Image processing apparatus

In an image processing apparatus, images on an original document captured by an image capturing device are classified into a binary image and a gradation image, and the densities of the images are then converted using density conversion curves, which are provided for binary images and gradation images, to output an image signal representing converted images. The state of a certain pixel is judged and is classified as one of a plurality of states between binary images and gradation images. Based on the results of the judgment, one curve is selected from a first density conversion curve for binary images, a second density conversion curve for gradation images and at least one third density conversion curve which interpolates the first and second density conversion curves. The density of the certain pixel is converted using the selected curve. The image processing apparatus can provide a high contrast in a binary image area of an original document and maintain the gradation of an image in a gradation image area of the original document. Also, the apparatus can output an image density signal which does not cause an unnatural vision due to an abrupt change in density.

BACKGROUND OF THE INVENTION 
1. Field of the Invention 
The present invention relates to an image processing apparatus in which an 
image is captured from an original document to obtain digital image data 
and the image data is then subjected to image processing to obtain image 
data suitably for the type of the apparatus. Examples of such an image 
processing apparatus include a digital printing press in which 
perforations are formed in a thermosensitive perforation master sheet in 
accordance with processed image data, a digital copying apparatus in which 
a latent image is formed on a light-sensitive material by 
electrophotography in accordance with processed image data and is 
transferred to a sheet of paper, and an apparatus in which an image is 
copied onto thermosensitive paper after image processing. In particular, 
the present invention relates to an image processing apparatus in which a 
captured image is converted to binary data to output binary information 
representing the captured image. 
2. Description of the Related Art 
When an original document containing both a completely black-and-white 
image such as a character or a line (hereinafter referred to as a "binary 
image") and an image having gradation (hereinafter referred to as a 
"gradation image") is processed by the above-described image processing 
apparatus, the best results can be obtained by classifying the density of 
the binary image, using a single threshold, into a maximum density and a 
minimum density and by carrying out density conversion for the gradation 
image area to maintain the consistency between the density of an input 
image and the density of an output image corresponding to the input image, 
taking into account the characteristics of input and output devices. 
Therefore, it is necessary to make a judgment as to whether each small 
area of an image contains a binary image or a gradation image so as to 
process the areas differently. 
Conventionally, an image is divided into pixel blocks of n.times.n, and the 
characteristics of each block are extracted. By using the results of the 
characteristic extraction, it is judged whether each block is a binary 
image area (an area containing a binary image) or a gradation image area 
(an area containing a gradation image) (Japanese Patent Application 
Laid-Open (kokai) No. 3-153167). Hereinafter, this judgment may be 
referred to as "area judgment". Alternatively, the characteristics of a 
certain pixel are extracted by using the certain pixel and its peripheral 
pixels, and the area judgment for each pixel is performed based on the 
extracted characteristics (Japanese Patent Application Laid-Open (kokai) 
No. 1-227573). 
In the former method, since the area judgment is performed for each block, 
rectangular shapes corresponding to the blocks are formed at portions 
where the area judgment is erroneously performed or at portions 
corresponding to the boundary between a binary image area and a gradation 
image area. In the latter method, the influence of an erroneous judgment 
is small. However, a considerable difference in density is produced 
between a portion in which the area judgment is erroneously performed and 
a portion in which the area judgment is properly performed, resulting in 
an unnatural vision. 
Also, it is difficult to discriminate a thick line and a black solid 
portion in a binary image area from a high density portion of a picture in 
a gradation image area. When parameters for area judgment are adjusted 
such that the thick line and the black solid portion are judged as binary 
images, some portions of a picture image are destroyed. When the 
parameters for area judgment are adjusted such that the high density 
portion of the picture image is judged as a gradation image, the densities 
of the thick line and the black solid portion decrease. 
SUMMARY OF THE INVENTION 
An object of the present invention is to solve the above-described problems 
of conventional image processing apparatus and to provide an improved 
image processing apparatus which can provide a high contrast in a binary 
image area of an original document and can maintain the gradation of an 
image in a gradation image area of the original document, and which can 
output an image density signal which does not cause an unnatural vision 
due to an abrupt change in density. 
The present invention provides an image processing apparatus in which 
images on an original document captured by an image capturing means are 
classified into a binary image and a gradation image, and the densities of 
the images are then converted using density conversion curves, which are 
provided for binary images and gradation images, to output an image signal 
representing converted images. The image processing apparatus includes 
means for judging the state of a certain pixel and classifying it as one 
of a plurality of states between binary images and gradation images, and 
means for selecting, based on the results of the judgment, one curve from 
a first density conversion curve for binary images, a second density 
conversion curve for gradation images and at least one third density 
conversion curve which interpolates the first and second density 
conversion curves to convert the density of the certain pixel. 
The means for detecting the state of a certain pixel can be realized in 
various ways as follows. 
