JP5396182B2 - Image processing apparatus and image processing method - Google Patents

Description

  The present invention relates to an image processing apparatus and an image processing method, and more particularly to an image processing apparatus and an image processing method for setting a removal threshold value when removing the background of an original from image data obtained by reading the original.

  In copy printing, various types of paper are read as originals by an image reading device. Some paper used as a manuscript has a relatively high background density, such as newspapers, recycled paper, and colored paper. In the case of reading such an original, processing for removing the background has been conventionally performed. Thereby, in addition to clarifying the characters of the copied document, it is possible to reduce consumption of ink and toner during printing.

  When performing background removal, image processing is performed to estimate the background density of the document, a threshold is determined according to the density, and pixels having a value equal to or lower than this threshold are removed from the image data as the background. In general, this process is not easy. As a technique for determining a threshold value for background removal (hereinafter referred to as a background removal threshold value), among the following three patent documents, Patent Document 1 describes that a document image is obtained from image data obtained by reading a document during prescanning. The document describes that a background removal threshold is determined by creating a histogram within the range of background density, and background removal is performed on image data obtained by reading the document during main scanning. Patent Document 2 describes that a background removal threshold is determined for each page of image data. Further, Patent Document 3 describes, as a method for determining the background removal threshold using a histogram, how far the peaks of the histogram are separated by a dispersion calculation and determining the background removal threshold therefrom. Has been.

JP-A-4-313774 JP 2000-101839 A JP-A-7-303188

  The techniques described in Patent Document 1 and Patent Document 2 determine a background removal threshold value in page units. For this reason, the background removal threshold value is determined after pre-scanning or after reading one page of image data, and there is a problem that the background removal processing becomes slow. Further, the technique described in Patent Document 3 has a problem that the variance has to be calculated based on a histogram, and the processing becomes complicated.

  The present invention has been made in view of such a situation, and an object thereof is to provide an image processing apparatus and an image processing method capable of determining a background removal threshold at high speed with simple processing.

In order to solve the above-described problem, the image processing apparatus according to the first aspect of the present invention sequentially reads image data obtained by reading a document line by line , and N (N is all the image data). Histogram creation means for creating a histogram representing the number of pixels for each density from the image data of the pixel area read the second time), and a maximum value on the highlight side of the created histogram A background density estimating means for estimating a density corresponding to the background density of the document, and an output value when the number of pixels corresponding to the estimated background density is input to a preset monotone decreasing function as an input value. a background removal reference value setting means for setting as the removal reference value, the lower the total value of the N-th provisional and the set background removal reference value and the estimated background density Set as removal threshold, and said N-th background removal threshold of temporary as the value of the background removal threshold was linear combination with the weight of the image data of the read pixel region N-1 th, read in the N-th Based on the background removal threshold value determining means for determining the background removal threshold value of the image data of the pixel area and the background removal threshold value of the image data of the pixel area read in the Nth time, the image data of the pixel area read in the Nth time And a ground removing means for removing the ground.

According to this, since the background removal threshold value is set in a predetermined reading unit (line unit) below the page, the background removal threshold value determination process can be performed at high speed. Further, since the background density of the original is estimated based on the maximum value on the highlight side of the histogram, the background removal threshold value determination process can be performed with a simple process.

  Further, the background density estimation means estimates the background density based on the local maximum value on the highlight side of the smoothed histogram of the created histogram as the background density of the original. This prevents erroneous recognition of the background density of the document due to a minute up / down of the histogram.

  Further, the background density estimation means estimates a background density based on an initial maximum value on a highlight side of the smoothed histogram exceeding a threshold of at least one pixel number as a background density of the document. This prevents erroneous recognition of a maximum value having a small number of pixels as the background density of the document.

  In addition, the background density estimating means may determine a first pixel number threshold value in a first background density range close to a highlight side of the smoothed histogram, and highlight the smoothed histogram. A second pixel number threshold smaller than the first pixel number threshold is set in a second background density range away from the first pixel density. This prevents erroneous recognition of the background density when the image data includes an image of the platen cover.

