JPH01232893A - Noise elimination method for color picture - Google Patents

Abstract

PURPOSE:To effectively eliminate noise at a contour part in versatile color pictures by applying color change in terms of probability in response to the noise-like degree. CONSTITUTION:A color picture of a processing object is read by an external scanner and its quantized data is stored in a picture memory 101. A CPU 102 applies the processing for noise rejection and its pre-processing according to a processing program 105 stored in a program memory 103. In the processing for noise rejection, the CPU 102 obtains a function of a color difference with two areas and its interval as to the area with a prescribed width or below having a color in the color mixture relation with the two areas and clipped by the two areas close to each other having a prescribed width and applies color conversion with a probability in response to the value. A data memory 104 is used to stored the data on the way of processing and its processing result data.

Description

DETAILED DESCRIPTION OF THE INVENTION [Technical Field] The present invention relates to a method for removing noise in an image in devices that handle image information.

[Prior art]

Traditionally, image filtering is used to perform similar processing.
Noise in the image is removed.

However, there is a problem in that parameters need to be adjusted (tuned) depending on the attributes of the image. Furthermore, it is difficult to achieve both smoothing processing and sharpening processing, and when processing for removing granular noise (smoothing processing) is performed, there are problems such as blurring of the outline of the image.

Publicly known materials regarding this type of noise removal include ``Enhancement and Smoothing Process'', published by Nakamura February Monthly Magazine ``Oplus E J, Na75.February 1986 (Shintechi Co., Ltd.) Communication ), 8th
There are pages 3 to 97, etc.

〔the purpose〕

An object of the present invention is to provide a noise removal method that can effectively remove noise in a variety of color images, especially noise on contours, and sharpen contours without adjusting parameters for each image. It's about doing.

〔composition〕

The present invention is characterized in that the image contour is sharpened by performing probability processing on ambiguity in the image contour.

In other words, according to the present invention, in a color image, the degree of noise-likeness of the contour area is checked, and when this degree exceeds a certain limit, the color is not uniformly changed (area integration), but the degree of noise-likeness is checked. Probabilistic color change (region integration) is performed according to the degree of change.

More specifically, for an area a of a certain width or less that is sandwiched between two adjacent areas of a certain width or less and has a color in a mixed color relationship with the two areas, the area that has a smaller color difference with respect to the area a of the two areas. The value E=f determined by the color difference and distance between the two
(color difference, distance). This function f is a function in which the value E increases as the color difference increases and as the distance decreases. Then, by changing the color of area a to the color of the area (integration of area a into the area) with a probability according to the value E, ambiguous areas (noise) on the outline are removed and the outline is sharpened. to achieve

〔Example〕

An embodiment of the present invention will be described below with reference to one drawing.

FIG. 1 shows an example of an apparatus configuration for implementing the present invention. 101 is an image memory, 102 is a central processing unit (
103 is a program memory, and 104 is a data memory.

A color image to be processed is read by an external scanner, and its quantized data is stored in image memory 101. Regarding this color image, processing for noise removal and its preprocessing are executed by the central processing unit 102.
The processing program 105 is stored in the program memory 103. The rules for noise removal are as described below■.

There are three types, (1) and (2), which are incorporated into the processing program 105, but may also be made independent as a rule base.The data memory 104 is used to store data in the middle of processing and processing result data. .

Next, the overall flow of the process will be explained with reference to the flowchart shown in FIG.

First, for the color image in the image memory 101, a label is attached to each area of uniform color (step 201).Next, the color image after this labeling is recursively divided into four parts, thereby creating a color image quadtree structure. The description data is stored in the image description data section 10 in the data memory 104.
6 (step 202), the identification number of each area is provided as attached information to the leaves of the tree structure.

Note that since the processing up to step 5 is not new, detailed explanation will be omitted. Further, instead of the quadtree structure, a tree structure such as a binary tree may be used.

When an image is described in a tree structure in this way, not only can the amount of data be reduced, but also a topological spatial search for a region in the image can be executed at high speed in subsequent processing.

Next, by referring to the image description data, table 1 shown in FIG. 3 and table 2 shown in FIG. 4 are created on storage units 107 and 108 in data memory 104, respectively (step 203).

Table 1 stores, for each labeled area, area identification number, color identification number, area (number of pixels), and area circumscribing rectangle information.

FIG. 5 is an explanatory diagram of this circumscribed rectangle information, where 501 is one labeled uniform color area, and the position 1jll (minx) of the diagonal vertex of the circumscribed rectangle (broken line).

1hiny) and (maxx, maxy) are stored in table 1 as circumscribed rectangle information.

When performing a topological spatial search for an image region, by limiting the range of the region to be searched using this circumscribed rectangle information, efficient search becomes possible.

Table 2 stores the three color data of R (red), G (green), and B (blue) corresponding to each color identification number above, and also Then, the color corresponding to the color identification number is converted into LIa*b umbrella space, which is a uniform color space, and its Lm value, a umbrella value, and b middle value are stored. In this way, by using the I, *a*b umbrella color system, which is a uniform color space, it becomes possible to perform noise determination that matches human visual characteristics.

Steps 204 to 206 refer to the image description data and Tables 1 and 2 to determine Rules I and II.

This is the processing part that performs noise removal according to (2).

At step 204, a region of interest a is set, and at step 205
Then, rules I, II, and III are sequentially applied to remove noise. Then, in step 206, it is determined whether all areas have been set as attention areas, and if there are any unset areas remaining, the process returns to step 204 to set the next attention area and perform processing.

In this embodiment, "ground dust" and "edge degree" are used as the degree of noise-likeness. Background dust is determined by the density and area of the area of interest, and edge degree is determined by the distance and color difference between the two areas of interest.
Each is defined as follows.

Background dust = COMB (wl ・D, w2 ・S)
(1) Edge degree = COMB (w3 ・L, w4
・E) (2) Here, C= COM B (
a, b) are defined as follows.

■ If a=1 or b=1 then C=1 ■ If a > O and b>0 then C= a + b −
a b■ If ab≦0 and a~±1 and b~±1 then C
= a + b ■ If a < O and b < o, then C = a + b +
a b ■ If a = -1 or b = -1, then C = -1 In equations (1) and (2), wi is the weighting coefficient, D is the concentration level, S is the area, L is the distance, and E is It's the color difference.

D, S, L, and E are expressed as values from -1 to +1 depending on their degree. In other words, both the background dust and edge degree are values from -1 to +1, and the density level is low (close to white).
, the larger the area, the higher the background dust, and the smaller the distance and the larger the color difference, the higher the edge degree. Note that the width value of the smaller of the widths of the two regions is substituted for L, and the Lm value is substituted for D.

Furthermore, E is the color difference in the L*B*b umbrella color system.

Next, the process of step 205 will be explained for each applied rule.

First, processing according to Rule I is performed. Rule (2) is an integration rule between adjacent areas, and this process mainly removes noise caused by color density unevenness. FIG. 6 is a flowchart.

The tree structure is searched for the region set B = (bilbi is the region adjacent to a) adjacent to the region of interest a (step 6
01), the degree of edge between the region of interest a and one adjacent region b1 is measured (step 602).

A comparison is made between the area of the attention area a and the area of the adjacent area bi (step 603).

When the area of the attention area a is larger than the area of the adjacent area bi, the adjacent area bi is integrated into the attention area a; otherwise, the attention area a is integrated into the adjacent area bi (step 6
04,605).

This integration is not performed uniformly, but with a probability that depends on the degree of edgeness.As shown in Figure 7, this integration probability is 0% when the degree of edgeness is above a certain value, and when the degree of edgeness is less than that value. The smaller the bigger.

It is assumed that the edge degree is 100% at -1. In other words, when the integration probability according to the edge degree is 100%, integration is always performed, when the integration probability is 0%, integration is not performed, and when the integration probability is 50%, integration is performed once every two times on average. Stochastic integration processing is performed according to the degree of edge, such as integration based on proportions.

In this way, in order to stochastically integrate ambiguous noise regions, unlike the method of uniformly integrating regions when the edge degree exceeds a fixed threshold, the noise removal processing parameters are adjusted for each image. Good noise removal is possible for a variety of images without any adjustment (tuning).

After the above processing for the region of interest a and one adjacent region bi is performed, it is determined whether the processing has ended for all regions bi in the region set B (step 606), and if there is any remaining region bi, the processing starts from step 602. resume.

One image description data and table 1 are updated for the combined areas through such processing.

Note that if the region integration processing according to this rule (2) is executed without considering the size of the area of the region of interest, the integration may proceed mainly in regions with small areas. Generally, a region with a small surface area contains uncertain information, so more accurate integration can be achieved by starting from a region with a large surface area and proceeding with the integration centering on that region. Therefore, it is preferable to sort all regions in order of area, set regions of interest in descending order of area, and proceed with the integration process. However, there is a problem in that the sorting process requires a very long processing time.

An example of a processing method for obtaining the same effect as sorting without performing this sorting is shown in FIG. 8. Step 801 corresponds to steps 201 to 20 in FIG.
3, and steps 804 and 807 are the same processing steps as steps 204 and 206 in FIG. Step 806 is a processing step similar to steps 601 to 606 in FIG.

Step 802 is a step of initializing the counter n, and step 808 is a step of comparing and determining the value of the counter n with the maximum value.

Step 805 is a comparison judgment between the area of the attention area a set in step 804 and 81 and S2, and only the attention area that satisfies this determination condition becomes the processing target of step 806.

In this N, Sl and S2 are values that change depending on the value of n. For example, when n=1, 5l=200. S2=infinity When n=2, 5l=lOO, 52=200 When n=3, 5L=1
0. When 52=100n=4, Sl,=O,52=l
It is O.

After the noise removal process according to rule I as described above, the noise removal process according to rule (2) is executed. This rule (2) is an integration rule for a small area included in a large area, and this process performs noise removal assuming white noise, halftone dots, and color toner adhesion noise in electrophotography. FIG. 9 is a flowchart of this process. First, it is checked whether the area of the attention area a is equal to or larger than the threshold value Th1 (step 901), and if the area is equal to or greater than Th1, the area set B included in the attention area a= (bi l b
i is the area included in a) on the tree structure (
Step 902).

This region set B is clustered into regions Ci with small color differences (step 903). The areas Ci are a subset of B, each consisting of one or more areas bi in a tree structure, and do not overlap with each other.

That is, B=CIUC2U...CkU...

Each region Ci is defined as the color difference with respect to the region of interest a, the area and "
The degree of aggregation is classified into categories 1.2.3 (step 904).
It is the value obtained by dividing the number i) by the area of region a.

Category 1 is assumed to be white noise,
Areas where the color difference is fairly small and the area is not large are classified into this category. Category 2 assumes halftone dots,
Regions with medium color difference, small area, and medium aggregation degree are classified into this category. Category 3 is assumed to be the adhesion noise of color toner, and an area where the color difference is quite large, the area is small, and the aggregation degree is quite small tJs is classified into this category. Regarding category 3, furthermore, for example, Y
(yellow) 1M (magenta), C (cyan), BK (black)
You may also add a color condition that the ink color is the same.

Areas Ci that are not classified into any of the above categories are not noise (they are valid information) and are therefore excluded from area integration targets.

At least one Chi in B is in category 1゜2.3
If the area of interest is classified as one of the categories, step 906), the degree of texture of the area of interest a is measured, and the area bi within the area Ci classified as category 1, 2, or 3 is set as the area of interest. Stochastic integration is performed in area a (step 907).

The integrated probability for this area is shown in Figure 10.

When the background texture is below a certain value, it is 0%, but when the background texture is above a certain value, it increases as the background texture increases.

Such area integration can mainly remove the above-mentioned granular noise. Furthermore, since area integration is not performed in a regular manner, but stochastic integration is performed according to the degree of texture, it is possible to remove noise from a variety of images without having to tune parameters for each image.