(1) The sharpness of the edge of a certain pixel is detected based on the 
densities of pixels adjacent to the certain pixel. 
(2) It is judged based on the densities of pixels adjacent to a certain 
pixel whether the certain pixel is an edge pixel, and the distance between 
a certain pixel and the edge pixel closest to the certain pixel is 
calculated. 
(3) A judgment as to whether a certain pixel forms a thin line and a 
judgment as to whether the certain pixel is an edge pixel are performed 
based on the characteristics of each pixel which are obtained from the 
sharpness of the edge of the certain pixel, the distance between the 
certain pixel and the edge pixel, and the thickness of a line including 
the certain pixel. 
In the present invention, when a segment exists wherein a certain pixel is 
located between a rising edge and a falling edge in density in a main 
scanning direction or an auxiliary scanning direction, the length of the 
segment is calculated. The shorter the segment, the larger the probability 
that the segment forms a part of a character. In the present invention, 
the distance between a certain pixel and an edge pixel closest to the 
certain pixel is calculated. The shorter the distance, the larger the 
probability that the certain pixel forms an image of a character. The 
longer the distance, the larger the possibility that the certain pixel 
forms a gradation image. 
In the present invention, not only a first density conversion curve for 
binary images and a second density conversion curve for gradation images 
but also a suitable number of third density conversion curves which 
interpolate the first and second density conversion curves are provided. 
Based on few characteristic values which are predicted for each pixel in 
the above-described manner, the density conversion curves are selectively 
used. When the characteristic values indicate a strong likelihood that a 
detected image is a binary image, the first density conversion curve for 
binary images is selected. When the characteristic values indicate a 
strong likelihood that the detected image is a gradation image, the second 
density conversion curve for gradation images is selected. When it is 
judged that the detected image is neither a binary image nor a gradation 
image, one of the third density conversion curves which interpolate the 
first and second density conversion curves is selected based on the 
probability of being a binary image and the probability of being a 
gradation image. The density of the detected image is converted using the 
selected density conversion curve. With this operation, the density of the 
output image does not abruptly change even when the characteristic values 
indicate neither the possibility of being a binary image nor the 
possibility of being a gradation image, or when an area is erroneously 
judged because of characteristic values which only represent the 
characteristics of an image in a narrow area. 
Also, by selecting a density conversion curve closer to the first density 
conversion curve for binary images when approaching the edge and selecting 
a density conversion curve closer to the second density conversion curve 
for gradation images when separating from the edge, thick characters and 
black solid portions can be made completely black, while the gradation of 
a portion of a gradation image where the density is high can be 
maintained. 
In the image processing apparatus according to the present invention, 
abrupt changes in the density of an output image due to an erroneous 
judgment of an area can be reduced so that a viewer feels a minimized 
unnaturality. Even when an original document containing both character 
images such as characters and lines and gradation images such as pictures, 
an image density signal which is highly consistent with the original 
document can be output. 
Also, the image processing apparatus according to the present invention 
stores density conversion information representing a plurality of density 
conversion curves which interpolate the first density conversion curve for 
binary images and the second density conversion curve for gradation 
images. Accordingly, thick characters and black solid portions can be 
reproduced as being completely black in appearance, because the thick 
characters and black solid portions can have high densities at their 
edges. Since the density of each gradation image is controlled to 
gradually have gradation away from the edges, the gradation of a high 
density portion of the gradation image can be maintained.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS 
Embodiments of the present invention will now be described in more detail 
with reference to the drawings. 
First Embodiment: 
FIG. 1 is a block diagram of an image processing apparatus according to a 
first embodiment of the present invention. Light is irradiated on an 
original document, and light reflected by the original document is 
detected by an unillustrated line sensor, such as a CCD, which serves as 
an image capturing means. The detected light is converted to an electric 
signal (image density signal) and the converted electric signal is output. 
An image density signal d.sub.i,j representing the density of an image 
captured by the image capturing means is output from the image capturing 
means. 
The image density signal output from the image capturing means is directly 
input to a line memory 2, or input to the line memory 2 via an MTF 
compensation circuit 1 illustrated by a broken line. The image density 
signal output from the image capturing means undergoes a compensation 
process in the MTF compensation circuit 1 so that an image density signal 
which closely represents the original image is output from the MTF 
compensation circuit 1. 
When the MTF compensation circuit 1 is used, the densities of pixels in an 
image are multiplied by corresponding coefficients in an MTF compensation 
coefficient matrix shown in FIG. 2. The multiplied densities are summed by 
a convolution calculation to obtain a compensated image signal d'.sub.i,j, 
which is then input to the line memory 2. 
