JP4201630B2 - Halftone line number identification device, halftone line number identification method, and image processing apparatus using halftone line number identification device - Google Patents

Description

式１からも分かるように，分散値は上記マトリクスサイズの画像データ内における画素値のばらつきの度合いを表すものである。従って，上記分散値σ²が小さい場合は上記ばらつきが少ないことを意味し，即ち，隣接する画素間の階調差（濃度差）が小さいことを意味する。この分散値σ²が略０である場合は上記ばらつきがほとんど無く，即ち画素間の階調差がほとんど無いことを意味する。逆に，上記分散値σ²が大きい場合は上記ばらつきが多いことを意味し，即ち画素間の階調差が大きいことを意味する。一般に，網点線数は使用する紙質と用途によってある程度決定されるものであり，複写機やＦＡＸ装置等の画像処理装置で通常使用される用紙サイズ（Ａ４，Ｂ４サイズ）では，細かな階調性の再現が要求される写真等の画像データは高線数で表現され，エッジ部の多い文字等で構成される画像データは低線数で表現されている。従って，上記分散値σ²が小さい画像データは高線数で表現された画像データであると判断することができ，上記分散値σ²が大きい画像データは低線数で表現された画像データであると判断することができる。
このように，ステップＳ２０の分散値算出処理を実行させるための手段が分散値算出手段の一例である。
【００１７】
ステップ２０においてフィルタ処理後の各画像データの分散値が算出されると，続いて上記各分散値が所定の閾値ｋと比較される（Ｓ３０，Ｓ４０，Ｓ５０，Ｓ６０）。ここで，フィルタ５１によりフィルタ処理された画像データの分散値をσ₅ ²，フィルタ７１によりフィルタ処理された画像データの分散値をσ₇ ²，フィルタ９１によりフィルタ処理された画像データの分散値をσ₉ ²とすると，ステップＳ３０ではσ₅ ²，σ₇ ²，σ₉ ²のすべてにおいてその値が閾値ｋ未満であるかどうかが判断される。上述した周波数特性グラフから読み取られるように，線数１７５以上の画像データが入力された場合は，どのフィルタでフィルタ処理を行っても網点成分を十分に除去することが可能である。このような場合，上記各分散値は小さく，所定の閾値を越えることはない。即ち，上記ステップＳ３０の処理は入力された画像データの線数が１７５以上であるかどうかを判断するものである。また，画像データをマトリクスサイズやフィルタ係数が大きいフィルタ（フィルタ９１）で処理するよりも小さいフィルタ（フィルタ５１）で処理したほうが処理時間を短縮することができ，更に画像データに含まれるエッジ部の過剰な平滑化を防止することができる。そうすると，上記のどのフィルタを用いても十分に画像データの網点成分を除去することができるのであれば，マトリクスサイズが最も小さいフィルタ５１でフィルタ処理することが望ましい。従って，上記ステップＳ３０において分散値σ₅ ²，σ₇ ²，σ₉ ²がすべて閾値ｋ未満であると判断された場合は，ステップ３１においてフィルタ５１によりフィルタ処理された画像データが出力され，その後に一連の処理が終了する。また，分散値σ₅ ²，σ₇ ²，σ₉ ²のいずれかが閾値ｋ以上であると判断されると処理はステップ４０に進む。尚，ステップＳ３０において分散値σ５のみを閾値ｋと比較判断することとしても良い。分散値σ₅ ²が閾値ｋ未満であれば，当然に分散値σ₇ ²，σ₉ ²も閾値ｋ未満であるからである。
このように，線数が１７５以上であると特定された画像データを，その線数に最も適したフィルタ５１でフィルタ処理することにより，画像データに含まれ網点成分が十分に除去されると共に，従来生じていたエッジ部分の過剰な平滑化が防止され，その結果として良好な画像を再生することが可能となる。また，フィルタ７１，９１よりもマトリクスサイズの小さいフィルタ５１によりフィルタ処理することにより，フィルタ処理に費やされる時間が大幅に短縮され得る。
【００１８】
