 is quantized to produce a quantized gradient intensity value. In an embodiment, the root mean square (RMS) value of the mean difference and its quantification can be expressed as follows: Where p k is the average of the adjacent sub-blocks 22/23, p c is the average of the central sub-blocks 21, and Ψ 1 , Ψ 2 , and Ψ T-1 are preset threshold values.
於步驟17,將步驟14(亦即,第一階段)所產生的區域多梯度位準圖(LMGP)值與步驟16(亦即,第二階段)所產生的量化梯度強度值(CM )予以串連(concatenate)或結合(join),以產生二階區域二元圖(two-stage local binary pattern, 2SLBP)值。在一實施例中,量化梯度強度值(CM )的位元較區域多梯度位準圖(LMGP)值的位元具有較高的有效性(significance)。步驟17所產生的二階區域二元圖(2SLBP)值,可於執行超解析度(SR)影像處理(例如訓練與測試)的分群(步驟18)時作為指標(index)。In step 17, the regional multi-gradient level map (LMGP) value generated in step 14 (ie, the first stage) and the quantized gradient intensity value (C M ) generated in step 16 (ie, the second stage) are obtained. Concatenate or join to generate a two-stage local binary pattern (2SLBP) value. In an embodiment, the bits of the quantized gradient intensity value (C M ) have a higher significance than the bits of the regional multi-gradient level map (LMGP) value. The second-order region binary map (2SLBP) value generated in step 17 can be used as an index when performing grouping (step 18) of super-resolution (SR) image processing (for example, training and testing).
根據前述的分群方法100,由於梯度方向與梯度強度係分別產生與量化,因此使用所產生的指標以執行分群時,較傳統方法更為強韌。例如,相較於傳統方法,本實施例的分群方法100所產生的極罕發生群集(例如使用率小於全部的0.001%)可有效降低,因而避免儲存的浪費且強化超解析度影像處理的強度。According to the foregoing clustering method 100, since the gradient direction and the gradient intensity are respectively generated and quantized, when the generated index is used to perform grouping, it is stronger than the conventional method. For example, compared to the conventional method, the clustering method 100 of the present embodiment generates a rare cluster (for example, the usage rate is less than 0.001%), which can be effectively reduced, thereby avoiding waste of storage and enhancing the intensity of super-resolution image processing. .
第四圖的方塊圖顯示本發明實施例基於二階區域二元圖(2SLBP)以進行超解析度(SR)影像處理的疊代影像測試系統400。疊代影像測試系統400的方塊可使用硬體、軟體或其組合來實施。The block diagram of the fourth diagram shows an iterative image testing system 400 for performing super-resolution (SR) image processing based on a second-order region binary map (2SLBP) in accordance with an embodiment of the present invention. The blocks of the iterative image testing system 400 can be implemented using hardware, software, or a combination thereof.
在本實施例中,疊代影像測試系統400可包含內插裝置41,其接收低解析度(LR)輸入影像,以產生內插像素於補塊(例如大小為7x7)當中。內插裝置41可使用適當的內插方法,例如雙三次(Bicubic)內插法。接著,分群裝置42基於二階區域二元圖(2SLBP),對含有內插像素的補塊執行第一圖所示的步驟,以產生指標。In this embodiment, the iterative image testing system 400 can include an interpolation device 41 that receives a low resolution (LR) input image to produce interpolated pixels in a patch (eg, 7x7 in size). The interpolation device 41 can use a suitable interpolation method such as bicubic interpolation. Next, the grouping device 42 performs the steps shown in the first figure on the patch including the interpolated pixels based on the second-order region binary map (2SLBP) to generate an index.
在一實施例中,分群裝置42於進行分群之前,先對含有內插像素的補塊進行常態化(normalization)。例如,將補塊的像素值減去補塊平均值以執行亮度(luminance)常態化。In one embodiment, the grouping device 42 normalizes the patch containing the interpolated pixels prior to grouping. For example, the pixel value of the patch is subtracted from the patch average to perform luminance normalization.
疊代影像測試系統400還可包含映射裝置43,其包含複數函數表,每一函數表包含複數映射函數,其通常係以矩陣係數形式儲存於映射裝置43的記憶體裝置。值得注意的是,映射函數係由基於二階區域二元圖(2SLBP)的影像訓練系統(未顯示於圖式)所提供。影像訓練系統可由傳統技術來實施,因此不予贅述。根據分群裝置42所產生的指標,取得所提供的複數映射函數當中的其中一個。The iterative image testing system 400 can also include mapping means 43, which includes a complex function table, each function table including a complex mapping function, which is typically stored in the memory device of the mapping device 43 in the form of matrix coefficients. It is worth noting that the mapping function is provided by an image training system (not shown in the figure) based on a second-order region binary map (2SLBP). The image training system can be implemented by conventional techniques and therefore will not be described. One of the provided complex mapping functions is obtained based on the index generated by the grouping device 42.