FIG. 3 shows the positions of pixels on an original document which 
correspond to image density signals stored in the line memory 2. p.sub.i,j 
represents a newest pixel corresponding to a newest image density signal 
output from the image capturing means. j is a pixel number in the main 
scanning direction while i is a pixel number in the auxiliary scanning 
direction. Therefore, p.sub.i,j is a pixel which is located at an i-th 
position in the auxiliary scanning direction and at a j-th position in the 
main scanning direction. The density of the pixel p.sub.i,j is represented 
as d.sub.i,j. The line memory 2 stores image density signals for pixels in 
a hatched area, wherein the number of the pixels is greater, by two, than 
the number of pixels in two lines extending in the main scanning 
direction. 
Image density signals (d.sub.i,j, d.sub.i,j-1, d.sub.i,j-2, d.sub.i-1,j, 
d.sub.i-1,j-1, d.sub.i-1,j-2, d.sub.i-2,j, d.sub.i-2,j-1, and 
d.sub.i-2,j-2) output from the line memory 2 form a matrix of 3.times.3 in 
which pixel p.sub.i-l,j-1 is centrally located. 
As shown in FIG. 1, image density signal d.sub.i-1,j- 5 output from the 
line memory 2 is input to a density conversion circuit 3. Image density 
signals (d.sub.i,j, d.sub.i,j-l, d.sub.i,j-2, d.sub.i-1,j, d.sub.i-1,j-1, 
d.sub.i-1,j-2, d.sub.i-2,j, d.sub.i-2,j-1, and d.sub.i-2,j-2) output from 
the line memory 2 are input to an edge detection circuit 27. The edge 
detection circuit 27 judges whether the density changes at a certain pixel 
with respect to the densities of pixels adjacent to the certain pixel. 
FIG. 4 shows the detail of the edge detection circuit 27. A calculation 
circuit 13 performs a convolution calculation using edge detection 
coefficient matrixes for lateral direction, longitudinal or vertical 
direction, and two oblique directions perpendicularly intersecting each 
other shown in FIGS. 5A, 5B, 5C and 5D. The largest one of the absolute 
values of four obtained values is output as an edge signal e.sub.i-l,j-l, 
and is input to comparators 14, 15 and 25. 
The comparator 15 outputs a first edge detection signal e1.sub.i-1,j-1. 
When the input edge signal e.sub.i-1,j-1 is equal to or greater than a 
first edge threshold T2, the first edge detection signal e1.sub.i-1,j-1 is 
a valid signal which indicates that the pixel is an edge pixel. When the 
input edge signal e.sub.i-1,j-1 is less than the first edge threshold T2, 
the first edge detection signal e1.sub.i-1,j-1 is an invalid signal which 
indicates that the pixel is not an edge pixel. The first edge threshold T2 
is determined to only detect large sharp edges (large changes in density) 
which hardly appear in gradation images. 
The comparator 14 outputs a second edge detection signal e2.sub.i-1,j-1. 
When the input edge signal e.sub.i-1,j-1 is equal to or greater than a 
second edge threshold T3, the second edge detection signal e2.sub.i-1,j-1 
is a valid signal. When the input edge signal e.sub.i-1,j-1 is less than 
the second edge threshold T3, the second edge detection signal 
e2.sub.i-1,j-1 is an invalid signal. The second edge threshold T3 is 
determined to be smaller than the first edge threshold T2 so as to detect 
intermediate sharp edges (slightly large changes in density). 
The comparator 25 outputs a third edge detection signal e3.sub.i-1,j-1. 
When the input edge signal e.sub.i-1,j-1 is equal to or greater than a 
third edge threshold T8, the third edge detection signal e3.sub.i-1,j-1 is 
a valid signal. When the input edge signal e.sub.i-1,j-1 is less than the 
third edge threshold T8, the third edge detection signal e3.sub.i-1,j-1 is 
an invalid signal. The third edge threshold T8 is set to be smaller than 
the second edge threshold T3 so as to detect small sharp edges (almost no 
changes in density). 
As shown in FIG. 1, the edge detection signals are input to dot delay 
circuits 8, 9 and 26 for matching with the timing of density conversion 
for image density signal d.sub.i-1,j-5 of a pixel to be processed. 
The first edge detection signal e1.sub.i-l,j-5, the second edge detection 
signal e2.sub.i-l,j-5 and the third edge detection signal e3.sub.i-l,j-5 
output from the dot delay circuits 8, 9 and 26 are input to a 
characteristic judging circuit 28. The characteristic judging circuit 28 
determines a density conversion selecting signal g.sub.i-l,j-5 based on 
the first edge detection signal e1.sub.i-l,j-5, the second edge detection 
signal e2.sub.i-l,j-5 and the third edge detection signal e3.sub.i-1,j-5, 
in accordance with rules shown in FIG. 6. 
The density conversion selecting signal g.sub.i-1,j-5 is determined such 
that a density conversion curve is selected taking account of the 
following. 
1. Edge pixels mainly exist in binary image areas. 
2. As the sharpness of an edge (change in the density) becomes larger, the 
probability of being a binary image increases, and as the sharpness 
(change in the density) becomes smaller, the probability of being a 
gradation image increases. 