ステップＳ４０では分散値σ₅ ²が閾値ｋ以上であり，分散値σ₇ ²，σ₉ ²が閾値ｋ未満であるかどうかが判断される。分散値σ₅ ²が閾値ｋ以上であることは，フィルタ５１では入力された画像データの網点成分を十分に除去することができず，これは該画像データが線数１７５未満のものであることを意味する。また，分散値σ₇ ²，σ₉ ²が閾値ｋ未満であることは，フィルタ７１及びフィルタ９１によって入力された画像データの網点成分を十分に除去することができ，これは該画像データが線数１５０以上のものであることを意味する。即ち，上記ステップＳ４０の処理は入力された画像データの線数が１５０以上かつ１７５未満であるかどうかを判断するものである。従って，上記ステップＳ４０の判断が肯定された場合はステップＳ４１においてフィルタ７１によりフィルタ処理された画像データが出力されて，その後に一連の処理が終了する。否定された場合は，処理はステップ５０に進む。尚，ステップＳ４０の処理が，分散値σ₅ ²が閾値ｋ以上かつσ₇ ²が閾値ｋ未満であることをと比較判断するものであっても良い。分散値σ₇ ²が閾値ｋ未満であれば，当然に分散値σ₉ ²も閾値ｋ未満であるからである。
【００１９】
ステップＳ５０では分散値σ₅ ²，σ₇ ²が閾値ｋ以上であり，分散値σ₉ ²が閾値ｋ未満であるかどうかが判断される。分散値σ₅ ²，σ₇ ²が閾値ｋ以上であることは，フィルタ５１及びフィルタ７１では入力された画像データの網点成分を十分に除去することができず，これは該画像データが線数１５０未満のものであることを意味する。また，分散値σ₉ ²が閾値ｋ未満であることは，フィルタ９１によって入力された画像データの網点成分を十分に除去することができ，これは該画像データが線数１３３以上のものであることを意味する。即ち，上記ステップＳ５０の処理は入力された画像データの線数が１３３以上かつ１５０未満であるかどうかを判断するものである。従って，上記ステップＳ５０の判断が肯定された場合はステップＳ５１においてフィルタ９１によりフィルタ処理された画像データが出力されて，その後に一連の処理が終了する。否定された場合は，処理はステップ６０に進む。尚，ステップＳ５０の処理が，分散値σ₇ ²が閾値ｋ以上かつσ₉ ²が閾値ｋ未満であることをと比較判断するものであっても良い。分散値σ₇ ²が閾値ｋ以上であれば，当然に分散値σ₅ ²も閾値ｋ以上であるからである。
【００２０】
ステップ６０ではσ₅ ²，σ₇ ²，σ₉ ²のすべてにおいてその値が閾値ｋ以上であるかどうかが判断される。線数１３３未満の画像データが入力された場合は，どのフィルタでフィルタ処理を行っても網点成分を十分に除去することができない。このような場合，上記各分散値は大きくなり所定の閾値を越えてしまう。即ち，上記ステップＳ６０の処理は入力された画像データの線数が１３３未満であるかどうかを判断するものである。従って，上記ステップＳ６０の判断が肯定された場合は，処理ステップＳ５１において上記各フィルタによりフィルタ処理がなされた画像データを出力しないで入力された画像データをそのままの状態で出力し，その後に一連の処理が終了する。上記各フィルタで処理しても網点成分を十分に除去できず，そのためフィルタ処理をするだけ時間を浪費することになりかねないからである。尚，ステップＳ６０において分散値σ₉ ²のみを閾値ｋと比較判断することとしても良い。分散値σ₉ ²が閾値ｋ以上であれば，当然に分散値σ₅ ²，σ₇ ²も閾値ｋ以上であるからである。
このようにステップＳ３０，Ｓ４０，Ｓ５０，Ｓ６０の処理が順次行われることにより，入力された画像データに含まれる網点線数が適確に識別されることになる。線数が識別された画像データは，上記ステップＳ３１，Ｓ４１，Ｓ５１において網点線数に応じた最適なフィルタで処理がなされる。これにより画像データに含まれる網点成分が十分に除去されると共に，過剰な平滑化が防止され，その結果，良好な画像を再生することが可能となる。
【００２１】
【実施例】
前記したように，上記ステップＳ３０の処理は入力された画像データの線数が１７５以上であるかどうかを判断するものである。上記実施形態の説明においても触れたが，一般に，網点線数は使用する紙質と用途によってある程度決定されるものである。通常，新聞紙に使用される更紙には線数６５，書籍・雑誌等で使用される上質紙には線数１２０若しくは１３３，カレンダー・カタログ等に使用されるコート紙には線数１５０，美術書や写真集に使用されるアート紙には線数１７５若しくは２００で画像が表現されることが多い。従って，このように紙質・用途に応じた線数が通常定められているのであれば，上記ステップＳ３０の処理を入力された画像データの線数が１７５，２００であるかどうかを判断するものであると考えることも可能である。
