JPH01170272A - Picture signal processing method - Google Patents

Abstract

PURPOSE:To improve the picture quality after binarization processing of a picture by obtaining a histogram of a picture signal with respect to density and obtaining a background noise cut level from the histogram. CONSTITUTION:The background noise cut level close to the background level is used to slice a picture signal in preceding over the blurring correction processing and to eliminate noise in the background level, then the background noise cut level is set to e density level being the result of addition or subtraction of a prescribed density level to/from the density level of the picture signal among the density levels having the frequency of nearly 1/40 of the total picture element number N of the image sensor. Thus, the background noise cut level is set to an always proper level even when the picture image is changed and the binarization processing with high picture quality is attained.

Description

Detailed Description of the Invention (Field of Industrial Application) The present invention relates to an image signal processing method for binarizing an image signal obtained by scanning an image with an image sensor after performing blur correction processing. be.

(Technical Background of the Invention) Various methods have been proposed in the past for reading out an image using an image sensor such as a CCD line sensor or a CCD area sensor and converting this image signal V) into a binary value. For example, the applicant of the present application proposed a method in which after correcting the blur of an image signal, it is compared with a fixed binarization level (Japanese Patent Laid-Open No. 62-13006
(See No. 8), and in this case, the binarization level is obtained by subjecting the image signal to smoothing, compression, and level shift processing, so that the binarization level floats according to the image signal. A method was also proposed.

Here, blur correction is a method of emphasizing the edges of an image by emphasizing the high frequency components of the image. ,
(hereinafter referred to as US mask) is used. However, since such high-frequency enhancement filters essentially use differentiation, they are susceptible to noise, and in particular, noise in the background area of the image signal is also emphasized at the same time, which significantly deteriorates the image quality after binarization processing. There was a problem of lowering the

Therefore, the applicant of the present application has considered slicing the image signal at a predetermined background noise cut level close to the background level to remove the noise contained in the background. However, in this case, since the background density level differs depending on the image, the problem is how to determine the background noise cut level. In other words, if this setting is incorrect, background noise may not be removed sufficiently, or the effective signal level fluctuation range (dynamic range) of the image signal will be narrowed, resulting in a reduction in image quality. A problem arises.

(Purpose of the Invention) The present invention has been made in view of the above circumstances, and provides a method for deblurring an image signal by emphasizing high frequencies prior to binarization processing using a background noise cut level close to the background level. Provides an image signal processing method that can always appropriately set the background noise cut level when slicing an image signal to remove background noise, and that can improve the image quality after binarizing the image. The purpose is to

(Structure of the Invention) According to the present invention, this purpose is to remove background noise by slicing an image signal obtained by scanning an image with an image sensor using a background noise cut level close to the background level. In an image signal processing method that sharpens an image signal by performing blur correction and further binarizes it at a predetermined binarization level, a histogram for the density of the image signal is obtained, and this histogram is calculated based on the total number of pixels of the image sensor. According to the image signal processing method, the density level obtained by adding or subtracting a certain density level from the density level on the density level side including the image among the density levels having a frequency of about 1/40 is set as the background noise cut level. achieved.

(Example) Fig. 1 is a block diagram of an embodiment of the present invention, Fig. 2 is a conceptual diagram of a part of the specific example, Figs. 3A to 30 are diagrams showing signal waveforms in the processing process, and Fig. 4 5 is a diagram showing a 3×3 matrix, and FIG. 5 is a diagram showing a histogram for a negative film.

In FIG. 1, reference numeral 10 is a light source, and the light from this light source IO is guided to an image sensor 20 via a condenser lens 12, a film 14, a projection lens 16, and a mirror 18, and the projected image of the film 14 is sent to the image sensor 20. Form an image. The image sensor 20 is a CCD line sensor or CC
D formed by area sensor etc., pulse circuit (not shown)
It scans the image and outputs a time-series image signal a). This image signal a has an output waveform as shown in FIG. 3A when the film 14 is negative. In FIGS. 3A to 3C, the horizontal axis indicates time or pixel order, and the vertical axis indicates voltage, that is, output (density) level.

The image signal a for one scanning line or one screen is temporarily stored in a semiconductor memory 22 such as a line memory or a frame memory. This image signal a is input to a histogram means 24 constituted by a CPU (not shown), where a histogram A for density is determined as shown in FIG.

The CPU determines the background noise cut level b from this histogram A using the background noise cut level detecting means 26 that the CPU itself has.

Histogram A shows the output (density) level of image signal a on the horizontal axis and the frequency H on the vertical axis.
4, a mountain B corresponding to the background appears on the low output level side.

CPtJ determines the background noise cut level b on the right side of the peak B of this histogram A, that is, near the base on the high output (density) level side.

This level b is determined as follows. In other words, when the total number of pixels of the image sensor 20 is N, among the output levels with a frequency of approximately N/40, the density level α of the force point C near the image No. 111 - is given by a constant density level β Add α+β to determine the F ground noise cut level b0 For example, if a 4000 pixel line sensor is used and the density level is 64
If it can be set in stages (equivalent to 6 bits),
Find the density level α at point C where the frequency is about 100, and add two density levels to this to obtain the base cut level b.
It can be done.

The image signal a and the background noise cut level b are input to the background noise cut circuit 28, where the noise included in the background area is removed. That is, this circuit 28
For example, as shown in FIG.
The comparator 30 compares the image signal a and the background cut level b. Also, the switch 32 is connected to this comparator 3.
When 0 determines that a<b, background cut level b is selected, and when a≧b, image signal a is selected. As a result, the output C of this circuit 28 is sliced at the background noise cut level b, and the noise in the background area is cut, resulting in a signal as shown in FIG. 3B. In this way, the background cut level b is set close to and slightly higher than the background level d (FIG. 3A).

