JPH03184467A - Image processing device - Google Patents

Abstract

PURPOSE:To sharply binarize a half tone character by providing an average density calculating means to calculate average density by two various weighting masks at least, edge amount calculating means to calculate an a edge amount from input picture data and average value mixing means to mix plural average values calculated by the various weighting masks corresponding to the edge amount in a facsimile or a digital copying machine, etc. CONSTITUTION:Multilevel picture data 100a such as read picture data, etc., from an original inputted from a picture data input part 100 are binarized by a binarizing processing part 200 as a pseudo half tone processing and outputted from a picture data output part 500 as a display picture or a printing object, etc. On the other hand, the input multilevel data 100a are inputted to an edge amount detection part 300 as well and by a control signal 400a from a binarizing control part 400 corresponding to the detected edge amount, mixture is controlled at a mixing part 9 in an average density calculation processing part 200a constituting the binarizing processing part 200. Then, binarizing at an edge part is made proper.

Description

[Detailed Description of the Invention] [Field of Industrial Application] The present invention relates to an image processing device such as a facsimile machine or a digital copying machine, and particularly to an image processing device that binarizes and reproduces a multivalued image by pseudo halftone processing. .

[Prior Art] Conventionally, as this type of pseudo halftone processing method, ■Error diffusion method and ■Average density approximation method (Japanese Patent Application Laid-Open No. 1043693/1983)
is known.

The former binarizes the multi-value pixel data of the pixel of interest using a fixed threshold, and then distributes and adds the error between the binarization level and the multi-value pixel data of the pixel of interest to pixels near the pixel of interest at a predetermined distribution rate. This is a binarization method. The latter uses binary data that has already been binarized near the pixel of interest, and when the pixel of interest is binarized into black or white, the weighting of both is determined by the neighborhood average value, and from this average value, the pixel of interest is This is a method of selecting the one closer to the level and binarizing the pixel of interest.

The former requires two-dimensional calculations on multivalued data, and has the disadvantage that it cannot be made into hardware at low cost due to the large amount of processing. texture occurs, and the number of gradations that can be reproduced is actually extremely inferior to the former.

[Problems to be Solved by the Invention] Therefore, a method called the average density preservation method is used in which a single average density is determined and the image is binarized using this value as a threshold value, and a correction process for the binarization error is added. A new binarization method is being considered.

However, when a density-preserving binarization method is incorporated, halftone characters become thin lines and become faint or blurred, resulting in a drawback that clear binarization cannot be achieved.

The present invention eliminates the above-mentioned drawbacks of the conventional method, and provides an image processing apparatus that performs pseudo-halftone processing at low cost and high quality to realize binarized reproduction, and that can clearly binarize halftone characters.

[Means for Solving the Problem] In order to solve this problem, the image processing device of the present invention has the following features:
An image processing device that binarizes multivalued image data using an average value based on binarized data in a predetermined range that has already been binarized, the average density calculation calculating the average density using at least two different weight masks. an edge amount calculating means for calculating an edge amount from input image data; and an average value mixing means for mixing a plurality of average values calculated using the different weight masks corresponding to the edge amount.

[Examples] Examples of the present invention will be described in detail below with reference to the accompanying drawings.

Example of Configuration of Image Processing Apparatus> FIG. 1 is a block diagram showing the configuration of an image processing apparatus of this embodiment.

Multivalued image data lOOa, such as image data read from a document, inputted from the image data input section 100 is binarized by the binarization processing section 200 as pseudo halftone processing, and then output from the image data output section 500. It is output as a display screen or printed matter. Here, as will be described in detail below, the binarization processing section 200 includes an average density calculation processing section 200a that performs processing based on the average density approximation method and an error diffusion processing section 200b that performs error correction processing. Process what happened. Hereinafter, in this embodiment, this processing method will be referred to as the average density preservation method.

Note that the average density calculation processing unit 200a includes a first average density calculation unit 8a that calculates the average density using a first weight mask;
A second average density calculation unit 8b calculates the average density using a second weight mask different from the first weight mask, and mixes the two calculated average densities to create an average density for binarization processing. It consists of a mixing section 9.

On the other hand, the input multi-value data 100a is sent to the edge amount detection section 300.
The control signal 400a from the binarization control unit 400 corresponding to the detected edge amount is also input to the control signal 400a to control the mixing in the mixing unit 9 of the average density calculation processing unit 200a constituting the binarization processing unit 200. Then, binarization at the edge portion is optimized. This has made it possible to clearly binarize halftone characters in particular.

<Principle of the average concentration preservation method> Here, the principle of the average concentration preservation method is explained in Figures 2A to 2.
This will be explained with reference to Figure C.