ステップＳ４０の処理についても，上記ステップＳ３０と同じように，入力された画像データの線数が１５０であるかどうかを判断するものであり，ステップＳ５０の処理は入力された画像データの線数が１３３であるかどうかを判断するものであると考えることができる。
【００２２】
【発明の効果】
以上説明したように，本発明は，複数のガウスフィルタによりディジタル画像データをフィルタ処理し，該フィルタ処理後の各ディジタル画像データの各々の分散値を算出することにより，かかる分散値に基づきディジタル画像データの網点線数を特定するものであり，これにより，網点線数に最も適したフィルタ処理を実行することが可能となる。その結果，文字等からなる画像データのエッジ部が過剰に平滑化されずに，良好な画像を再生することが可能となる。
【図面の簡単な説明】
【図１】本発明の実施の形態に係る網点線数特定装置のシステムブロック図。
【図２】本発明の実施の形態に係る網点線数特定装置が行う処理の一例を説明するフローチャート。
【図３】５×５のガウスフィルタを示す図。
【図４】７×７のガウスフィルタを示す図。
【図５】９×９のガウスフィルタを示す図。
【符号の説明】
１…画像データ入力部
２…分散値算出処理部
３…線数特定部
４…出力処理部
５…５×５フィルタ処理部
７…７×７フィルタ処理部
９…９×９フィルタ処理部
５１…マトリクスサイズが５×５のガウスフィルタ
５２…５×５のガウスフィルタの周波数特性グラフ
７１…マトリクスサイズが７×７のガウスフィルタ
７２…７×７のガウスフィルタの周波数特性グラフ
９１…マトリクスサイズが９×９のガウスフィルタ
９２…９×９のガウスフィルタの周波数特性グラフ[0001]
BACKGROUND OF THE INVENTION
The present invention relates to a halftone line number specifying device, a halftone line number specifying method, and an image processing apparatus using the halftone line number specifying device for specifying the number of halftone lines of digital image data having a halftone dot structure.
[0002]
[Prior art]
Conventionally, in an image processing apparatus such as a digital copying machine or a FAX apparatus, there has been a problem that moire occurs due to interference between a halftone dot of digital image data and a screen constituting an image generated by the image processing apparatus. In order to prevent the occurrence of moire, it is necessary to smooth the halftone dot structure of the digital image data and remove the halftone dot frequency component (halftone dot component) before image processing.
[0003]
For removing the halftone dot component, there is a spatial filter device proposed in Patent Document 1, for example. This filter device prevents the occurrence of moire by using a smoothing filter that uniformly removes halftone dot components contained in a halftone dot region having 100 or more lines.