Also, the background removal reference value setting means, and calculates the background removal reference value based on the following calculation formula. When the number of pixels corresponding to the estimated background density is x and the background removal reference value is α, (1) When the number of pixels x is less than x1, α = x1, and (2) When the number of pixels x is greater than or equal to x2, α = x2 is calculated. (3) When the number of pixels x is greater than or equal to x1 and less than x2, α = {(y2−y1) × x + y1 × x2−y2 Xx1} / (x2-x1). Here, x1, x2, y1, and y2 are predetermined values. In general, the larger the ratio of the number of pixels corresponding to the estimated background density to the total number of pixels, the higher the sharpness of the peak of the histogram and the less the background density blur. This is because it is considered that the sharpness of the mountain is lowered and the blur of the background density is increased.

Also, the background removal threshold determining means, and calculates the background removal threshold of the image data of the N-th the read pixel region based on the following calculation formula. The Nth provisional background removal threshold is DB + α, the background removal threshold of the image data of the pixel area read in the (N−1) th time is Cut (N−1), and the background of the image data in the pixel area read in the Nth time. When the removal threshold is Cut (N), the calculation is performed as Cut (N) = (1−W) × (DB + α) + W × Cut (N−1). Here, W is a weighting coefficient, and 0 <W <1. DB is the estimated background density, and α is the background removal threshold. This prevents the background removal threshold from fluctuating greatly between adjacent pixel regions.

    Further, the ratio of the coefficient of the Nth temporary background removal threshold to the coefficient of the background removal threshold of the image data of the pixel area read in the N−1th time in the linear combination is increased as the value of N is smaller. It is stipulated in. This is because the image feed area at the beginning of reading is the edge of the document, and therefore there are many background portions, and it is expected that the background density estimation accuracy is high.

In order to solve the above problem, an image processing method according to the second aspect of the present invention sequentially reads image data obtained by reading a document line by line , and N (N is all the image data read). A natural number with the maximum number of readings at the time) Step of creating a histogram representing the number of pixels for each density from the pixel data of the pixel area read the second time, and corresponding to the maximum value on the highlight side of the created histogram setting estimating a concentration as background density of the document, the estimated output value when the input to the preset monotonically decreasing function as the input value of the number of pixels corresponding to the background density as background removal reference value And a total value of the estimated background density and the set background removal reference value is set as an Nth temporary background removal threshold, and the Nth time Determining a background removal threshold, as a value of the background removal threshold linear combination with the weight of the image data of the read pixel region N-1 th, the background removal threshold of the image data of the read pixel region N th And removing the background of the image data of the pixel area read at the Nth time based on the background removal threshold value of the image data of the pixel area read at the Nth time.

  According to the present invention, an image processing apparatus and an image processing method capable of determining a background removal threshold at high speed with simple processing are provided. Since the background removal threshold can be determined by simple processing, the load on the CPU 21 spent on the background removal processing is reduced, and the load can be assigned to other processing. Further, since the background removal threshold can be determined at high speed, it is particularly effective in a scene where high speed is required, particularly when the background removal amount is obtained for each line as shown in the present embodiment. Further, when the processing unit related to the background removal processing is realized by hardware, a small-scale and simple circuit configuration can be achieved, and therefore the development period and the evaluation man-hour can be reduced.

1 is a block diagram illustrating a hardware configuration of an image reading apparatus as an image processing apparatus according to an embodiment of the present invention. 3 is a block diagram illustrating a processing unit related to background removal processing of the image reading apparatus. FIG. It is a block diagram which shows the structure of the background removal threshold value setting part of a process part. It is a figure which shows an example of the smoothing process of the histogram of a background removal threshold value setting part. It is a figure which shows the detection process of the background density | concentration by a background removal threshold value setting part. It is a figure which shows the example which cannot detect a background density | concentration by a background removal threshold value setting part. It is a figure which shows the reference value (henceforth a background removal reference value) for the background removal in a background removal threshold value setting part. It is a figure which shows an example of the setting method of the background removal reference value by a background removal threshold value setting part. It is a figure which shows an example of the weighting coefficient in a background removal threshold value setting part. It is a flowchart which shows the setting process of the background removal threshold value by a background removal threshold value setting part. It is a flowchart which shows the estimation process of the background density | concentration by a background removal threshold value setting part. It is a block diagram which shows the example of a change of the background removal threshold value setting part shown in FIG. 13 is a flowchart showing a background removal threshold value setting process in a background removal threshold value setting unit shown in FIG.