In the table shown in FIG. 6, for a signal taking one of two values, symbol 
o indicates that the signal is valid, symbol x indicates that the signal 
is invalid, and symbol-indicates that the signal is ignored. For a signal 
taking one of three or more values, a numeral represents the value of the 
signal. The value of the density conversion selecting signal g.sub.i-1,j-5 
has the following meaning: 
when g.sub.i-1,j-5 =0, a binary image, 
when g.sub.i-1,j-5 =3, a gradation image, 
when g.sub.i-1,j-5 =1, probably a binary image, and 
when g.sub.i-1,j-5 =2, probably a gradation image. 
The density conversion circuit 3 includes four density conversion curves 
shown in FIG. 7, i.e., a density conversion curve A (solid line) for 
binary images which converts an input density signal to a binary signal 
i.e., to the maximum or minimum value, a density conversion curve B 
(alternate long and short dash line) for gradation images which closely 
copies the gradation of an input density signal to an output density 
signal, a density conversion curve (C) (broken line) close to the density 
conversion curve (A) for binary images, and a density conversion curve (D) 
(alternate long and two short dashes line) close to the density conversion 
curve (B) for gradation images. The density conversion curves (C) and (D) 
interpolate the density conversion curves (A) and (B). 
Based on the value of the density conversion selecting signal g.sub.i-1,j-5 
input from the characteristic judging circuit 28, a density conversion 
curve is selected in accordance with the following rules: 
when g.sub.i-1,j-5 =0, density conversion curve A for binary image, 
when g.sub.i-1,j-5 =3, density conversion curve B for gradation image, 
when g.sub.i-1,j-5 =1, density conversion curve C which interpolates the 
density conversion curves A and B; and 
when g.sub.i-1,j-5 =2, density conversion curve D which interpolates the 
density conversion curves A and B. The image density signal d.sub.i-1,j-5 
is subjected to density conversion using a selected density conversion 
curve to output a converted density signal cd.sub.i-1,j-5. 
Actually, the density conversion circuit 3 has four data conversion tables 
corresponding to the above-described four density conversion curves. The 
data conversion tables are selectively used based on the value of the 
density conversion selecting signal g.sub.i-1,j-5 input from the 
characteristic judging circuit 28, and the image density signal 
d.sub.i-1,j-5 is converted to the density signal cd.sub.i-1,j-5 with 
reference to a selected data conversion table. 
The converted density signal cd.sub.i-1,j-5 output from the density 
conversion circuit 3 is input to a binary signal producing circuit 4. The 
binary signal producing circuit 4 converts the converted density signal 
cd.sub.i-1,j-5 to a binary signal by an error diffusion method. 
In the resent embodiment, coefficient matrixes of 3.times.3 are used for 
the edge detection. The accuracy of the edge detection can be increased by 
changing the size of the matrixes to n.times.m (n&gt;0, m&gt;0, n and m are 
integers), changing the coefficients, or increasing the number of the 
matrixes. 
Although two additional density conversion curves which interpolate the 
density conversion curve for binary images and the density conversion 
curve for gradation images are used in the present embodiment, the number 
of the additional density conversion curves can be reduced from two to 
one. Also, the number of classes into which the sharpness of an edge is 
classified may be increased by adding comparators having different 
thresholds to the edge detection circuit 27, thereby providing three or 
more density conversion curves which interpolate the density conversion 
curve for binary images and the density conversion curve for gradation 
images. 
Second Embodiment: 
Next, an image processing apparatus according to a second embodiment will 
be described with reference to FIG. 8. 
Like the first embodiment, image density signals (d.sub.i,.sub.j, 
d.sub.i,j-1, d.sub.i,j-2, d.sub.i-1,j, d.sub.i-1, j-1, d.sub.i-1,j-2, 
d.sub.i-2,j, d.sub.i-2,j-1, and d.sub.i-2,j-2) output from the line memory 
2 form a matrix of 3.times.3 in which pixel p.sub.i-1,j-1 is centrally 
located. 
As shown in FIG. 8, image density signal d.sub.i-1,j-5 output from the line 
memory 2 is input to a density conversion circuit 3. Image density signals 
(d.sub.i,j, d.sub.i,j-1, d.sub.i,j-2, d.sub.i-1,j, d.sub.i-1,j-1, 
d.sub.i-1,j-2, d.sub.i-2,j, d.sub.i-2,j-1, and d.sub.i-2,j-2) output from 
the line memory 2 are input to an edge detection circuit 29. 
FIG. 9 shows the detail of the edge detection circuit 29. A calculation 
circuit 13 performs a convolution calculation using edge detection 
coefficient matrixes for lateral direction, longitudinal or vertical 
direction, and two oblique directions perpendicularly intersecting each 
other shown in FIGS. 5A, 5B, 5C and 5D, like the first embodiment. The 
largest one of the absolute values of four obtained values is output as an 
edge signal e.sub.i-1,j-1, and is input to a comparator 15. 