[0004]
[Patent Document 1]
Japanese Patent Laid-Open No. 07-95409
[Problems to be solved by the invention]
However, when the process of removing halftone dot components of all halftone dot areas having a line number of 100 or more is uniformly performed, if a dot area having a high line number (for example, a line number of 175 or more) is filtered, the dot component Can be removed sufficiently, but on the other hand, there is a problem that edge portions such as characters are not emphasized and a clear image cannot be reproduced. Such a problem is considered to be solved by executing filter processing adapted to the number of halftone lines. For that purpose, it is necessary to specify the number of dotted lines of the digital image data.
Further, if the filtering process adapted to the number of halftone lines is not performed, an unnecessary filtering process may occur as a result, and there is a possibility that the time spent for the process is wasted.
Accordingly, the present invention has been made in view of the above circumstances, and its object is to provide a halftone line number specifying device, a halftone line number specifying method for specifying the number of halftone lines in digital image data having a halftone dot structure, Another object of the present invention is to provide image processing using the halftone dot number specifying device.
[0006]
[Means for Solving the Problems]
In order to achieve the above object, the present invention filters the digital image data with a plurality of Gaussian filters in a halftone dot number specifying device for specifying the number of lines in a halftone dot region of digital image data having different halftone dot structures. Filter processing means; variance value calculating means for calculating each variance value of the digital image data filtered by the filter processing means; and the digital image data based on the variance value calculated by the variance value calculating means. And a halftone line number specifying device for specifying the number of halftone lines. With this configuration, it is possible to execute a filter process most suitable for the number of halftone lines. As a result, a good image can be reproduced without excessively smoothing the edge portion of the image data composed of characters or the like.
[0007]
In this case, the filter processing means, it is desirable is to filter the plurality of different spatial Gaussian filters matrix size.
[0008]
Further, the present invention may be understood as a halftone line number specifying method for performing processing corresponding to each means included in the halftone line number specifying device.
That is, in a halftone dot number specifying method for specifying the number of lines in a halftone dot region of digital image data having different halftone dot structures, a filtering process step for filtering the digital image data with a plurality of Gaussian filters, and the filtering process step A variance value calculating step of calculating each variance value of the digital image data filtered by the step of calculating a halftone dot number of the digital image data based on the variance value calculated by the variance value calculating step A halftone line number identification method characterized by comprising a specific step.
[0009]
Further, the above problem can be solved by the following image processing apparatus.
That is, a halftone dot number specifying device for specifying the number of lines in a halftone dot region of digital image data having different halftone dot structures, the filter processing means for filtering the digital image data with a plurality of Gaussian filters, and the filter A variance value calculating means for calculating each variance value of the digital image data filtered by the processing means, and a network for specifying the number of dotted lines of the digital image data based on the variance value calculated by the variance value calculating means A dot number specifying device comprising: a dot line number specifying unit; and digital image data filtered by the Gaussian filter according to the dot number specified by the dot number specifying device. And an image processing apparatus characterized by comprising: means for executing pseudo halftone processing There.
[0010]
DETAILED DESCRIPTION OF THE INVENTION
Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings so that the present invention can be understood. The following embodiment is an example embodying the present invention, and does not limit the technical scope of the present invention.
FIG. 1 is a system block diagram of the halftone line number specifying device according to the embodiment of the present invention. FIG. 2 is a flowchart for explaining an example of processing performed by the halftone line number specifying device according to the embodiment of the present invention. 3 is a diagram showing a 5 × 5 Gaussian filter, FIG. 4 is a diagram showing a 7 × 7 Gaussian filter, and FIG. 5 is a diagram showing a 9 × 9 Gaussian filter.
[0011]
First, with reference to FIG. 1, a system configuration of a dotted line number specifying device according to an embodiment of the present invention will be described.