(Embodiment)
Embodiments of the present invention will be described with reference to the drawings. FIG. 1 is a block diagram showing a hardware configuration of an image reading apparatus 10 which is an embodiment of an image processing apparatus according to the present invention. As shown in the figure, the image reading apparatus 10 includes a controller 20 and an image reading mechanism 30. The image reading apparatus 10 performs a process of removing the background of the original from the image data of the original read by the image reading mechanism 30 and outputting it to an external device.

  The image reading mechanism 30 includes a light source, a document table, a document table cover, a CCD that is a charge-coupled imaging device array, a drive motor that scans the CCD, an AD converter that converts an output signal of the CCD into a digital signal, and the like. By irradiating the original placed on the original table from the light source with light, and reading the reflected light of the exposure surface by the CCD, image data of the original exposure surface is acquired. The document table cover plays a role of pressing the document placed on the document table and blocking external light. The document table cover is white in order to reduce the influence on the image data when the document is smaller than the reading range of the CCD. The document table cover may be provided with an auto seed feeder function.

  The controller 20 includes a CPU 21, a ROM 22, a RAM 23, and an interface 24. The CPU 21 performs processing according to the program recorded in the ROM 22 to control the optical document reading operation of the image reading mechanism 30 and to perform image processing for removing the background of the document from the read image data. The RAM 23 is used as a work area at this time. The interface 24 communicates with an external device using a predetermined protocol such as USB.

  FIG. 2 is a block diagram illustrating a processing unit related to the background removal processing of the image reading apparatus 10. As shown in the figure, the image reading apparatus 10 includes an image reading unit 110, an image data storage unit 120, a background removal threshold setting unit 130, a background removal unit 140, and an image output unit 150. These processing units are configured to include program elements that are processed by the CPU 21, and each unit of the controller 20 and the image reading mechanism 30 shown in FIG. 1 performs processing alone or cooperatively. Partly implemented in hardware. For example, the image reading unit 110 includes a scanning optical system of the image reading mechanism 30, the image data storage unit 120 includes a physical storage system of the RAM 23, and the image output unit 150 includes a connection circuit system of the interface 24.

  The image reading unit 110 reads a document placed on a document table and generates binary image data. The image data storage unit 120 stores the image data generated by the image reading unit 110. It is also used as a work area when removing the background from the image data. The background removal threshold value setting unit 130 reads the image data from the image data storage unit 120 line by line as the row data, and sets the background removal threshold value for that line. In other words, in the present embodiment, the background removal threshold is determined in units of lines, and background removal is performed in units of lines. For this reason, it is possible to remove the base at high speed. The background removal unit 140 performs background removal on a line basis from the image data stored in the image data storage unit 120 based on the background removal threshold set by the background removal threshold setting unit 130. The image output unit 150 outputs the image data with the background removed to an external device.

  FIG. 3 is a block diagram illustrating a configuration of the background removal threshold value setting unit 130. As shown in the figure, the background removal threshold setting unit 130 includes a histogram creation unit 131, a histogram smoothing unit 132, a background density estimation unit 133, a background density pixel number normalization unit 134, a background removal reference value setting unit 135, and a background removal. A threshold determination unit 136, a background removal threshold initial value storage unit 137, and a previous line background removal threshold storage unit 138 are provided.

  The histogram creation unit 131 reads the image data stored in the image data storage unit 120 for each line and creates a histogram. That is, a histogram for pixel values for one line instead of one page is created.

  The histogram may be created, for example, by converting each pixel value of the read image data into luminance data. Here, the histogram is represented by G (green) data values close to the luminance data among the RGB data of the pixels. Shall be created. If G is represented by 8 bits for each pixel, the horizontal axis represents a density of 0 to 255, and the vertical axis represents a histogram representing the number of pixels. The total number of pixels is equal to the number of pixels constituting one line. That is, this histogram covers all the states from the background density “0” state in which all pixel areas of one line of the document are regarded as images to the background density “225” state in which it is regarded as the background. Here, 0 indicates the lightest state (highlight), and 255 indicates the darkest state (shadow). FIG. 4A shows an example of an 8-bit input histogram.

  The histogram smoothing unit 132 smoothes the peaks of the 8-bit input histogram created by the histogram creating unit 131. Here, as shown in FIG. 4B, smoothing is performed by reconstructing a histogram using the upper 5 bits of 8-bit data. As will be described later, in this embodiment, the background density is estimated by detecting the highest peak (maximum value) of the histogram. For this reason, if the up / down change of the histogram is sensitive without smoothing, the background density may be erroneously detected. Further, the smoothing is performed in the upper 5 bits because the accuracy is 32 steps of 8 steps with respect to 255 steps of input 8 bits, and practically sufficient smoothing is possible, and an error of about 8 with respect to 255 steps is actually This is because there is no problem. On the other hand, if the upper 6 bits are set to 4 increments, sufficient smoothing cannot be performed, and if the upper 4 bits are incremented by 16 increments, the background density is considered to increase.

