JPH02163883A - Picture processing system - Google Patents

Abstract

PURPOSE:To attain high definition binarization with the minimum memory capacity by correcting a selected reference slice level with a value obtained by multiplying the difference between the density of an object picture element and the average of the respective densities of the peripheral picture elements of the object picture element by a correction factor, and binarizing the density of the object picture element with the above-mentioned value as an effective slice level. CONSTITUTION:An appropriate reference slice level SBj to slice a black level and a white level is automatically selected for every original and for every area on every original according to the change of the density of the picture of every original and the change of the ground color of every original. Further, when an original picture having blurred shading or the blurred edge of a pattern part (contour part) is to be read, binarization processing is executed, in which the average density of the neighborhood area of an object picture element density S0 is made into the offset component of the object picture element density S0 and subtracted from S0 in a fixed ratio, and the change of the object picture element density S0 is emphasized, by setting a correction factor K to a comparatively large value. Thus, the picture having a clear edge can be obtained.

Description

[Detailed Description of the Invention] [Summary] This relates to a binarization processing method for multivalued image signals in an image processing device, and in particular, it is a method for recording originally binary images such as characters and line drawings at various density levels. This paper relates to an optimal binarization processing method for multivalued image data obtained by reading images of original documents.

The purpose of the present invention is to provide a means for performing high-quality binary processing with a small amount of memory on read image information of various originals, such as originals with different densities and originals with unclear printed areas.

In an image processing device, the number of density levels of input multi-valued image data is divided into a plurality of density levels, a reference slice level is determined for each divided density level range, and each pixel of interest in the multi-valued image data is divided into the above-mentioned values to which the density belongs. The divided density level range is determined, a reference slice level corresponding to the determined density level range is selected, and the difference between the density of the pixel of interest and the average value of each density of the surrounding pixels of the pixel of interest is multiplied by a correction coefficient. Correct the reference slice level selected above with the value.

Use the corrected value as the effective slice level.

It has a configuration that binarizes the density of the pixel of interest.

[Field of Industrial Application] The present invention relates to a binarization processing method for multivalued image signals in an image processing device, and in particular, the present invention relates to a binarization processing method for multivalued image signals in an image processing device, and in particular,
(Optimal binarization processing method for multivalued image data obtained by reading an image of a document in which a image is recorded at various 1 degree levels [Prior art] Many conventional image processing devices Now, when reading a document such as a line drawing, the operator judges the overall density level of the document and ranks it in three levels, for example, dark/light/light.
The image processing apparatus performs binarization processing by slicing the multilevel image signal at an appropriate slice level corresponding to the specified density.

This slice level designation can also be automated, and several methods have been proposed so far. Two typical examples are shown next.

(2) Prior to reading the document, a preliminary scan (prescan) is performed to collect density information and create a density histogram as illustrated in FIG.

A slice level (indicated by an arrow) is set between the two peaks of density obtained.

■ Float the slice level according to the local nature of the manuscript. For example, let the density value of the pixel of interest be S, let the density values of the eight surrounding pixels be 81 to S, and, as a constant, give the slice level S of So by the following equation.

sr -E-K (E-Σ St/8)
-・-・-(11 Here, E means reading a part of the document, for example, the leading area of the document, and determining the density based on the nature of the image information contained in it.6 For example, noise component detection using power spectrum, density E is the base slice level for ground color detection using histograms, etc.

K is a correction coefficient that depends on the thickness of the variation in the black density level (0<K<1), and the value in parentheses is the amount of variation determined by the average density of the local area where the pixel of interest exists.

[Problem to be solved by the invention]

The method (2) in which the slice level is determined by performing a preliminary scan as described above has the disadvantage that it increases the substantial document reading time and significantly reduces the operability. On the other hand, method (2), which determines the slice level based on local characteristics, requires a large memory to develop one image data, and also requires a large amount of memory between pixels. A large number of operations were required. In both methods, it is difficult to perform appropriate binarization on manuscripts in which the edges of printed areas such as seal impressions and resident cards are unclear.

SUMMARY OF THE INVENTION An object of the present invention is to provide a means for performing high-quality binarization with a small amount of memory on read image information of various originals, such as originals with different densities and originals with unclear printed areas. shall be.

[Means to solve the problem]

In order to deal with various documents with two different densities, the present invention divides the number of density levels of multivalued image data into multiple parts and sets an appropriate reference slice level (here, reference slice level) for each density level range. In addition, in order to clarify the edges of unclear parts such as NvA portions in the manuscript, an emphasis effect using Laplacian operation is used. Therefore, average the density of each pixel surrounding the pixel of interest, subtract this average density from the density of the pixel of interest, and then multiply this subtraction result by an appropriate correction coefficient and subtract it from the reference slice level above. A corrected slice level (referred to as an effective slice level) is created, and the pixel density of interest is binarized using this effective slice level.

FIG. 1 is a diagram explaining the principle of the present invention.

In Fig. 1, ■ is the input multivalued image data, and the density is expressed by the number of quantization levels N (N≧3) for each pixel. represents the density of the pixel of interest, and S to SIl represent the respective densities of its surrounding pixels.