This dot number specifying device includes an image data input unit 1, a filter processing unit 5 (an example of a filter processing unit), a filter processing unit 7 (an example of a filter processing unit), a filter processing unit 9 (an example of a filter processing unit), A dispersion value calculation processing unit 2 (an example of a dispersion value calculation unit), a line number identification unit 3 (an example of a halftone line number identification unit), an output processing unit 4, and a calculation (not shown) composed of a CPU, a DSP, etc. A processing unit is provided. The digital image data input to the image input unit 1 is transferred to the filter processing units 5, 7, and 9 by the image input unit 1, and then the network included in the digital image data in each filter processing unit. Filter processing for removing point components is performed. The variance value is calculated in the variance value calculation processing unit 2 for each image data subjected to each filter processing. Thereafter, the line number specifying unit 3 specifies the number of halftone dots included in the input digital image data based on the variance value. Thereafter, the image data output unit 4 outputs the image data that has been subjected to the filtering process according to the number of halftone lines specified by the line number specifying unit 3.
[0012]
Here, the filter processing unit 5 will be described with reference to FIG.
The filter processing unit 5 uses a Gaussian filter 51 which is an example of a spatial frequency filter. As shown in FIG. 3A, the Gaussian filter 51 is a filter composed of a 5 × 5 matrix. Although one block of the matrix shown here will be described as indicating one pixel, the present invention is not limited to this, and it may be considered that the block is composed of one halftone dot or several pixels. FIG. 3B shows a graph 52 representing the frequency characteristics of the filter 51. In the frequency characteristic graph 52, the horizontal axis represents the number of halftone dots of the image data input to the filter 51, and the vertical axis represents the power spectrum of the halftone dot component included in the image data after filtering by the filter 51. It represents a power spectrum after filtering for the number of halftone lines. As can be seen from the frequency characteristic graph 52, by using the filter 51, the power spectrum of the halftone component included in the image data having more than 175 halftone lines is made substantially zero. In other words, the filter 51 has a property of substantially completely removing halftone dot components contained in image data having 175 or more halftone lines. On the other hand, with respect to image data having a dot number of less than 175, it is difficult to remove the dot component as the dot number of the image data decreases. The power spectrum is a guideline indicating how much the frequency component (halftone dot component) is included in the image data. The fact that this numerical value indicates substantially 0 means that the network included in the image data from the filter 51A. This means that the point component has been almost completely removed.
[0013]
Next, the filter processing unit 7 will be described with reference to FIG.
As the filter processing unit 7, a Gaussian filter 71, which is an example of a spatial frequency filter, is used as in the filter processing unit 5. The Gaussian filter 71 is a filter composed of a 7 × 7 matrix as shown in FIG. FIG. 4B shows a graph 72 representing the frequency characteristics of the filter 71. As can be seen from the frequency characteristic graph 72, by using the filter 71, the power spectrum of the halftone component included in the image data having 150 or more halftone lines is made substantially zero. That is, unlike the filter 51, the filter 71 has the property of removing the halftone component contained in the image data having 150 or more halftone lines almost completely. On the other hand, with respect to image data having a halftone dot number of less than 150, the halftone dot component tends to be difficult to remove as the halftone dot number of the image data decreases.
[0014]
Further, the filter processing unit 9 will be described with reference to FIG.
As the filter processing unit 9, a Gaussian filter 91, which is an example of a spatial frequency filter, is used like the filter processing unit 5 and the filter processing unit 7. As shown in FIG. 5A, the Gaussian filter 91 is a filter composed of a 9 × 9 matrix. FIG. 5B shows a graph 92 representing the frequency characteristics of the filter 91. As can be seen from the frequency characteristic graph 92, the power spectrum of the halftone dot component included in the image data having the number of halftone dots of 133 or more is made substantially zero by using the filter 91. That is, unlike the filter 51 and the filter 71, the filter 91 has the property of substantially completely removing the halftone dot component included in the image data having 133 or more halftone lines. On the other hand, with respect to image data having a dot number of less than 133, it is difficult to remove the dot component as the dot number of the image data decreases.
[0015]
Next, a processing procedure executed by an arithmetic processing unit including a CPU, a DSP, and the like provided in the halftone number specifying device according to the embodiment of the present invention will be described using the flowchart of FIG. Hereinafter, S10, S20,... Indicate processing procedure (step) numbers. The process starts from step S10.