Background density estimation unit 133 estimates a background density D _B based on a smoothed histogram. Basically, the density corresponding to the first mountain highlight side of the histogram (maximum value) estimating a background density D _B. Therefore, it is possible to estimate the background density D _B by a simple process. For example, as shown in FIG. 5 (a), in the histogram obtained from the original near white thin underlayer, a density corresponding to a first hill Pk1 estimating the background density D _B. Further, as shown in FIG. 5 (b), even in the histogram base was obtained from dark original such as a newspaper, a concentration corresponding to a first hill Pk2 estimating the background density D _B.

However, if you've made mountains Pk3 a small number of pixels as shown in FIG. 5 (c), so that the corresponding concentrations erroneously determined background density D _B. Therefore, as shown in FIG. 5D, a threshold value Th for the number of pixels is determined, and erroneous determination is prevented by not recognizing the number of pixels equal to or less than the threshold value Th as a mountain. In this case, Pk4 that first exceeds the threshold Th from the highlight side is detected as a mountain.

  By the way, when the document is smaller than the reading range of the CCD, the image data includes the image on the document table cover. In such image data, the number of pixels having a density corresponding to the color of the document table cover increases in the histogram. Since the document table cover is white in order to reduce the influence on the read image data, there may be a peak Pk5 exceeding the threshold Th on the highlight side as shown in FIG. This mountain Pk5 is unrelated to the background of the document, so it is necessary to remove it from the detection target.

  Therefore, as shown in FIG. 5 (f), two threshold values Th1 and Th2 (Th1> Th2) are prepared as the threshold values for the number of pixels, and the range of Wt from the highlight side corresponding to the color of the document table cover is prepared. The threshold value Th1 is applied to the density, and the threshold value Th2 having a value smaller than the threshold value Th1 is applied to the density in the other range. As a result, Pk5 based on the color of the platen cover is not recognized as a mountain, and Pk6 that first exceeds the threshold Th2 from the highlight side is detected as a mountain.

  Here, the threshold value Th can be specified as a ratio to the total number of pixels in the histogram. For example, when the threshold value Th is designated as n% with respect to all pixels, the threshold value Th = (number of pixels in one line × n) / 100 can be obtained. However, what n is used is determined experimentally for each of the threshold Th1 and the threshold Th2. The width Wt to which the threshold value Th1 is applied is also set in advance based on the color of the document table cover.

Note that the base density estimating unit 133, when the mountain above a threshold as shown in FIG. 6 can not be detected, outputs the fact that background density D _B to be estimated can not be detected in the background removal threshold determination unit 136. As will be described later, for a line in which a peak exceeding the threshold value cannot be detected, background removal is performed using the same background removal value as the previous line (see step S108 in FIG. 10). Therefore, the line background density D _B can not be detected so that outputs that to the background removal threshold determination unit 136.

Background density pixel number normalization unit 134 normalizes the number of pixels corresponding to the background density D _B estimated by the background density estimating unit 133. The number of pixels included in one line, resolution, because vary depending on document width, previously normalized number of pixels corresponding to the estimated background density D _B Upon determining the background removal reference value to be described later. Here, it is assumed that the number of pixels is normalized in the range of 0 to 1000 by the following formula. That is, the normalized number of pixels of the background density = the number of pixels of the background density × the number of pixels included in the 1000/1 line.

Background removal reference value setting unit 135 sets a background removal reference value α as a reference margin of background removal threshold relative to the estimated background density D _B. Here, the background removal reference value alpha, FIG. 7 (a), the as shown in FIG. 7 (b), an amount to be added to the shadow side of the estimated background density D _B. Estimated background density D value obtained by adding the background removal reference value alpha in _B (D _{B +} α) becomes background removal threshold tentative that line. The reason is that the final background removal threshold value is determined in relation to the background removal threshold value of the previous line, as will be described later. However, in order to further simplify the process, estimated background density D value obtained by adding the background removal reference value alpha in _B a (D B ₊ alpha) may be background removal threshold of the line.