This method converts the input multivalued data f (i, j) (0 to 255) to be binarized, shown in Figure 2A, to a plurality of already binarized data located in the vicinity, as shown in Figure 2B. The weighted average value m (
i, j) and binarize it using the average value m(i, j) as a threshold, and also calculate the average value m(i, j) and the input multivalued data f (i, j) (accurately). This is a method of correcting adjacent input multi-value data to be binarized from now on using a difference value from corrected input multi-value data (to be described later) and preserving the density.

m(i, j) = Σ, x, -, R(x, y) X B (i+x, ja
y)...■f(i, j)+E(i, j) >m(i
, j), then f(i
, l+E(i,j) When 5m(i,j), B(i,j)
= 0 Er (i, , ++1) □ (
f1. J)+ε(i,j)-m(i,j))...
・■・E2 (i+1. j) However, E (i, j) = E+ (i, j) +
Ea (i, l...■) The above principle will now be explained using the example shown in FIG.

The weight mask is set so that the total sum becomes 255 for 12 pixels near the pixel of interest, as shown in the upper part of FIG. Therefore, m(i, j) can be used as it is for binarization as a threshold value between 0 and 255. Now, the threshold value for binarizing the multivalued data 20 at the pixel of interest position, that is, m (i, j), is the binary data B (i, j) shown in the middle row.
j), m(i, j) = (37x O+27x 1) + (16
x O+27X O+37X 1+27X O+15X
l) + (6x O+16x O+27X O◆1
5X O+5 X 1)=84 Therefore, B (i, j) is “O” (because 20<84)
The error generated is 2O-84=-64, that is, Er(i, j + 1) = Ez(i +
1j)=-32. Through this error diffusion, f (i+1.j) changes from 25 to 25-32=-7, respectively.
f (i, j+1) is corrected from 30 to 3O-32=-2.

Principle of binarization processing in this embodiment> Now, in this embodiment, in addition to the above basic processing, in order to solve the problem that pale characters are binarized indistinctly, two different types of weight masks R^ are used. (x, y) and Ra(x,
y), two types of average values mA(i.

j) and m+(i, j), ∎ Find the edge amount β indicating the edge portion from the original density data, and ∎ calculate the average value mA(i.j) according to the edge amount.

j) and m5(i, j) are mixed at a predetermined ratio to obtain a binarization threshold m (i, j), and binarization is executed based on this threshold.

Here, the edge amount I2. is the so-called -dimensional Laplacian (constant α = 1),
j)+f(i+f, j3N...■ (However, when β>K, β is set to =).

In addition, the weight masks used are RA (X, y) as shown in FIG. 3, and Ra (x, y) as shown in FIG. 4, where the 12 weights are set to be approximately equal. Ra(x,
y), an average value m5(i, j) that better reflects the level of the background near the text in the manuscript is obtained,
Therefore, near the edge of the character part in the white background, me(
i, j) is mA(i.

In order to show a smaller (closer to white) value than j), the character portion can be emphasized and clearly binarized.

Therefore, the mixing process of the two types of average values mA and m obtained using the two types of weight masks described above requires an edge amount of 4. The larger the value, the closer it is to m8, and vice versa. When is small, it is controlled to approach mA.

In this embodiment, the average density m(i, j) that is mixed and used as a binarization threshold is .

The larger the constant α used in this embodiment is, the better the edge detection sensitivity is, and the larger the constant is, the more fine-grained intermediate values can be obtained from mA and me. In the specific example below, α=
1. = 128.

Example of Hardware Configuration of Binarization Processing Unit> An example of the hardware configuration of the binarization processing unit of this embodiment will be explained in detail with reference to FIG.

First, the binary data binarized by the comparator 10 is input to a D type flip-flop (hereinafter referred to as D-F/F) 31 and line memory 2, and is sequentially input to 10 D-F/F3.
By shifting to a to 3j and line memory l, each output terminal can simultaneously output 12 binary data corresponding to each weight of the weight mask area shown in FIGS. 3 and 4.

Average density calculation units 8a and 8b input these 12 outputs.
is calculated by formula ■ using the weights shown in Figures 3 and 4 in advance.
It is made up of a ROM that calculates and stores the average concentration value m A * m s according to the following, and is designed to quickly obtain the aforementioned mA (i, j) ms (x, j) using a look-up table conversion method. The two types of average values m^ and ms are respectively input to the mixing section 9.