The image data input to the image data input unit 1 is subjected to filter processing for removing halftone components included in the image data by the Gauss filters 51, 71, 91 of the filter processing units 5, 7, 9 (S10, S11, S12). As described above, in step S10, S11, and S12, means for executing filtering processing of a single digital image data using a plurality of filters having different matrix sizes is an example of the filtering processing means.
[0016]
Next, in step S20, a variance value of each image data filtered by the filters 51, 71, 91 is calculated. That is, a variance value of 5 × 5 matrix size image data that has passed through the filter 51 is calculated, and similarly, a variance value of 7 × 7 and 9 × 9 matrix size image data is calculated. The variance value σ ² here is a variance generally used in probability statistics. For example, each of the pixel values x _i (for example, 256th floor) of 25 pixels that have passed the matrix size (5 × 5) of the filter 51. The average of the deviations σ [= x _i −x _P ] of each pixel value when the average of the gradation of the tone when expressed in a tone is x _P (see Equation 1).
[Expression 1]

As can be seen from Equation 1, the variance value represents the degree of variation in pixel values in the image data of the matrix size. Therefore, when the variance value σ ² is small, it means that the variation is small, that is, a gradation difference (density difference) between adjacent pixels is small. When the dispersion value σ ² is substantially 0, it means that there is almost no variation, that is, there is almost no gradation difference between pixels. Conversely, when the variance value σ ² is large, it means that the variation is large, that is, the gradation difference between pixels is large. In general, the number of halftone lines is determined to some extent according to the quality of the paper to be used and the purpose of use. For paper sizes (A4 and B4 sizes) normally used in image processing apparatuses such as copiers and fax machines, fine gradation is provided. Image data such as photographs that are required to be reproduced is expressed with a high number of lines, and image data composed of characters with many edge portions is expressed with a low number of lines. Therefore, it can be determined that the image data having a small variance value σ ² is image data expressed by a high line number, and the image data having a large variance value σ ² is image data expressed by a low line number. It can be judged that there is.
As described above, the means for executing the variance value calculation process in step S20 is an example of the variance value calculation means.
[0017]
When the variance value of each image data after filtering is calculated in step 20, each variance value is subsequently compared with a predetermined threshold value k (S30, S40, S50, S60). Here, the variance value of the image data filtered by the filter 51 is σ ₅ ² , the variance value of the image data filtered by the filter 71 is σ ₇ ² , and the variance value of the image data filtered by the filter 91 is If σ ₉ ² is assumed, it is determined in step S30 whether or not the values of σ ₅ ² , σ ₇ ² , and σ ₉ ² are less than the threshold value k. As can be read from the above-described frequency characteristic graph, when image data having more than 175 lines is input, the halftone dot component can be sufficiently removed by any filter. In such a case, each variance value is small and does not exceed a predetermined threshold value. That is, the process in step S30 is to determine whether the number of lines of input image data is 175 or more. Further, processing time can be shortened by processing the image data with a smaller filter (filter 51) than when processing the image data with a filter having a large matrix size or filter coefficient (filter 91), and the edge portion included in the image data can be shortened. Excessive smoothing can be prevented. Then, if any halftone dot component of the image data can be sufficiently removed using any of the above-described filters, it is desirable to perform the filtering process with the filter 51 having the smallest matrix size. Therefore, if it is determined in step S30 that the variance values σ ₅ ² , σ ₇ ² , and σ ₉ ² are all less than the threshold value k, the image data filtered by the filter 51 is output in step 31, and thereafter A series of processing ends. If it is determined that one of the variance values σ ₅ ² , σ ₇ ² , or σ ₉ ² is greater than or equal to the threshold value k, the process proceeds to step 40. In step S30, only the variance value σ5 may be compared with the threshold value k. This is because if the variance value σ ₅ ² is less than the threshold value k, the variance values σ ₇ ² and σ ₉ ² are naturally also less than the threshold value k.