The comparator 15 outputs a first edge detection signal e1.sub.i-1,j-1. 
When the input edge signal e.sub.i-1,j-1 is equal to or greater than a 
first edge threshold T2, the first edge detection signal e1.sub.i-1,j-1 is 
a valid signal which indicates that the pixel is an edge pixel. When the 
input edge signal e.sub.i-1,j-1 is less than the first edge threshold T2, 
the first edge detection signal e1.sub.i-1,j-1 is an invalid signal which 
indicates that the pixel is not an edge pixel. The first edge threshold T2 
is determined to only detect large sharp edges (large changes in density) 
which hardly appear in gradation images. 
As shown in FIG. 8, the first edge detection signal e1.sub.i-1,j-5 output 
from the dot delay circuit 9 is input to a characteristic judging circuit 
30 and a distance classifying circuit 11. The distance classifying circuit 
11 calculates the distance between a certain pixel and an edge pixel and 
classifies the distance. FIG. 10 shows the detail of the distance 
classifying circuit 11. The line memory 23 stores distance data 
representing the distance between each pixel in a single line and an edge 
pixel. 
FIG. 16 shows the positions of the pixels on an original document which 
correspond to the distances between the pixels stored in the line memory 
and the corresponding edge pixels. The first edge detection signal 
e1.sub.i-1,j-5 is input to a distance counting circuit 22 in which the 
distance de.sub.i-1,j-5 between the pixel p.sub.i-1,j-5 and the 
corresponding edge is calculated based on the distance de.sub.i-1,j-6 
between the left-hand pixel p.sub.i-1,j-6 and the corresponding edge, the 
distance de.sub.i-2,j-5 between the upper pixel p.sub.i-2,j-5 and the 
corresponding edge, and the distance de.sub.i-2,j-4 between the 
right-upper pixel p.sub.i-2,j-4 and the corresponding edge, as follows: 
when e1.sub.i-1,j-5 =1, de.sub.i-1,j-5 =0, and 
when e1.sub.i-1,j-5 =0, de.sub.i-1,j-5 =min (de.sub.i-1,j-6, 
de.sub.i-2,j-5, de.sub.i-2,j-4)+1. 
The distances de.sub.i-1,j-6, de.sub.i-2,j-5, de.sub.i-2,j-4 are read out 
from the line memory 23, and the distance de.sub.i-1,j-5 is newly stored 
in the line memory 23. The distance de.sub.i-1,j-5 from the edge is input 
to a comparator 24 and is classified based on the following rules: 
when de.sub.i-1,j-5 .ltoreq.T6, f.sub.i-1,j-5 =0, 
when T6 &lt;de.sub.i-1,j-5 .ltoreq.T7, f.sub.i-1,j-5 =1, 
when T7 &lt;de.sub.i-1,j-5 .ltoreq.T8, f.sub.i-1 j-5 =2, and 
when T8 &lt;de.sub.i-1,j-5, f.sub.i-1,j-5 =3. 
The distance classifying signal f.sub.i-1,j-5 is input to the 
characteristic judging circuit 30. 
The characteristic judging circuit 30 determines a density conversion 
selecting signal g.sub.i-1,j-5 based on the first edge detection signal 
e1.sub.i-1,j-5 and the distance classifying signal f.sub.i-1,j-5, in 
accordance with rules shown in FIG. 11. The density conversion selecting 
signal g.sub.i-1,j-5 is determined such that a density conversion curve is 
selected taking account of the following. 
1. Edge pixels mainly exist in binary image areas. 
2. As the distance between a certain pixel and an edge pixel closest to the 
certain pixel becomes shorter, the probability that the image is a binary 
image increases, and as the distance between the certain pixel and the 
edge pixel closest to the certain pixel becomes longer, the probability of 
being a gradation image increases. 
In the table shown in FIG. 11, for a signal taking one of two values, 
symbol o indicates that the signal is valid, symbol x indicates that the 
signal is invalid, and symbol -indicates that the signal is ignored. For a 
signal taking one of three or more values, a numeral represents the value 
of the signal. The value of the density conversion selecting signal 
g.sub.i-1,j-5 has the following meaning: 
when g.sub.i-1,j-5 =0, a binary image, 
when g.sub.i-1,j-5 =3, a gradation image, 
when g.sub.i-1,j-5 =1, probably a binary image, and 
when g.sub.i-1,j-5 =2, probably a gradation image. 
The density conversion circuit 3 includes four density conversion curves, 
like in the first embodiment. Based on the value of the density conversion 
selecting signal g.sub.i-1,j-5 input from the characteristic judging 
circuit 30, a density conversion curve is selected in accordance with the 
following rules: 
when g.sub.i-1,j-5 =0, density conversion curve A for binary image, 
when g.sub.i-1,j-5 =3, density conversion curve B for gradation image, 
when g.sub.i-1,j-5 =1, density conversion curve C which interpolates the 
density conversion curves A and B and which is close to the density 
conversion curve A; and 
when g.sub.i-1,j-5 =2, density conversion curve D which interpolates the 
density conversion curves A and B and which is close to the density 
conversion curve B. 
The image density signal d.sub.i-1,j-5 is subjected to density conversion 
using a selected density conversion curve to output a converted density 
signal cd.sub.i-1,j-5. 
Actually, the density conversion circuit 3 has four data conversion tables 
corresponding to the above-described four density conversion curves. The 
data conversion tables are selectively used based on the value of the 
density conversion selecting signal g.sub.i-1,j-5 input from the 
characteristic judging circuit 30, and the image density signal 
d.sub.i-1,j-5 is converted to the density signal cd.sub.i-1,j-5 with 
reference to a selected data conversion table. 
The converted density signal cd.sub.i-1,j-5 output from the density 
conversion circuit 3 is input to a binary signal producing circuit 4. The 
binary signal producing circuit 4 converts the converted density signal 
cd.sub.i-1,j-5 to a binary signal by an error diffusion method. In the 
resent embodiment, the accuracy of the edge detection can be increased in 
the same manner as that used in the first embodiment. Although two 
additional density conversion curves which interpolate the density 
conversion curve for binary images and the density conversion curve for 
gradation images are used in the present embodiment, the number of the 
additional density conversion curves can be reduced from two to one. Also, 
the number of classes into which the distances are classified by the 
distance classifying circuit 11 may be increased so as to provide three or 
more density conversion curves which interpolate the density conversion 
curve for binary images and the density conversion curve for gradation 
images. 
Third Embodiment: 
Next, an image processing apparatus according to a third embodiment will be 
described with reference to FIG. 12. 
Like the first embodiment, image density signals (d.sub.i,j, d.sub.i,j-1, 
d.sub.i,j-2, d.sub.i-1,j, d.sub.i-1,j-1, d.sub.i-1,j-2, d.sub.i-2,j, 
d.sub.i-2,j-1, and d.sub.i-2,j-2) output from the line memory 2 form a 
matrix of 3.times.3 in which pixel p.sub.i-1,j-1 is centrally located. 
As shown in FIG. 12, image density signal d.sub.i_1,j-5 output from the 
line memory 2 is input to a high density line detection circuit 10 and a 
density conversion circuit 3. Image density signals (d.sub.i,j, 
d.sub.i,j-1, d.sub.i,j-2, d.sub.i-1,j, d.sub.i-1,j-1, d.sub.i-1,j-2, 
d.sub.i-2,j, d.sub.i-2,j-1, and d.sub.i-2,j-2) output from the line memory 
2 are input to a thin line detection circuit 5, and an edge detection 
circuit 7. The thin line detection circuit 5 detects whether a certain 
pixel is a part of a thin line or not. In the thin line detection circuit 
5, a convolution calculation is performed using thin line detection 
coefficient matrixes for the lateral direction (main scanning direction) 
and the longitudinal direction (auxiliary scanning direction) shown in 
FIGS. 18A and 18B. The larger one of the absolute values of two obtained 
values is compared with a threshold T1. When the lager one of the absolute 
values is equal to or greater than the threshold T1, a thin line detection 
signal h.sub.i-1,j-1 indicating a validity (i.e., a signal indicating that 
the detected image is a thin line). When the lager one of the absolute 
values is less than the threshold T1, a thin line detection signal 
h.sub.i-1,j-1 indicating an invalidity (i.e., a signal indicating that the 
detected image is not a thin line). The thin line detection signal 
h.sub.i-1,j-1 is input to a dot delay circuits 6 for matching with the 
timing of density conversion for image density signal d.sub.i-1,j-5 of a 
pixel to be processed. The thin line detection signal h.sub.i-1,j-5 output 
from the dot delay circuit 6 is input to a characteristic judging circuit 
12. 
FIG. 13 shows the detail of the edge detection circuit. A calculation 
circuit 13 performs a convolution calculation using edge detection 
coefficient matrixes for lateral direction, longitudinal or vertical 
direction, and two oblique directions perpendicularly intersecting each 
other shown in FIGS. 5A, 5B, 5C and 5D, like the first embodiment. The 
largest one of the absolute values of four obtained values is output as an 
edge signal e.sub.i-1,j-1, and is input to comparators 14 and 15. 
The comparator 15 outputs a first edge detection signal e1.sub.i-1,j-1. 
When the input edge signal e.sub.i-1,j-1 is equal to or greater than a 
first edge threshold T2, the first edge detection signal e1.sub.i-1,j-1 is 
a valid signal which indicates that the pixel is an edge pixel. When the 
input edge signal e.sub.i-1,j-1 is less than the first edge threshold T2, 
the first edge detection signal e1.sub.i-1,j-1 is an invalid signal which 
indicates that the pixel is not an edge pixel. The first edge threshold T2 
is determined to only detect large sharp edges (large changes in density) 
which hardly appear in gradation images. 
The comparator 14 outputs a second edge detection signal e2.sub.i-1,j-1. 
When the input edge signal e.sub.i-1,j-1 is equal to or greater than a 
second edge threshold T3, the second edge detection signal e2.sub.i-1,j-1 
is a valid signal. When the input edge signal e.sub.i,j-1 is less than the 
second edge threshold T3, the second edge detection signal e2.sub.i-1,j-1, 
is an invalid signal. The second edge threshold T3 is determined to be 
smaller than the first edge threshold T2 so as to detect intermediate 
sharp edges (slightly large changes in density). 
FIG. 14 shows the detail structure of the high density line detection 
circuit. The high density line detection circuit is used to identify a 
line having a predetermined thickness and density. That is, the circuit 
detects whether a certain pixel is located between an edge pixel at a 
rising edge and another edge pixel at a falling edge and whether the 
density of the certain pixel is high. 
The first edge detection signal e1.sub.i-1,j-1 is input to a dot delay 
circuit 16 including four delay stages, and an OR circuit 17. 
Simultaneously, the outputs e1.sub.i-1,j-2, e1.sub.i-1,j-3, e1.sub.i-1,j-4 
and e1.sub.i-1,j-5 of the dot delay circuit 16 are input to the OR circuit 
17 so that an edge signal m.sub.i-1,j-5 for a pixel located ahead is 
output from the OR circuit 17. 
The comparator 19 outputs a high density detection signal c4.sub.i-1,j-1 
indicating a validity when the input image density signal d.sub.i-1,j-5 is 
equal to or greater than a threshold T4. The comparator 19 outputs a high 
density detection signal c4.sub.i-1,j-5 indicating an invalidity when the 
input image density signal d.sub.i-1,j-5 is less than a threshold T4. The 
output e1.sub.i-1,j-5 of the delay circuit 16 and the high density line 
detection signal m'.sub.i-1,j-5 are input to an OR circuit 21. An 
edge/high density line detection signal bm.sub.i-1,j-5 is output from the 
OR circuit 21 and is input to a dot delay circuit 20 so that an edge/high 
density line detection signal bm.sub.i-1,j-6 for a pixel located behind is 
output from the dot delay circuit 20. 
The edge signal m.sub.i-1,j-5 for the pixel located ahead, the high density 
detection signal c4.sub.i-1,j-5, and the edge/high density line detection 
signal bm.sub.i-1,j-6 for the pixel located behind are input to an AND 
circuit 18. A high density line detection signal m'.sub.i-1,j-5 is output 
from the AND circuit 18, and is input to the OR circuit 21 and the 
characteristic judging circuit 12. 
The distance classifying circuit 11 calculates the distance between a 
certain pixel and an edge pixel and classifies the distance. FIG. 15 shows 
the detail of the distance classifying circuit 11. The line memory 23 
stores distance data representing the distances between pixels in a single 
line and an edge pixel. 
FIG. 16 shows the positions of the pixels on an original document which 
correspond to the distances between the pixels stored in the line memory 
and the corresponding edge pixels. The first edge detection signal 
e1.sub.i-1,j-5 is input to a distance counting circuit 22 in which the 
distance de.sub.i-1,j-5 between the pixel p.sub.i-1,j-5 and the 
corresponding edge is calculated based on the distance de.sub.i-1,j-6 
between the left-hand pixel p.sub.i-1,j-6 and the corresponding edge, the 
distance de.sub.i-2,j-5 between the upper pixel p.sub.i-2,j-5 and the 
corresponding edge, and the distance de.sub.i-2,j-4 between the 
right-upper pixel p.sub.i-2,j-4 and the corresponding edge, as follows: 
when e1.sub.i-1,j-5 =1, de.sub.i-1,j-5 =0, and 
when e1.sub.i-1,j-5 =0, de.sub.i-1,j-5 =min (de.sub.i-1,j-6, 
de.sub.i-2,j-5, de.sub.i-2,j-4)+1. 
The distances de.sub.i-1,j-6, de.sub.i-2,j-5, de.sub.i-2,j-4 are read out 
from the line memory 23, and the distance de.sub.i-1,j-5 is newly stored 
in the line memory 23. The distance de.sub.i-1,j-5 from the edge is input 
to a comparator 24 and is classified based on the following rules: 
when de.sub.i-1,j-5 &lt;T6, f.sub.i-1,j-5 =0, 
when T6 .ltoreq.de.sub.i-1,j-5 &lt;T7, f.sub.i-1,j-5 =1, 
when T7 .ltoreq.de.sub.i-1,j-5 &lt;T8, f.sub.i-1,j-5 =2, and 
when T8 .ltoreq.de.sub.i-1,j-5, f.sub.i-1,j-5 =3. 