Here, the background removal reference value α for the estimated background density D _B, tend to the background density D _B at smaller the larger the number of pixels that have been normalized, so as the number of pixels normalized small increase Determine as follows. This is because when the number of pixels is assumed histogram normalized over the entire range of background density D _B, generally, the larger the number of pixels that have been normalized, as shown in FIG. 7 (a), the mountains (PK7) sharpness is high, the then shake less putative background density D _B, and as the number of pixels that have been normalized is small, as shown in FIG. 7 (b), low sharpness mountain (PK8) it is because the blur of the background density D _B is considered to increase therefrom estimated.

FIG. 8 is a diagram illustrating an example of a method for setting the background removal reference value α. In this example, uses the function to background removal reference value α is determined according to the number of pixels that have been normalized in the estimated background density D _B. This function is α = y1 when the normalized pixel number x is less than x1, and α = y2 when the normalized pixel number x is equal to or greater than x2 (where x1 <x2, y1> y2) When the normalized number of pixels x is not less than x1 and less than x2, it is determined to be α = {(y2−y1) × x + y1 × x2−y2 × x1} / (x2−x1). . Here, x1, x2, y1, and y2 are determined experimentally.

The background removal threshold value determination unit 136 includes the provisional background removal threshold value (D _B + α) of the Nth line (hereinafter referred to as line N) and the N−1th line (ie, line (N−1)). The background removal threshold value Cut (N) for line N is determined from the background removal threshold value Cut (N−1). The background removal threshold Cut (N) is determined by the density of 8-bit or 5-bit G data, and pixels having the density lower than the density are removed by the background removal unit 140. That is, a histogram before smoothing may be used, or a histogram after smoothing may be used.

  The background removal threshold value Cut (N−1) of the (N−1) th line is stored in the previous line background removal threshold value storage unit 138. However, in the case of the first line of the page (that is, N = 1), there is no background removal threshold for the line in the previous row, so the initial value stored in the background removal threshold initial value storage unit 137 is used. The background removal threshold Cut (1) for the first line of each page is determined using Cut (0). For this reason, the initial value storage unit 137 stores a standard initial value of the background removal threshold in advance. However, the background removal threshold Cut (1) may be determined using only the temporary background removal threshold of the line in the first row without using the storage unit 137.

Specifically, the background removal threshold Cut (N) is determined by the following formula: Cut (N) = (1−W) × (D _B + α) + W × Cut (N−1). Here, W is a weighting coefficient. Since the value (D _B + α) is the temporary background removal threshold value of the line N, the background removal threshold value Cut (N) includes the temporary background removal threshold value of the line N and the background removal threshold value of the line (N−1). Each is a linear combination that is weighted and added by the coefficient (1-W) and W.

That is, the smaller the weighting factor W, the greater the influence of the temporary background removal threshold (D _B + α) of the currently processed line, and the larger the weighting factor W, the background removal threshold Cut (N−1) of the previous line. ) Will increase. In the present embodiment, the influence of the temporary background removal threshold (D _B + α) of the line (line N) itself is relatively large as the line (N = 1, 2, 3,. After that, W is set so that the influence of the background removal threshold Cut (N−1) of the previous line (line (N−1)) becomes large. Thus, the background removal threshold value is prevented from changing abruptly for each line. Further, since the background portion in the image data for a (base region of example 9) line width of the original document in the time of beginning the reading of the document is large, it is expected higher estimation accuracy of the background density D _B, the The influence of the temporary ground removal threshold (D _B + α) of the line itself is increased. The intermediate portion subsequent line N estimation accuracy of the document that may fall in the background density D _B by lower underlying rate, the line (N + 1), the influence of the background removal threshold Cut (N) at the beginning of the line N readings I try to get bigger.

  Specifically, a weight coefficient W having a function form as shown in FIG. 9 is used. In the example of this figure, W is gradually increased so that the first line at the beginning of reading of the document is W = 0.5, and the B-th line is W = 1.0. = 1.0. The number of lines B in the Base portion from the first row is determined experimentally.

  In order to prevent the background removal threshold Cut (N) from becoming excessively large, a background removal threshold threshold ThC is determined. If the calculated Cut (N) exceeds ThC, the background removal threshold is set to ThC. To do.

  Next, background removal threshold setting processing in the background removal threshold setting unit 130 will be described with reference to the flowchart of FIG. First, 1 is set as the line N to be processed (S101). Then, the Nth line of the image data is read from the image data storage unit 120 (S102). Then, a histogram is created based on the read image data (S103). As described above, in this embodiment, it is assumed that a histogram is created using the G value indicated by 8 bits.

Then, to estimate the background density _{D B} (S104). FIG. 11 is a flowchart for explaining the background density estimation process (S104). In the background density estimation process, first, the histogram is smoothed (S1041). In the smoothing of the histogram, as described above, the histogram is reconstructed using the upper 5 bits of the 8 G data of each pixel to obtain a smoothed histogram.

  For the smoothed histogram, a threshold value for the number of pixels having different values for each predetermined background density range as shown in FIG. 5F is set (S1042). Specifically, a threshold value Th1 is set for the density range on the highlight side of the width Wt, and a threshold value Th2 lower than Th1 is set for the other density range.

Then, the first peak as viewed from the highlight side, that is, the highlight side, exceeding these threshold values is detected (S1043). If it is detected (S1043: Yes) estimates the concentration corresponding to the mountains and background density _{D B} (S 1044), if not detected (S1043: No), the the background density estimation failure (S 1045) .

Then, estimation processing (S104) the results of the background density is confirmed (S105 in FIG. 10), if the background density _{D B} is estimated (S105: Yes) sets the background removal reference value alpha (S106). Setting the background removal reference value, the number of pixels corresponding to the estimated background density D _B normalized to obtain the background removal reference value α in accordance with a predetermined function as shown in FIG. As described above, the background removal reference value α is determined so as to decrease as the normalized number of pixels increases and to decrease as the normalized number of pixels decreases.

When the background removal reference value α is set, the background removal threshold Cut (N) for the Nth line is calculated (S107). The background removal threshold Cut (N) for the Nth line is set to the temporary background removal threshold (D _B + α) for the Nth line and the background removal threshold Cut (N−1) for the N−1th line. Based on the weighting factor W. As for the weighting factor W, the weight of the temporary background removal threshold (D _B + α) of the Nth line is relatively large at the beginning of reading the document, and thereafter the background removal threshold of the N−1th line. The weight is set to be large. However, the background removal threshold Cut (N) is determined so as not to exceed the predetermined threshold ThC.

On the other hand, the result of the estimation process of background density, if the background density _{D B} can not be estimated (S105: No), the background removal threshold Cut (N-1) th row of the line (N-1) N-th line Is set as a background removal threshold value (S108).

  When the background removal threshold Cut (N) for the Nth line is determined, the previous line background removal threshold storage unit 138 is updated with the determined background removal threshold Cut (N) (S109). Then, the determined background removal threshold Cut (N) is notified to the background removal unit 140, and the background removal unit 140 executes the background removal of the Nth line (S110). In the background removal, pixel data having a density equal to or lower than the background removal threshold Cut (N) is removed.

  If there is an unprocessed line (S111: No), N = N + 1 is set (S112), and the process for the next line is performed. When the background removal processing for all lines is completed (S111: Yes), this processing ends.

(Example of change)
In the above embodiment, the line unit, which is the minimum processing unit, is assumed as the reading unit of the image data by the image reading unit 110. However, even if other reading units are adopted, the same procedure as in the above embodiment is used. It is possible to remove the background by using For example, a pixel area composed of a plurality of lines or a pixel area having a predetermined number of pixels of image data can be used as a reading unit.

FIG. 12 is a block diagram showing a configuration of a modified example of the background removal threshold setting unit 130 shown in FIG. The background removal threshold value setting unit 230 according to the modified example includes a previous pixel area background removal threshold value storage unit 238 as a processing unit instead of the previous line background removal threshold value storage unit 138 in the background removal threshold value setting unit 130. The other processing units 131 to 137 are basically the same as the background removal threshold value setting unit 130. As will be described later, the previous pixel area background removal threshold storage unit 238 stores the background removal threshold Cut (N−1) of the pixel area read for the N−1th time for processing of the pixel area read for the Nth time. .

  FIG. 13 is a flowchart illustrating a control operation for setting a background removal threshold in the background removal threshold setting unit 230. A processing unit constituting the background removal threshold setting unit 230 will be described with reference to FIG. In the present modification example, a specific pixel region is assumed as a unit for reading image data without being limited to a line unit. Then, the background removal threshold value setting unit 230 reads the Nth pixel region in the Nth reading. First, 1 is set as the parameter N (S201). Then, the Nth pixel area of the image data is read from the image data storage unit 120 (S202). Then, a histogram is created based on the read image data of the pixel area (S203). In this modified example, it is also assumed that a histogram is created using the G value indicated by 8 bits.