On the other hand, the edge amount detection section 17 converts the input data into D-F/Fl.
6. Delay hold using 15 and edge! according to formula ■! I2. This is a processing unit mainly composed of adders that calculates . The output, that is, the edge amount β, is input to the mixing section 9, and within the heating section 9, calculation according to equation (2) is executed.

The output of the mixing unit 9, that is, the average density value m(i, j) as a binarization threshold is input to a comparator 10 and a subtractor 19 for error calculation, and binarization and error value calculation are executed, respectively. be done.

Now, the polarized error output from the subtracter 9 is the error ROM
12, the error data is divided into two parts in the ROM according to formula (2), E, (i, j+1) is input to the error memory 14, and is delayed and held until the next line is binarized.
+l, j) is already delayed and held by one line from the same error memory, and the input data f(i+1.j) is corrected by the adder 13 together with Ez(i+1.j). The corrected input multi-value data is input to the above-mentioned subtracter 19 and comparator 10 in synchronization with DF/Fil, and the next pixel is binarized.

The above processing operation is executed for each pixel of input multi-level data in synchronization with a data clock (not shown) input to each D-F/F.

<Other Examples of Weight Masks> The first weight mask R^ that satisfies the intermediate tone portions with two values, and the second weight mask R8 that satisfies the edge portions and clearly emphasizes them, is similar to the above embodiment. There are many possibilities, depending on the weight distribution, area, etc., without limitation. In particular, the weight mask Re(x, y) characteristically used in the present invention is
It is more effective to increase the weight as the distance from the target pixel position increases, or to set the weight to O at neighboring positions. Examples of such weight masks are shown in FIGS. 6A and 6B.

Other mixing examples> As another example in which two types of average values are mixed according to the amount of Laplacian β, I2. When β,〉β, m (i, j)=ma (i, j)9, , ≦
When β, m (i, j) = mA (i, j)...■, and two types of average values may be switched and used.

This embodiment has the advantage of simplifying the hardware of the mixing section 9.

Further, in the embodiment described above, the average values obtained by two types of weight masks were mixed, but by using three types, that is, the weight mask Rc (x, y) located between the two types of the embodiment, β,
It is also possible to switch between the three types depending on the value of . or,
Determining m(i, j) by mixing each of the three types in an analog manner is advantageous in that the processing can be continued more smoothly.

Other side of wedge amount detection> In the above embodiment, β is obtained by one-dimensional Laplacian operation, but if it is obtained two-dimensionally, the two-dimensional line segments in both orthogonal directions are more clearly expressed in binary values. It is possible to convert Furthermore, all known masks for so-called edge detection can be applied. It goes without saying that equation (2) can be executed in accordance with the operator's operations (in particular, area designation, mode selection, etc.), regardless of the amount of edges obtained from the image.

Although this embodiment has been described with monochrome processing, for example, R, G, B
, Bx are separated into four colors, especially when applied to B, a binarized image in which only black characters are emphasized can be obtained. Furthermore, when applying this embodiment to each color, it is desirable to set the edge amount detection sensitivity α to be larger for B than for other colors, and conversely to set the constant to be smaller for BK. .

[Effects of the Invention] According to the present invention, it is possible to provide an image processing device that can realize binarized reproduction by performing pseudo halftone processing at low cost and with high quality, and can clearly binarize halftone characters.

That is, compared to the error diffusion method, the amount of calculation is small and high image quality can be achieved, and character parts etc. can be clearly binarized in an adaptive manner.

[Brief explanation of drawings]

FIG. 1 is a block diagram showing the configuration of the image processing device of this embodiment. FIGS. 2A to 2C are diagrams explaining the average error preservation method. FIG. 3 is a diagram explaining the average density calculation. FIG. 5 is a diagram showing an example of the weight mask used in the second average density calculation section of this embodiment, FIG. 5 is a diagram showing the hardware configuration of the binarization processing section of this embodiment, FIG. 6A, FIG. 6B The figure shows the other side of the weight mask used in the second average density calculation section. In the figure, 8a...first average concentration calculation section, 8b...second
Average density calculation section, 9... Mixing section, 100... Image data input section, 200... Binarization processing section, 200a...
・Average density calculation processing section, 200b...Error diffusion processing section, 300...Edge amount detection section, 400...Binarization control section, 500...Image data output section.

Claims (1)

[Claims] (1) An image processing device that binarizes multivalued image data using an average value based on binarized data in a predetermined range that has already been binarized, and calculates the average density using at least two different weight masks. The image forming apparatus includes an average density calculation means, an edge amount calculation means for calculating an edge amount from input image data, and an average value mixing means for mixing a plurality of average values calculated by the different weight masks in accordance with the edge amount. An image processing device characterized by: