JP2969921B2 - Edge feature extraction device - Google Patents

Description

Description: BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an edge extraction process for an image, which is capable of extracting an edge line which is less affected by noise and has better connectivity from a degraded image, and which can process the image at high speed. The present invention relates to a possible edge feature extraction device.

2. Description of the Related Art Conventional edge feature extraction devices include, for example, "C. Koch, J. Marroquin and A. Yuille: Analog" Neuronal "Networks in Early Vision (Analog “Neuronal” N
etworks in early vision), Proc. Natl. Acad. Sci. USA, 8
3, pp. 4263-4267 (1986) ".

FIG. 8 shows a configuration diagram of this conventional edge feature extraction apparatus, wherein 1 is an input image, 2 is convolution matrix data, 81 is an edge energy minimizing section, 82
Denotes a pixel energy minimizing unit, and 6 denotes an edge output unit.

The operation of the conventional edge feature extraction device configured as described above will be described with reference to the drawings.

In order to obtain a correct edge line from a deteriorated image, it is necessary to extract an edge while performing restoration processing of the deteriorated image. Generally, an image degradation process is expressed in such a manner that an original image is first blurred and then noise is added. Therefore, it is assumed that the degraded image is g (x, y), the original image is f (x, y), the original image is blurred by the function h (x, y), and the noise is n (x, y). Then, the degradation process of the image can be expressed as the following equation.

g (x, y) = ∬h (x−a, y−b) f (a, b) dadb + n (x, y) (1) Equation (1) is replaced with a discrete image. And (2).

Here, the pixel value of the deteriorated image at the coordinates (i, j) is represented by G
(I, j), the pixel value of the original image is F (i, j), and the noise value is N
(I, j), the component (i, j) of the convolution matrix representing the function h (x, y) is H (i, j). However, in the convolution matrix, the (k, l) component is f
(I, j).

F (i, j) when G (i, j) and H (i, j) are known
Consider estimating. Now, the equation (2) is transformed into the following equation.

Since N (i, j) is unknown, it is necessary to find F (i, j) that minimizes the right side of equation (3) while smoothing G (i, j). Therefore, as a constraint condition for performing smoothing, a difference in pixel value between pixels as expressed by the following equation is 0.
The minimum expression Econ (i, j) is added to the expression (3) when

Econ (i, j) = {F (i, j + 1) -F (i, j))} ² + {F (i + 1, j) -F (i, j)} ² (4) Assume that it exists between pixels.
Therefore, virtual values A (i, j) and B (i, j) existing between pixels, which are called line processes as shown in FIG. 9, are introduced as indices indicating whether or not there is an edge. And Econ
(I, j) is rewritten as the following equation. Where A (i, j),
It is assumed that B (i, j) has a value of 1 if the edge is hit and 0 if it is not.

Econ (i, j) = {F (i, j + 1) -F (i, j)} ² {1-A (i, j)} + {F (i + 1, j) -F (i, j)} ² {1−B (i, j)} (5) Equation (5) shows that, as viewed from the line processes A and B, “the greater the difference in pixel value between pixels, the closer the value of the line process to 1”. In view of the pixel value F, it indicates that "smoothing is performed between pixels having no standing edges, and smoothing is not performed between pixels having standing edges".

Further, A (i, j) and B (i, j) must be estimated from the input image. Therefore, it is assumed that a set of A (i, j) and B (i, j) when Eedg expressed by the following equation is minimized is most likely to be an edge of an image.

Here, the first item is “the line assumption assumes a value of 1 or 0”, the second item is “line assumptions in the same direction are unlikely to be arranged in parallel”, and the third item is “edge Do not stand too much, "and the last term describes the process," the edges are usually continuous or bent, and do not branch too much. " However, Ca, Cb,
Cc is a parameter representing a weight for the entirety of each term.

Eventually, F (i, j) that minimizes E expressed by the following equation is the pixel value at the coordinates (i, j) of the restored image to be obtained.

Here, C1, C2, C3, C4, and C5 are parameters representing the weight of each term with respect to the whole.

In the conventional edge feature extraction device, as shown in FIG. 8, the edge energy minimizing section 81 receives inputs from the input image 1, the convolution matrix data 2, and the pixel energy minimizing section.

FIG. 10 is a specific configuration diagram of the edge energy minimizing unit 81 and the pixel energy minimizing unit 82, 101 is an edge partial differentiating unit, 104 is an edge memory, 105 is a pixel partial differentiating unit,
108 is a pixel memory, 102 and 106 are multiplication units, 103 and 10
7 represents an addition unit.