Then, to estimate the background density _{D B} (S204). This estimation process follows the flowchart shown in FIG. 11, and first smoothes the histogram (S1041). In the smoothing of the histogram, the histogram is reconstructed using the upper 5 bits of the 8 bits of G data of each pixel to obtain a smoothed histogram. For the smoothed histogram, a threshold value for the number of pixels having different values for each predetermined background density range as shown in FIG. 5F is set (S1042). Specifically, the threshold value Th1 is set in the highlight side density range of the width Wt, and the threshold value Th2 lower than Th1 is set in the other density range. Then, the first peak on the highlight side exceeding these threshold values is detected (S1043). If it is detected (S1043: Yes) estimates the concentration corresponding to the mountains and background density _{D B} (S 1044), if not detected (S1043: No), the the background density estimation failure (S 1045) .

Then, estimation processing (S204) the results of the background density is confirmed (S205 in FIG. 13), if the background density _{D B} is estimated (S205: Yes) sets the background removal reference value alpha (S206). Setting the background removal reference value, the number of pixels corresponding to the estimated background density D _B normalized to obtain the background removal reference value α in accordance with a predetermined function as shown in FIG. The background removal reference value α is determined so as to decrease as the normalized number of pixels increases, and to increase as the normalized number of pixels decreases.

When the background removal reference value α is set, the background removal threshold Cut (N) of the image data of the pixel area read in the Nth time is calculated (S207). The background removal threshold Cut (N) of the image data of the pixel area read for the Nth time is the temporary background removal threshold (D _B + α) of the image data of the pixel area read for the Nth time and the pixel area read for the N−1th time. By the linear combination of the image data with the background removal threshold Cut (N−1), it is determined as Cut (N) = (1−W) × (D _B + α) + W × Cut (N−1). Here, W is a weighting coefficient. The weighting factor W is determined to be smaller as the value of the parameter N is smaller (that is, as the reading of the original is closer). However, the background removal threshold Cut (N) is determined so as not to exceed the predetermined threshold ThC.

On the other hand, the result of the estimation process of background density, if the background density _{D B} can not be estimated (S205: No), the background removal threshold Cut the image data of the read pixel region N-1 th and (N-1) It is set as the background removal threshold value of the image data of the pixel area read in the Nth time (S208).

  When the background removal threshold Cut (N) of the image data of the pixel area read in the Nth time is determined, the previous pixel area background removal threshold storage unit 238 is updated with the determined background removal threshold Cut (N) (S209). . Then, the determined background removal threshold Cut (N) is notified to the background removal unit 140, and the background removal unit 140 executes the background removal of the image data of the pixel area read in the Nth time (S210). In the background removal, pixel data having a density equal to or lower than the background removal threshold Cut (N) is removed.

If there is an unprocessed pixel area (S211: No), N = N + 1 is set (S212), and the process for the next pixel area is performed. When the background removal processing for all the pixel regions is finished (S211: Yes), this processing is finished.

    As described above, according to the embodiment and the modification thereof, an image processing apparatus and an image processing method that can determine the background removal threshold at high speed with simple processing are provided. Since the background removal threshold can be determined by simple processing, the load on the CPU 21 spent on the background removal processing is reduced, and the load can be assigned to other processing. Further, since the background removal threshold can be determined at high speed, it is particularly effective in a scene where high speed is required, particularly when the background removal amount is obtained for each line as shown in the present embodiment. Further, when the processing unit related to the background removal processing is realized by hardware, a small-scale and simple circuit configuration can be achieved, and therefore the development period and the evaluation man-hour can be reduced.