The distance classifying signal f.sub.i-1,j-5 is input to the 
characteristic judging circuit 12. 
The characteristic judging circuit 12 determines a density conversion 
selecting signal g.sub.i-1,j-5 based on the thin line detection signal 
h.sub.i-1,j-5, the first edge detection signal e1.sub.i-1,j-5, the second 
edge detection signal e2.sub.i-1,j-5, the high density line detection 
signal m'.sub.i-1,j-5 and the distance classifying signal f.sub.i-1,j-5, 
in accordance with rules shown in FIG. 17. 
The density conversion selecting signal g.sub.i-1,j-5 is determined such 
that a density conversion curve is selected taking account of the 
following. 
1. Edge pixels mainly exist in binary image areas. 
2. As the sharpness (change in the density) becomes larger, the probability 
of being a binary image increases, and as the sharpness (change in the 
density) becomes smaller, the probability of being a gradation image 
increases. 
3. When a segment of a line exist in which a certain pixel is located 
between an edge pixel at the rising edge and another edge pixel at the 
falling edge, a probability that the line forms a character (a part of a 
binary image) is high if the line has a high density and is thin. 
4. As the distance between a certain pixel and an edge pixel closest to the 
certain pixel becomes shorter, the probability that the image is a binary 
image increases, and as the distance between the certain pixel and the 
edge pixel closest to the certain pixel becomes longer, the probability of 
being a gradation image increases. 
In the table shown in FIG. 17, for a signal taking one of two values, 
symbol o indicates that the signal is valid, symbol x indicates that the 
signal is invalid, and symbol - indicates that the signal is ignored. For 
a signal taking one of three or more values, a numeral represents the 
value of the signal. The value of the density conversion selecting signal 
g.sub.i-1,j-5 has the following meaning: 
when g.sub.i-1,j-5 =0, a binary image, 
when g.sub.i-1,j-5 =3, a gradation image, 
when g.sub.i-1,j-5 =1, probably a binary image, and 
when g.sub.i-1,j-5 =2, probably a gradation image. 
The density conversion circuit 3 includes four density conversion curves 
shown in FIG. 7, i.e., a density conversion curve A (solid line) for 
binary images which converts an input density signal to a binary signal 
i.e., to the maximum or the minimum value, a density conversion curve B 
(alternate long and short dash line) for gradation images which closely 
copies the gradation of an input density signal to an output density 
signal, a density conversion curve (C) (broken line) close to the density 
conversion curve (A) for binary images, and a density conversion curve (D) 
(alternate long and two short dashes line) close to the density conversion 
curve (B) for gradation images. The density conversion curves (C) and (D) 
interpolate the density conversion curves (A) and (B). 
Based on the value of the density conversion selecting signal g.sub.i-1,j-5 
input from the characteristic judging circuit 12, a density conversion 
curve is selected in accordance with the following rules: 
when g.sub.i-1,j-5 =0, density conversion curve A for binary image, 
when g.sub.i-1,j-5 =3, density conversion curve B for gradation image, 
when g.sub.i-1,j-5 =1, density conversion curve C which interpolates the 
density conversion curves A and B and which is close to the density 
conversion curve A; and 
when g.sub.i-1,j-5 =2, density conversion curve D which interpolates the 
density conversion curves A and B and which is close to the density 
conversion curve B. The image density signal d.sub.i-1,j-5 is subjected to 
density conversion using a selected density conversion curve to output a 
converted density signal cd.sub.i-1,j-5. 
The density conversion circuit 3 is the same as that used in the first 
embodiment. In the present embodiment, the thin line detection coefficient 
matrixes and the edge detection coefficient matrixes are 3.times.3. The 
accuracy of the thin line detection circuit and the edge detection circuit 
can be increased by changing the size of the matrixes to n.times.m (n&gt;0, 
m&gt;0, n and m are integers), changing the coefficients, or increasing the 
number of the matrixes. Also, thicker lines can be detected by increasing 
the number of stages of delay in the dot delay circuit in the high density 
line detection circuit. 
Although two additional density conversion curves which interpolate the 
density conversion curve for binary images and the density conversion 
curve for gradation images are used in the present embodiment, the number 
of the additional density conversion curves can be reduced from two to 
one. Also, the number of classes into which the sharpness of an edge is 
classified may be increased by adding comparators having different 
thresholds to the edge detection circuit 7, or the number of classes into 
which the distances are classified by the distance classifying circuit 11 
may be increased, so as to provide three or more density conversion curves 
which interpolate the density conversion curve for binary images and the 
density conversion curve for gradation images. 
Obviously, numerous conversions and variations of the present invention are 
possible in light of the above teachings. It is therefore to be understood 
that within the scope of the appended claims, the present invention may be 
practiced other than as specifically described herein.