Thus, by filtering the image data identified as having 175 or more lines with the filter 51 most suitable for the number of lines, the halftone components contained in the image data are sufficiently removed. Therefore, the excessive smoothing of the edge portion which has conventionally occurred is prevented, and as a result, a good image can be reproduced. In addition, by performing the filter process with the filter 51 having a smaller matrix size than the filters 71 and 91, the time spent for the filter process can be significantly shortened.
[0018]
In step S40, it is determined whether or not the variance value σ ₅ ² is greater than or equal to the threshold value k and the variance values σ ₇ ² and σ ₉ ² are less than the threshold value k. If the variance value σ ₅ ² is equal to or greater than the threshold value k, the filter 51 cannot sufficiently remove the halftone dot component of the input image data. This is because the image data is less than 175 lines. Means that. Further, the fact that the variance values σ ₇ ² and σ ₉ ² are less than the threshold value k can sufficiently remove the halftone dot component of the image data input by the filter 71 and the filter 91, It means that the number of lines is 150 or more. That is, the process of step S40 determines whether or not the number of lines of input image data is 150 or more and less than 175. Therefore, if the determination in step S40 is affirmative, the image data filtered by the filter 71 is output in step S41, and then a series of processing ends. If not, the process proceeds to step 50. It should be noted that the process of step S40 may be compared with determining that the variance value σ ₅ ² is greater than or equal to the threshold value k and σ ₇ ² is less than the threshold value k. This is because if the variance value σ ₇ ² is less than the threshold value k, the variance value σ ₉ ² is naturally also less than the threshold value k.
[0019]
In step S50, it is determined whether or not the variance values σ ₅ ² and σ ₇ ² are greater than or equal to the threshold value k and the variance value σ ₉ ² is less than the threshold value k. The fact that the variance values σ ₅ ² and σ ₇ ² are equal to or greater than the threshold value k does not sufficiently remove the halftone dot component of the input image data by the filter 51 and the filter 71. It means that the number is less than 150. Further, the fact that the variance value σ ₉ ² is less than the threshold value k can sufficiently remove the halftone dot component of the image data input by the filter 91, and this is because the image data has a line number of 133 or more. It means that there is. That is, the process of step S50 is to determine whether the number of lines of input image data is 133 or more and less than 150. Therefore, if the determination in step S50 is affirmed, the image data filtered by the filter 91 is output in step S51, and then a series of processing ends. If not, the process proceeds to step 60. Note that the process of step S50 may be compared with the determination that the variance value σ ₇ ² is greater than or equal to the threshold value k and σ ₉ ² is less than the threshold value k. This is because if the variance value σ ₇ ² is greater than or equal to the threshold value k, the variance value σ ₅ ² is naturally greater than or equal to the threshold value k.
[0020]
In step 60, it is determined whether or not the values of σ ₅ ² , σ ₇ ² , and σ ₉ ² are equal to or greater than the threshold value k. When image data having fewer than 133 lines is input, the halftone dot component cannot be sufficiently removed by any filter. In such a case, each variance value becomes large and exceeds a predetermined threshold value. That is, the process in step S60 determines whether the number of lines of input image data is less than 133. Therefore, if the determination in step S60 is affirmed, the input image data is output as it is without outputting the image data filtered by the respective filters in the processing step S51. Processing ends. This is because the halftone dot component cannot be sufficiently removed even if processing is performed by each of the filters described above, and therefore time may be wasted as long as the filter processing is performed. In step S60, only the variance value σ ₉ ² may be compared with the threshold value k. This is because if the variance value σ ₉ ² is greater than or equal to the threshold value k, the variance values σ ₅ ² and σ ₇ ² are naturally greater than or equal to the threshold value k.
As described above, the processes in steps S30, S40, S50, and S60 are sequentially performed, so that the number of halftone lines included in the input image data is accurately identified. The image data for which the number of lines has been identified is processed by the optimum filter corresponding to the number of halftone lines in the above steps S31, S41, and S51. As a result, halftone dot components included in the image data are sufficiently removed and excessive smoothing is prevented, and as a result, a good image can be reproduced.