DESCRIPTION OF SYMBOLS 10 ... Image reading apparatus 20 ... Controller 21 ... CPU
22 ... ROM
23 ... RAM
24 ... Interface 30 ... Image reading mechanism 110 ... Image reading unit 120 ... Image data storage unit 130 ... Background removal threshold setting unit 131 ... Histogram creation unit 132 ... Histogram smoothing unit 133 ... Background density estimation unit 134 ... Background density pixel number normalization 135: Background removal reference value setting unit 136 ... Background removal threshold value determination unit 137 ... Background removal threshold initial value storage unit 138 ... Previous line background removal threshold storage unit 140 ... Background removal unit 150 ... Image output unit 238 ... Previous pixel area background Removal threshold value storage unit

Claims (8)

Image data obtained by reading the original is sequentially read line by line, and the image data of the pixel area read N times (N is a natural number with the maximum number of readings when all the image data is read) being read. Histogram creation means for creating a histogram representing the number of pixels for each density from,
A background density estimating means for estimating a density corresponding to a maximum value on the highlight side of the created histogram as a background density of the original;
A background removal reference value setting means for setting, as a background removal reference value, an output value when the number of pixels corresponding to the estimated background density is input to a preset monotone decreasing function as an input value ;
The total value of the estimated background density and the set background removal reference value is set as the Nth temporary background removal threshold, and the Nth temporary background removal threshold and the N-1th time are read. A background removal threshold value determining means for determining a background removal threshold value of the image data of the pixel area read in the N-th time as a value obtained by weighting and linearly combining the background removal threshold value of the image data of the pixel area;
Ground removal means for removing the ground of the image data of the pixel area read in the Nth time, based on the ground removal threshold of the image data of the pixel area read in the Nth time;
An image processing apparatus comprising: The image processing apparatus according to claim 1,
The image processing apparatus according to claim 1, wherein the background density estimation unit estimates a background density based on a maximum value on a highlight side of a smoothed histogram of the created histogram as a background density of the document. The image processing apparatus according to claim 2,
The background density estimation unit estimates a background density based on an initial maximum value on a highlight side of the smoothed histogram exceeding a threshold of at least one pixel number as a background density of the document. Image processing device. The image processing apparatus according to claim 3,
The background density estimating means sets a first pixel number threshold value in a first background density range close to a highlight side of the smoothed histogram, and moves away from a highlight side of the smoothed histogram. A second pixel number threshold value smaller than the first pixel number threshold value is defined in the second background density range. The image processing apparatus according to claim 1,
The background removal reference value setting means, an image processing apparatus and calculates the background removal reference value based on the following calculation formula.
When the number of pixels corresponding to the estimated background density is x and the background removal reference value is α, (1) When the number of pixels x is less than x1, calculate as α = x1,
(2) If the number of pixels x is greater than or equal to x2, calculate as α = x2,
(3) When the number of pixels x is greater than or equal to x1 and less than x2, it is calculated as α = {(y2−y1) × x + y1 × x2−y2 × x1} / (x2−x1). Here, x1, x2, y1, and y2 are predetermined values. The image processing apparatus according to claim 1,
The background removal threshold determining means, an image processing apparatus and calculates the background removal threshold of the image data of the N-th the read pixel region based on the following calculation formula.
The Nth temporary background removal threshold is DB + α, the background removal threshold of the image data of the pixel area read in the N−1th time is Cut (N−1), and the background of the image data in the pixel area read in the Nth time. When the removal threshold is Cut (N), the calculation is performed as Cut (N) = (1−W) × (DB + α) + W × Cut (N−1). Here, W is a weighting coefficient, and 0 <W <1. DB is the estimated background density, and α is the background removal threshold.   The image processing apparatus according to claim 6, wherein a ratio of a coefficient of the Nth temporary background removal threshold to a coefficient of a background removal threshold of the image data of the pixel area read in the N−1th time in the linear combination. Is determined to be larger as the value of N is smaller. The image data obtained by reading the document is sequentially read line by line, and the pixel data of the pixel area read N times (N is a natural number with the maximum number of times read when all the image data is read) Creating a histogram representing the number of pixels for each density from,
Estimating the density corresponding to the maximum value on the highlight side of the created histogram as the background density of the document;
A step of setting an output value when the input to the monotonically decreasing function which is set in advance the number of pixels corresponding to the estimated background density as the input value as the background removal reference value,
The total value of the estimated background density and the set background removal reference value is set as the Nth temporary background removal threshold, and the Nth temporary background removal threshold and the N-1th time are read. Determining the background removal threshold value of the image data of the pixel region read in the Nth time as a value obtained by linearly combining the background removal threshold value of the pixel region image data with a weight ;
Removing the background of the image data of the pixel area read in the Nth time based on the background removal threshold of the image data of the pixel area read in the Nth time;
An image processing method comprising:

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