疊代影像測試系統400更可包含預測(prediction)裝置44,其根據(映射裝置43)所取得的映射函數,將(內插裝置41所產生的)內插像素映射至強化像素。因此,強化像素與補塊的其他像素形成高解析度(HR)輸出影像。值得注意的是,根據本實施例,欲進行分群的像素係由內插裝置41予以預先內插(pre-interpolate),因此內插像素的影像品質係被預測裝置44強化(enhance),而非被增大比例(scale up)。The iterative image testing system 400 may further include a prediction device 44 that maps the interpolated pixels (generated by the interpolation device 41) to the enhancement pixels in accordance with the mapping function obtained by the mapping device 43. Therefore, the enhanced pixels and other pixels of the patch form a high resolution (HR) output image. It should be noted that, according to the present embodiment, the pixels to be grouped are pre-interpolated by the interpolation device 41, so the image quality of the interpolated pixels is enhanced by the prediction device 44 instead of Scaled up.
如果補塊於分群之前執行過常態化(例如亮度常態化),則高解析度(HR)輸出影像的補塊必須進行去常態化(de-normalize),亦即常態化的逆向動作。例如,將補塊平均值加至高解析度(HR)輸出影像的補塊的像素值。If the patch is normalized before the grouping (for example, brightness normalization), the patch of the high-resolution (HR) output image must be de-normalized, that is, the normalized reverse action. For example, the patch average is added to the pixel value of the patch of the high resolution (HR) output image.
根據本實施例的特徵,含有強化像素的補塊可重複(或疊代)地受影像訓練系統(未顯示於圖式)的處理,以提供包含複數映射函數的更新函數表。如第四圖所示,含有強化像素的補塊被回饋至分群裝置42、映射裝置43與預測裝置44,以產生更新的強化像素,因而改善高解析度(HR)輸出影像。本實施例可重複執行預設次數,每一次會相應有改善的更新函數表。於實務上,由於影像訓練系統通常為離線(offline)作業,而疊代影像測試系統400則為線上(online)作業,該些更新函數表可整個的儲存於映射裝置43的記憶體裝置中,於每一次重複執行時使用相應的更新函數表。In accordance with features of the present embodiment, patches containing enhancement pixels may be repeatedly (or iteratively) processed by an image training system (not shown) to provide an update function table containing complex mapping functions. As shown in the fourth figure, the patches containing the enhancement pixels are fed back to the grouping device 42, the mapping device 43, and the prediction device 44 to produce updated enhancement pixels, thereby improving the high resolution (HR) output image. In this embodiment, the preset number of times can be repeatedly executed, and each time there is an improved update function table. In practice, since the image training system is usually an offline job, the iterative image testing system 400 is an online job, and the update function tables may be stored in the memory device of the mapping device 43 as a whole. Use the corresponding update function table for each iteration.
以上所述僅為本發明之較佳實施例而已,並非用以限定本發明之申請專利範圍;凡其它未脫離發明所揭示之精神下所完成之等效改變或修飾,均應包含在下述之申請專利範圍內。The above description is only the preferred embodiment of the present invention, and is not intended to limit the scope of the present invention; all other equivalent changes or modifications which are not departing from the spirit of the invention should be included in the following Within the scope of the patent application.
100‧‧‧分群方法100‧‧‧ group method
11‧‧‧劃分影像為補塊11‧‧‧Division of images as patches
12‧‧‧產生平均差值12‧‧‧ average difference
13‧‧‧檢視所有方向已達到13‧‧‧View all directions have been reached
14‧‧‧量化平均差值以產生區域多梯度位準圖值14‧‧‧Quantitative mean difference to generate regional multi-gradient level map values
15‧‧‧產生平均差值的均方根值15‧‧‧The root mean square value of the mean difference
16‧‧‧量化均方根值16‧‧‧Quantitative rms value
17‧‧‧產生二階區域二元圖值17‧‧‧ Generate second-order regional binary graph values
18‧‧‧執行分群18‧‧‧Performance grouping
21‧‧‧中心次區塊21‧‧‧ Central sub-block
211‧‧‧中心像素211‧‧‧Center pixel
22‧‧‧相鄰次區塊22‧‧‧ adjacent subblocks
221‧‧‧中心像素221‧‧‧Center pixel
23‧‧‧相鄰次區塊23‧‧‧ adjacent subblocks
231‧‧‧中心像素231‧‧‧Center pixel
400‧‧‧疊代影像測試系統400‧‧‧Overlay image test system
41‧‧‧內插裝置41‧‧‧Interpolation device
42‧‧‧分群裝置42‧‧‧Group device
43‧‧‧映射裝置43‧‧‧ Mapping device
44‧‧‧預測裝置44‧‧‧ forecasting device
第一圖的流程圖顯示本發明實施例的使用二階區域二元圖(2SLBP)的分群方法。 第二A圖至第二C圖顯示於相鄰次區塊與中心次區塊之間產生平均差值。 第三A圖例示中心次區塊與相鄰次區塊的平均差值。 第三B圖顯示各梯度的相應平均差值。 第三C圖顯示各梯度的相應量化平均差值。 第四圖的方塊圖顯示本發明實施例基於二階區域二元圖(2SLBP)以進行超解析度(SR)影像處理的疊代影像測試系統。The flowchart of the first figure shows a grouping method using a second-order region binary map (2SLBP) according to an embodiment of the present invention. The second to second C diagrams show an average difference between the adjacent sub-block and the central sub-block. The third A diagram illustrates the average difference between the central sub-block and the adjacent sub-block. The third B plot shows the corresponding average difference for each gradient. The third C plot shows the corresponding quantized mean difference for each gradient. The block diagram of the fourth figure shows an iterative image testing system based on a second-order region binary map (2SLBP) for performing super-resolution (SR) image processing according to an embodiment of the present invention.
Claims (14)
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