First, the contents of the pixel memory representing the pixel values of the restored image are initially set to the pixel values of the input image 1. The contents of the edge memory 104 representing the values of the line process are all initialized to appropriate values of 0 to 1.

As shown in FIG. 10, the pixel partial differentiator 105
The input is received from the convolution matrix data 2, the adder 103, and the edge memory 104.

Now, a function obtained by partially differentiating equation (7) with respect to F (i, j) is expressed as E
_{If f} is set, the pixel partial differentiator 105 calculates the value of E _f for each pixel based on the input value, and feeds it back to itself via the multiplier 106 and the subsequent adder 107. However, the coefficients C1 to C5 in the equation (7) are fixed to appropriate values in advance.

The multiplication unit 106 calculates a negative number (−−) sufficiently close to 0 to the input value.
ε _f ) and output.

The addition unit 107 outputs a value obtained by adding the inputs from the multiplication unit 106 and the pixel memory 108 to the pixel partial differentiation unit 105 and the edge partial differentiation unit 101, and writes the content of the pixel memory 108 into the output value. Change. As a result, the content of the pixel memory 108 is the value of F (i, j) one time before, and the content is updated according to the following equation.

F _{t + 1} (i, j) = F _t (i, j) −ε _f E _f (F _t (i, j)) (8) where F _{t + 1} (i, j) and F _t (i, j) are coordinates (i,
j) represents the pixel value at time t + 1 and the pixel value at time t, and ε _f is a sufficiently small positive number.

Here, since E is differentiable with respect to F (i, j), the following holds.

If ΔF (i, j) → 0, then ｛E (△ F (i, j) + F (i, j)) − E (F (i, j))｝ / △ F (i, j) = ∂E / ∂F (i, j) (9) where ΔF (i, j) = − ε _f ∂E / ∂F (i, j) where E (△ F (i, j) + F (i, j)) − E (F (i, j)) = − ε _f {E / {F (i, j)} ² ≦ 0 (10) From equation (10), (8) ), F (i, j) that minimizes E can be obtained by updating F (i, j).

The edge partial differentiator 101 includes an adder 107 and a pixel memory 108.
Receives input from

Now equation (7) to A (i, j), B (i, j) partially differentiating function E _e far To pixel partial differential unit 105 with respect to the the E _e of each line process based on the input value Calculate the value, multiplying unit 106,
The feedback is provided to itself via the subsequent addition unit 107. However, the coefficients C1 to C5 in the equation (7) are fixed to appropriate values in advance.

The multiplication unit 102 generates a negative number (−−) sufficiently close to the value of the input.
ε _l ) and output.

The adder 103 outputs a value obtained by adding the inputs from the multiplier 102 and the edge memory 104 to the edge partial differentiator 101 and the pixel partial differentiator 105, and writes the content of the edge memory 104 into the output value. Change. After all, the contents of the edge memory 104 are the values of A (i, j) and B (i, j) one time before,
For the same reason as shown in the expressions (8) to (10), E
A (i, j) and B (i, j) that minimize the following can be obtained.

Next, the edge output unit 6 outputs the contents of the edge memory 104 as an edge image.

Problems to be Solved by the Invention However, in the above-described configuration, between pixels in which the fluctuation range of the pixel value is extremely large, when estimating the line process,
The pull-in by the second term (Equation (5)) of Equation (7), which expresses the assumption that the value of the line process approaches 1 as the pixel value difference between the pixels is larger, occurs. 7)
Regardless of the third term in the equation), the value of the line process is
Will be approached. Therefore, between pixels on which noise having an extremely large variation width is loaded, the value of the line process approaches 1 very quickly, so that an erroneous edge due to noise remains in the edge image. Further, the influence of the other assumption of the connectivity of the edge lines (from the third term of the equation (7)) is reduced, and the connectivity of the edge lines is also deteriorated because erroneous edges remain. If there is a lot of noise with extremely large fluctuation range,
Since many unnecessary line processes have a value close to 1, smoothing of the entire image does not proceed, and there is a problem that the processing speed is reduced.

In view of the above, the present invention provides an edge feature extraction device which is less susceptible to noise even when there is noise having a large fluctuation range of pixel values, can extract an edge line having good connectivity, and has a high processing speed. The purpose is to do.