Reference numeral 34 denotes a blur correction circuit, which emphasizes the high frequency components of the signal C using, for example, a US mask 36 as a high frequency emphasis filter, thereby emphasizing the edges of the image.

This US mask 36 functions to emphasize the central pixel, for example, when the image space is a δ number matrix in which the central pixel appears. For this US mask 36, for example, when each pixel data of a 3×3 matrix is ai as shown in FIG. -(b+d+h+f) and use this E as a new image signal. In this case U
The S mask 36 is set as shown in FIG. 2, and each element of this matrix is integrated into the image data of four surrounding pixels around the center pixel, and the sum E of the integrated values is determined. The image signal e emphasized in this way is sent to the comparator 3.
At step 8, the signal is compared with the binarized level f, and the binarized signal g is obtained at step 11).

In this way, prior to blur correction processing by high-frequency emphasis,
Since background noise is removed by the background noise cut level b, fluctuations due to fine noise in the image signal, especially in the background area, are not excessively magnified during blur correction processing. Therefore, it is not affected by background noise.

Furthermore, since the background noise cut level b is determined based on the histogram A, an appropriate level b that is close to the background level can always be determined even if the image changes.

Therefore, there will be no inconvenience that this level b is too low and noise cannot be removed completely, or that this level b is too high that the output width (dynamic range) of the image signal a cannot be used effectively and the image quality deteriorates. .

FIG. 6 is a block diagram of another embodiment, and FIGS. 7A to 7D are output waveform diagrams of each part.

In this embodiment, the binarization level is varied using the image signal a. That is, the image signal a is smoothed in the smoothing circuit 50 to form a smoothed signal h (FIG. 7A), and this smoothed signal is compressed in the compression circuit 52.

The voltage level of this compressed signal i (FIG. 7n) is further increased by k in a level shift circuit 54. This level-shifted signal j (FIG. 7C) is compared with the signal d, which has already undergone blur correction, in a comparator 38, and a binarized level is obtained.

Note that as the smoothing circuit 50 of this embodiment, for example, a median filter can be used that uses the median of a 3×3 matrix centered on the center pixel as the image data of the center pixel.

According to this embodiment, since the binarization level j itself varies depending on the image signal a, it is suitable for highly accurate binarization processing of images with low contrast.

Although each of the above embodiments uses the US mask 36 in the blur correction circuit, the present invention is not limited to this.

In addition, the background noise cut level b (= α + β) is set slightly higher than the background d when using a negative film image, as in the city record example, but it is set slightly higher than the background noise cut level b (= α + β) when using a positive film. Of course, set it slightly lower than the background, and in this case, subtract a constant density β from the density level α to the left side of the histogram mountain, that is, the base of the low output (low density) level side (α− β).

(Effect of Issue 11) As described above, the present invention uses a background noise cut level close to the background level to slice the image signal prior to the blur correction 1F processing and remove the noise at the background level. , the background noise cut level is set to a density level that is obtained by adding or subtracting a certain density level from the density level on the signal side of the image among the density levels that have a frequency of approximately 1/40 of the total number of pixels N of the image sensor. Therefore, even if the image changes, the background noise cut level can always be set to an appropriate level.

Therefore, in the binarization process after blur correction, this ground noise cut level may be too close to the background and noise cannot be removed sufficiently, or conversely, it may be too far from the background level and the dynamic range of the image signal may be sacrificed. This enables high-quality binarization processing without causing inconveniences such as .

[Brief explanation of the drawing]

FIG. 1 is a block diagram of an embodiment of the present invention, FIG. 2 is a conceptual diagram of a specific example, FIGS. 3A to 30 are diagrams showing signal waveforms in the processing process, and FIG. FIG. 5 is a diagram showing a histogram, FIG. 6 is a block diagram of another embodiment, and FIGS. 7A to 7D are output waveform diagrams of each part. 20... Image sensor, 24... Histogram means, 28... Base noise cut circuit, a... Image signal, b... Base noise cut level, d... Background level, f, j ...-value level, A... histogram. Patent applicant: Fuji Photo Film Co., Ltd. Representative: Patent attorney Yama 1) Yu Fumi (1 other person) Figure 1 Figure 2 Figure 4 Figure 3A Figure 5

Claims (6)

[Claims] (1) After removing the background noise by slicing the image signal obtained by scanning the image with an image sensor using a background noise cut level close to the background level, the image signal is sharpened by performing blur correction,
Furthermore, in the image signal processing method of binarizing at a predetermined binarization level, a histogram for the density of the image signal is obtained, and this histogram is a density level that has a frequency of about 1/40 of the total number of pixels of the image sensor. An image signal processing method characterized in that a density level obtained by adding or subtracting a certain density level from a density level on a density level side including an image is set as the background noise cut level. (2) The image signal processing method according to claim 1, wherein the blur correction is performed using an unsharp mask. (3) The binarization level is determined by smoothing the image signal,
Claim 1 characterized in that the smoothed signal is obtained by compressing the smoothed signal and further level-shifting the smoothed signal.
Image signal processing method described in section. (4) The image signal processing method according to claim 3, wherein the smoothing is performed using a median filter. (5) The image is a projected image obtained from a negative film, and the background noise cut level is set near the foot of a high-density level side of a peak of a histogram with respect to the background. 1st
Image signal processing method described in section. (6) The image is a projection image obtained with a positive film, and the background noise cut level is set near the foot of a low density level side of the histogram peak with respect to the background. The image signal processing method according to item 1.