[0021]
【Example】
As described above, the process of step S30 determines whether the number of lines of input image data is 175 or more. As mentioned in the description of the above embodiment, in general, the number of halftone lines is determined to some extent depending on the paper quality and application. Usually, the number of lines used for newspapers is 65, the number of lines is 120 or 133 for high-quality paper used in books and magazines, the number of lines is 150 for coated paper used for calendars, catalogs, etc. In art paper used for books and photo books, images are often expressed with 175 or 200 lines. Therefore, if the number of lines according to the paper quality and application is normally determined as described above, it is determined whether or not the number of lines of the input image data is 175,200 in step S30. It is possible to think that there is.
Also in step S40, as in step S30, it is determined whether or not the number of lines of input image data is 150. In step S50, the number of lines of input image data is determined. It can be considered that it is to determine whether or not it is 133.
[0022]
【The invention's effect】
As described above, the present invention filters digital image data with a plurality of Gaussian filters and calculates each variance value of each digital image data after the filter processing. The number of halftone lines of data is specified, and this makes it possible to execute a filter process most suitable for the number of halftone lines. As a result, it is possible to reproduce a good image without excessively smoothing the edge portion of the image data composed of characters and the like.
[Brief description of the drawings]
FIG. 1 is a system block diagram of a halftone line number specifying device according to an embodiment of the present invention.
FIG. 2 is a flowchart for explaining an example of processing performed by the halftone line number specifying device according to the embodiment of the present invention.
FIG. 3 is a diagram illustrating a 5 × 5 Gaussian filter.
FIG. 4 is a diagram illustrating a 7 × 7 Gaussian filter.
FIG. 5 is a diagram illustrating a 9 × 9 Gaussian filter.
[Explanation of symbols]
DESCRIPTION OF SYMBOLS 1 ... Image data input part 2 ... Variance calculation processing part 3 ... Line number specific | specification part 4 ... Output processing part 5 ... 5x5 filter processing part 7 ... 7x7 filter processing part 9 ... 9x9 filter processing part 51 ... Gaussian filter 52 with 5 × 5 matrix size ... Frequency characteristic graph 71 of Gaussian filter with 5 × 5 ... Gaussian filter 72 with matrix size 7 ... Frequency characteristic graph 91 with Gaussian filter with 7x7 matrix size 9 X9 Gaussian filter 92 ... Frequency characteristic graph of 9x9 Gaussian filter

Claims (4)

In a halftone dot number identifying device for identifying the number of lines in a halftone dot region of digital image data having different halftone dot structures,
Filter processing means for filtering the digital image data with a plurality of Gaussian filters;
Dispersion value calculating means for calculating each dispersion value of the digital image data filtered by the filtering means;
A halftone line number specifying device, comprising: halftone line number specifying means for specifying the number of halftone lines of the digital image data based on the dispersion value calculated by the dispersion value calculating means. 2. The halftone dot number specifying device according to claim 1, wherein the filter processing means performs a filtering process using a plurality of Gauss filters having different matrix sizes. In a halftone dot number identification method for identifying the number of halftone dot areas of digital image data having different halftone dot structures,
A filtering process for filtering the digital image data with a plurality of Gaussian filters;
A variance value calculating step of calculating each variance value of the digital image data filtered by the filtering step;
And a halftone line number specifying step of specifying the number of halftone lines of the digital image data based on the dispersion value calculated by the dispersion value calculating step. A halftone line number identifying device for identifying the number of lines in a halftone dot region of digital image data having different halftone dot structures,
Filter processing means for filtering the digital image data with a plurality of Gaussian filters;
Dispersion value calculating means for calculating each dispersion value of the digital image data filtered by the filtering means;
A halftone line number specifying device for specifying the number of halftone lines of the digital image data based on the dispersion value calculated by the dispersion value calculating means, and the halftone line number specifying device And a means for executing a predetermined pseudo halftone process on the digital image data filtered by the Gaussian filter according to the number of halftone lines specified by the image processing apparatus.

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