Means for Solving the Problems A parameter setting unit that sets parameters associated with each pixel of an image according to a difference between adjacent pixels of an input image, and represents a probability that an edge exists between pixels. A line process having a value as a variable, an edge energy minimizing unit that estimates a line process by minimizing a function determined by an input image and an input from the parameter setting unit,
Pixel energy minimization for estimating a pixel value by minimizing a function determined by an input image, an input from the edge energy minimizing unit, and convolution matrix data representing a deterioration process of the image, using a pixel value as a variable. And an edge feature extraction device comprising: an edge output unit that outputs an image obtained from the value of the line process estimated by the edge energy minimization unit as an edge image.

Effect of the Invention With the above configuration, the present invention is based on the assumption that “when the difference between pixel values between pixels is extremely large, the probability of noise is high”. Changes the coefficient of equation (7). By controlling the coefficient of the equation (7), (7)
The pull-in by the second term of the equation (equation (5)) can be suppressed, and an edge image free from erroneous edges due to noise can be obtained. Accordingly, the connectivity of the edge lines is improved. In addition, the activation speed of an unnecessary line process can be suppressed, and accordingly, the processing speed increases.

Embodiment FIG. 1 shows a configuration diagram of an edge feature extraction device according to an embodiment of the present invention. In FIG. 1, 1 is an input image, 2 is a parameter setting unit, 3 is convolution matrix data, 4 is an edge energy minimizing unit, 5
Denotes a pixel energy minimizing unit, and 6 denotes an edge output unit.

The operation of the edge feature extraction device of the present embodiment configured as described above will be described below.

First, the input image 1 is input to the parameter setting unit 2 and the pixel energy minimizing unit 5. However, the parameter setting unit 2
, The input image 1 is input at the beginning of the process, and the output from the pixel energy minimizing unit 5 is input thereafter.

FIG. 2 is a configuration diagram of the parameter setting unit 2, in which 21 is a function parameter determination unit, 22 is a pixel difference unit, 23 is a coefficient control unit, and 24 is a coefficient memory. The coefficient memory 24 is a memory that is distinguished by the coordinate position of the line process. The contents of each memory are all initialized with the same value.

The image input to the parameter setting unit 2 is first input to the function parameter determination unit 21 and the pixel difference unit 22. The function parameter determination unit 21 performs, for example, a spatial frequency analysis on the input image, and when the high frequency component is large, the image with a strong discontinuity is obtained, and when the low frequency component is large, the discontinuity is weak. The image is determined to be an image, and a large value is output to the coefficient control unit 23 if the image has a strong discontinuity, and a small value is output if the image has a weak discontinuity.

The pixel difference unit 21 calculates a difference value between adjacent pixels, and outputs the result to the coefficient control unit 23 for each calculated coordinate position of a line process between pixels.

The coefficient control unit 23 determines a function (coefficient control function) for controlling the coefficient of Expression (7) based on the input from the function parameter determination unit 21, and further determines the function of the coefficient control function with respect to the input from the pixel difference unit 22. The output is stored in a coefficient memory 24 corresponding to the coordinate position of the linear process between pixels for which the input from the pixel difference unit 22 has been calculated.
Output to the memory inside. The coefficient memory 24 rewrites the contents of each memory to input values and holds them.

FIG. 3 is a diagram showing a specific configuration diagram of the coefficient control unit 23, where 31 is a time function unit, and 32 is a coefficient control function unit.

The output of the function parameter determination unit 21 is input to the time function unit 31. The output value θ of the time function unit 31 is, for example, a linear function as shown in FIG. 4 (a) or a linear function as shown in FIG. 4 (b) with respect to the input value θ ₀ from the function parameter determination unit 21. It has a value determined by a combination of functions or a non-linear function as shown in FIG. 4 (c).

The coefficient control function unit 32 receives inputs from the time function unit 31 and the pixel difference unit 22, and outputs a value for each pixel. For example, when controlling the coefficient C2 in the equation (7), the output value C2 (x, i, j) of the coefficient control function unit 32 is calculated based on the input value ΔF from the pixel difference unit 22.
(X, i, j) has a constant value up to the input value θ from the time function unit 31, and the input ΔF (x, i,
For j), a linear function as shown in FIG.
It has a value determined by a combination of linear functions as shown in FIG. 5 (b) or a nonlinear function as shown in FIG. 5 (c). However, C2 (x, i, j ) is initially set to appropriate initial values C2 _0. Here, the subscript x of C2 and ΔF indicates the direction (horizontal direction or vertical direction) of the pixel from which the difference is obtained.

C2 represents a ratio that takes into account the assumption that "edges do not stand too much". If the value of C2 is large,
The effect of suppressing the activation of the line process also increases, and the value of the line process approaches zero. Therefore, the control of the coefficient C2 as shown in FIG. 5 corresponds to "if the difference between the pixels is larger than a certain value, the activation of the line process is suppressed in proportion to the difference value".

FIG. 6 is a specific configuration diagram of the edge energy minimizing section 4 and the pixel energy minimizing section 5, where 61 is an edge partial differentiating section, 62 and 66 are multiplying sections, and 63 and 67 are adding sections. , 64 are edge memories, 65 is a pixel partial differentiator, and 68 is a pixel memory.

The contents of the edge memory 64 representing the value of the line process are all 0 to
Initialized to an appropriate value of 1. First, the contents of the pixel memory 68 representing the pixel values of the restored image are initially set to the respective pixel values of the input image 1.

The edge partial differentiator 61 receives inputs from the adder 67, the pixel memory 68, and the parameter memory 64.

Assuming that a function obtained by partially differentiating equation (7) with respect to A (i, j) and B (i, j) is E _e , the pixel partial differentiator 45 calculates the value of E _e for each line process based on the input value. The value is calculated and fed back to itself via the multiplication unit 46 and the subsequent addition unit 47. However, when calculating the value of E _e in each line process, the corresponding contents stored in the memory of the parameter memory 24 is read out, the values of the coefficients of equation (7), the read value Can be changed to

The multiplier 62 multiplies the input value by a negative number sufficiently close to 0 and outputs the result.

The adder 63 calculates a value obtained by adding the inputs from the multiplier 62 and the edge memory 64 to the edge partial differentiator 61 and the pixel partial differentiator 65.
While the contents of the edge memory 64 are rewritten to the output values. Eventually, the contents of the edge memory 64 are the values of A (i, j) and B (i, j) one time before, and (8) to (1)
A (i, j) and B (i, j) that minimize E can be obtained for the same reason as the content shown in equation (0).

At this time, the coefficient control by the parameter setting unit 2 suppresses the pull-in by the second term of the equation (7) (equation (5)), and the connectivity is determined by another condition (the third and subsequent terms of the equation (7)). Can be added. Therefore, even when there is noise having a large fluctuation range, the linear processes A (i, j) and B (i,
The value of j) does not suddenly approach 1, and a line process with good connectivity can be obtained.

As shown in FIG. 6, the pixel partial differentiator 65 includes an input image 1, convolution matrix data 2, an adder 63,
An input is received from the edge memory 64.

Now, a function obtained by partially differentiating equation (7) with respect to F (i, j) is expressed as E
_f , the pixel partial differentiator 65 calculates the value of E _f for each pixel based on the input value, and calculates a multiplier 66 and a subsequent adder.
Give yourself feedback via 67.

The multiplier 66 multiplies the input value by a negative number sufficiently close to 0 and outputs the result.

The adder 67 outputs a value obtained by adding the inputs from the multiplier 66 and the pixel memory 68 to the pixel partial differentiator 65 and the edge partial differentiator 61, and writes the content of the pixel memory 68 into the output value. Change. After all, the content of the pixel memory 68 is the value of F (i, j) one time before, and for the same reason as the content shown in the equations (8) to (10), F (i, j) can be obtained.

At this time, the line process held in the edge memory 64 is
The control of the coefficient by the parameter setting unit 2 has no influence of noise and is in a state of good connectivity, and accordingly, the pixel value held in the pixel memory 68 is:
There is no noise with a large fluctuation range, and the edge portion is in a clear state.

Furthermore, even when there is a large amount of noise having a large fluctuation range, the value of many unnecessary line processes does not approach 1 due to the influence of the noise. Therefore, the smoothing proceeds quickly, and the processing speed can be increased.

Next, the edge output unit 6 outputs the contents of the edge memory 64 as an edge image.

When controlling the coefficient C1 in the equation (7), the output function of the coefficient control function unit 32 may be a linear function as shown in FIG. 7 (a) or a linear function as shown in FIG. 7 (b). A non-linear function or the like as shown in FIG.

Advantageous Effects of the Invention According to the present invention, it is possible to obtain an edge line having good connectivity without leaving erroneous edges due to noise having a large fluctuation width. Further, the processing speed increases accordingly.