Next, processing is performed according to rule 3. Rule 3 is a rule for integrating ambiguous areas (noise) in the outline of the image,
This processing makes it possible to sharpen the outline. 1st
Figure 1 is a flowchart of this process.

First, check whether the width of the attention area a is smaller than the threshold Th2 (
In step 1001), if the width is equal to or greater than Th2, the region of interest a is not an outline region and is therefore excluded from processing.

For the region of interest a whose width is smaller than Th2, a nearby region bi (not necessarily adjacent to the region of interest a) within a certain range is searched on the tree structure (step 1002).

In this proximal area, select two areas bl and b2 that are in a positional relationship sandwiching the attention area a from both sides (does not need to be in contact with a) and have a width of Th3 or more (Step 1
003). If such areas bl and b2 cannot be selected, the process ends.

Next, it is determined whether the color of the attention area a has a color mixing relationship with the colors of the areas bl and b2 (step 1004), and if there is no color mixing relationship, the process ends.

If it is a color mixture relationship, the attention area a is selected from the areas bl and b2.
Let the area with the smaller color difference be bl, the other b2,
The color differences ΔEl and ΔE2 with respect to each region of interest a are determined (step 1005).

A comparative judgment is made between ΔE1 and Δ2 (step 1006),
If ΔE1≧ΔE2, the process ends.

If ΔE1<ΔE2, the degree of edge between the region of interest a and the region b1 is measured (step 1007).

Then, the attention area a is determined with probability according to the degree of edge.
The color is changed to the color of area b1 (step 1008).
. If the open areas are in contact, this color change is the integration of the areas themselves, but if they are separated, it is the area integration of only the color planes.

As shown in FIG. 12, the probability of this color change (area integration) increases as the edge degree increases.

By stochastically changing the area in this way, it is possible to remove ambiguous areas (noise) in the image outline, and instead of randomly changing the color (integration), we can change the probability according to the degree of edgeness. Since the colors are changed using , good results can be obtained for a variety of images without having to tune parameters for each image.

Through the above processing, the quadtree-structured image description data and table 1 are updated, and these are obtained as processing results in the respective storage units 106 and 107 of the data memory 104.

Note that the rules and algorithms for noise removal, the noise-likeness index, the configuration of the processing device, etc. can be changed as necessary.

〔effect〕

As is clear from the above description, according to the present invention, by changing the color of area a to area 2 (area integration) with a probability according to the edge degree of areas a and b of one contour, It is possible to effectively remove ambiguous areas (noise) of outlines in various color images and sharpen one outline without having to perform parameter tuning.

[Brief explanation of the drawing]

FIG. 1 is a block diagram showing an example of a device configuration for carrying out the present invention, FIG. 2 is a flowchart of the entire process, FIG. 3 is an explanatory diagram of table 1, FIG. 4 is an explanatory diagram of table 2, Fifth
The figure is an explanatory diagram of circumscribed rectangle information, Figure 6 is a flowchart of processing according to rule 1, Figure 7 is a diagram of the relationship between edge degree and integrated probability, and Figure 8 is a flowchart showing a modified example of processing related to rule 1.
Figure 9 is a flowchart of the processing according to Rule 2, Figure 10 is a diagram of the relationship between background degree and integrated probability, Figure 11 is a flowchart of processing according to Rule 3, and Figure 12 is a diagram of the relationship between edge degree and color change probability. . 101... Image memory, 102... Central processing unit, 103... Program memory, 104... Data memory. Fig. 2 Fig. 3 (Chee 7" 1 le Fig. 4 Fig. 6 Fig. 1 O Fig. + S Color Moon a, cliff 2 Fig. 12 → cross

Claims (1)

[Claims] (1) In a color image, for an area a of a certain width or less that is sandwiched between two adjacent areas of a certain width or less and has a color in a mixed color relationship with the two areas, the one of the two areas that has a smaller color difference with respect to the area a Measure the value E = f (color difference, distance) determined by the color difference and distance from area b of A color image noise removal method characterized by changing the color of area a to the color of area b with probability.