2 is a reference slice level table, where .about.LM-1 is divided into M (M≧2), and each divided density level range is divided into .about.L1.・・・
・+1J-1 to Lj, ...+ Standard slice level 3M+, ..., determined for each of LH-2 to LH-1
Smj+..., S□□,) is given.

3 is a reference slice level selection unit, which selects the 11 degree S6 for each successive pixel of interest of the input multivalued image data 1.
Access the reference slice level table 2 using
The corresponding reference slice level 5IIJ is retrieved.

4 is an effective slice level calculation unit, which performs Laplacian calculation using the density So of the pixel of interest, the density St of each surrounding pixel St""'St reference slice level S, , correction coefficient K (0<K<1). By the following equation containing:

Find the effective slice level change.

St = SmJK (So E St
/8) "" (215 is a binarization unit, which binarizes the density S0 of the pixel of interest using the effective slice level SE and outputs it.

The setting of the correction coefficient can be changed externally depending on the ambiguity of the original image.

Alternatively, it may be determined based on the concentration of the pixel of interest (for example, N
(When So is the density level of the pixel of interest, then = SO/N, etc.) [Operation] As shown in FIG. 1, according to the present invention, the images of 5 originals are It is possible to automatically select an appropriate reference slice level SIJ for dividing the black level and white level for each document or for each region on the document in response to changes in the density and background color of the document.

Furthermore, when reading an original image in which the edges (contours) of a seal impression or drawing area are blurred, by setting the correction coefficient K relatively large, the average density ΣS8/8 of the area near the pixel density S0 of interest can be adjusted. By subtracting 1 pixel of interest concentration S00 from 30 at a constant rate as an offset component and performing binarization processing that emphasizes changes in the pixel of interest concentration 5ol117), an image with sharp edges can be obtained.

Figure 2 shows a specific example of slice level change based on the present invention. In Figure 0, the horizontal axis represents the pixel position Z(x) in the main scanning direction, the vertical axis represents the density level, and the solid line waveform represents the input multi-value. This is the level of image data.

The dotted line waveform indicates the effective slice level that changes based on this input multilevel image data.

The effective slice level not only follows the level of the input multi-value image data, but also has edge and AI adjustment effects based on Laplacian calculation, so that the slice level is relatively lower in convex parts of the input multi-value image data level. On the other hand, in the concave portion, the slice level changes to relatively increase.

〔Example〕

One embodiment of the present invention will be explained with reference to FIGS. 3 and 4. FIG.

FIG. 3 is a block diagram of a binarization processing circuit according to an embodiment of the present invention.

In the figure, 6 is an A/D that manually converts the video signal obtained by reading the original using COD etc. into a digital signal.
It is a converter.

Reference numeral 7.8 denotes a line memory, which is a shift register type memory capable of holding digital signals output from the A/D converter 6 pixel by pixel for each raster line.

9 is a ΣS calculation unit, which outputs the output of the A/D converter 6;
Each output of line memory 7.8 is input in parallel to create 3×3 matrix data consisting of each density S0 to S of the pixel of interest and its surrounding 8 pixels, and from this Σ
Calculate S, Gi.

10 is a slice level calculation table ROM, S
o, ΣS, , the calculation result of the above equation (2), that is, the effective slice level S.

A table is stored that gives . Note that the reference slice level Slj is hidden in the table as internal data.

A comparator 11 compares the effective slice level St and the density S0 of the pixel of interest, and outputs the binarized data based on the magnitude relationship.

Figure 4 shows the slice level calculation table RO of this embodiment.
This is an example of the reference slice level used in the Ml0 table, and the number of density levels is 16 (level value: 0 to 15).
), the concentration level range is O~4.4~12.
Divide into 3 into 12 to 15, and set the standard slice level S□-6, Sgt=8,5s in the 1 degree level range (each divided).
s=10 is specified.

It is possible to automatically select the optimal slice level and binarize images of documents of various quality, including characters and line drawings with blurred outlines and blurred outlines, thereby providing an image processing device with high image quality. hot water

[Brief explanation of the drawing]

Fig. 1 is an explanatory diagram of the principle of the present invention, Fig. 2 is an explanatory diagram showing a specific example of slice level change based on the present invention, Fig. 3 is a block diagram of one embodiment of the present invention, and Fig. 4 is a reference slice. FIG. 5 is an explanatory diagram of an example of a level. FIG. 5 is an explanatory diagram of an example of a density histogram. In Figure 1. 1: Multivalued image data 2: Al slice level table 3: Reference slice level selection section 4: Effective slice level calculation section 5: Binarization section [Effects of the Invention] According to the present invention, it is possible to Concentration concentration (N-+6) Shallow stock A4 row t the other day n figure rod 5 figure

Claims (1)

[Claims] In an image processing device, the number of density levels of input multi-valued image data is divided into a plurality of parts, a reference slice level is determined for each divided density level range, and a pixel of interest in the multi-valued image data is determined. For each pixel, find the divided density level range to which that density belongs, select a reference slice level corresponding to the found density level range, and calculate the difference between the density of the pixel of interest and the average value of each density of the surrounding pixels of the pixel of interest. An image processing method characterized in that the selected reference slice level is corrected by a value obtained by multiplying the difference by a correction coefficient, and the density of the pixel of interest is binarized using the value resulting from the correction as an effective slice level.