[Brief description of the drawings]

FIG. 1 is a block diagram showing a configuration of an edge feature extraction device in the present embodiment, FIG. 2 is a block diagram showing a configuration of a parameter setting unit in FIG. 1, and FIG. 3 is a configuration of a coefficient control unit in FIG. FIG. 4 is a diagram of the output function of the time function unit of FIG. 3, FIG. 5 is a diagram of a coefficient control function when controlling the coefficient C2 of the equation (7), and FIG. FIG. 7 is a block diagram showing a specific configuration of an edge energy minimizing unit and a pixel energy minimizing unit shown in FIG. 7; FIG. 7 is a diagram of a coefficient control function for controlling a coefficient C1 in equation (7); FIG. 9 is a block diagram showing a configuration of an edge feature extraction device in an embodiment of the example, FIG. 9 is an explanatory diagram of a line process, and FIG. 10 is a block diagram showing a configuration of an edge energy minimizing unit and a pixel energy minimizing unit in FIG. FIG. 1 ... input image, 2 ... parameter setting unit, 3 ... convolution matrix data, 4, 81 ... edge energy minimizing unit, 5, 82 ... pixel energy minimizing unit, 6 ... edge output unit, 21 ... function parameter determination unit, 22 ... pixel difference unit, 23 ... coefficient control unit, 24 ... coefficient memory, 31 ... time function unit, 32 ... coefficient control function unit, 6
1, 101: Edge partial differentiator, 62, 66, 102, 106 Multiplier, 63, 67, 103, 107 Adder, 64, 104 Edge memory, 65, 105 Pixel partial differential , 68, 108 ... Pixel memory.

Continuing on the front page (72) Inventor Toshiyuki Koda 1006 Kazuma Kadoma, Osaka Prefecture Matsushita Electric Industrial Co., Ltd. 56) References JP-A-62-111780 (JP, A) JP-A-1-113879 (JP, A) JP-A-63-261479 (JP, A) JP-A-1-145578 (JP, A) JP Hei 1-2224883 (JP, A) Proceedings of the Conference of the Institute of Image Electronics Engineers of Japan Vol. 119th 1990.4.5 "Study of bias input method for restoration of natural images based on energy minimization principle" Yamamoto et al. (58) Fields studied (Int. Cl. ⁶ , DB name) G06T 9/20

Claims (10)

(57) [Claims] A parameter setting unit for setting a parameter associated with each pixel of an image according to a difference between adjacent pixels of an input image, and a parameter indicating a probability that an edge exists between the pixels. An edge energy minimizing unit that estimates a line process by minimizing a function determined by an input image and an input from the parameter setting unit, and a pixel value as a variable. A pixel energy minimizing unit for estimating a pixel value by minimizing a function determined by an input from the edge energy minimizing unit and convolution matrix data representing a deterioration process of an image, and the edge energy minimizing unit. An edge output unit for outputting an image obtained from the estimated value of the line process as an edge image. Detection device. 2. A parameter setting unit for determining whether an input image is an image having high continuity or an image having low continuity, and a function parameter determining unit for outputting an output according to the result, and a difference value between adjacent pixels of the input image. And a function determined by the input from the function parameter determination unit as a coefficient control function, by the input from the pixel difference unit,
2. The edge feature extraction device according to claim 1, wherein the coefficient control function includes a coefficient control unit that controls a parameter of an edge energy minimizing unit, and a coefficient memory that holds an output from the coefficient control unit. . 3. A coefficient control unit, wherein an input from a function parameter determination unit is used as an initial value, a time function unit whose output value changes with processing time, and an output from the time function unit and an input from a pixel difference unit. 3. The edge feature extraction device according to claim 2, wherein the edge feature extraction device is configured by a coefficient control function unit that determines the following. 4. The edge feature extraction device according to claim 3, wherein the time function unit uses an input value from the function parameter determination unit as an output value as it is. 5. The edge feature extraction device according to claim 3, wherein the output value of the time function section changes at a constant rate with the processing time. 6. The edge feature extraction device according to claim 3, wherein the time function unit changes output values at different rates in sections distinguished by processing time. 7. The edge feature extraction device according to claim 3, wherein the output value of the time function section changes nonlinearly with the processing time. 8. The coefficient control function section does not change when the input value from the pixel difference section is smaller than the input value from the time function section, and when the input value from the time function section is equal to or greater than the input value from the time function section, 4. The edge feature extraction device according to claim 3, wherein an output value that changes at a fixed rate with respect to an input value from the input device is obtained. 9. The coefficient control function section does not change when the input value from the pixel difference section is smaller than the input value from the time function section, and when the input value from the time function section is equal to or larger than the input value from the time function section, The edge feature extraction device according to claim 3, wherein output values that change at different rates are taken for each section distinguished by an input value from the edge feature. 10. The coefficient control function section does not change when the input value from the pixel difference section is smaller than the input value from the time function section, and when the input value from the time function section is greater than the input value from the time function section, the coefficient control function section does not change. The edge feature extraction device according to claim 3, wherein an output value that changes nonlinearly is taken for an input value from the edge feature.