Edge detecting method

Abstract

There is provided an edge detecting method, which is capable of preventing a noise influence caused by imaging device and a color interpolation. The edge detecting method includes the steps of: setting a first kernel based on a center pixel in pixel data arranged in a mosaic structure; setting a second kernel based on the center pixel within the first kernel; detecting whether a pixel having a green value in the second kernel is a defective pixel, and correcting the pixel; converting all pixels of the second kernel into pixels having green value; calculating a slope value by using a mask for detecting an edge in the second kernel; and detecting an edge by adding the slope value to a luminance value obtained by a color space conversion.

Description

FIELD OF THE INVENTION

The present invention relates to a method for processing an image signal; and, more particularly, to a method for detecting an edge of an image signal.

DESCRIPTION OF RELATED ART

An image sensor can be used in various fields, such as a cell phone, a personal computer (PC) camera, a medical science, a toy, and so on. That is, the image sensor is widely used in all fields where an image signal is used.

Such an image sensor captures an image of an object and the captured image is displayed on a screen. A picture quality of the displayed image is largely determined depending on a sharpness of an edge. Accordingly, various correction methods for improving the sharpness of the edge of the image have been proposed.

FIG. 1 is a schematic block diagram of a conventional image sensor.

Referring to FIG. 1, the conventional image sensor includes a control and external system interface 11, a pixel array 10, an analog-to-digital converter (hereinafter, referred to as an ADC) 12, a line memory 13, and an image signal processor 14.

The pixel array 10 includes pixels arranged in an N×M matrix and detects an image information. The control and external system interface 11 controls an overall operation of the image sensor by using a finite state machine (FSM), and manages an interface operation for an external system. The control and external system interface 11 includes a batch register (not shown) so that several internal operations can be programmed. Also, the control and external system interface 11 controls an operation of the entire chip according to the programmed information.

Although not shown in FIG. 1, an analog line buffer detects and stores voltages of selected pixels of one row. A data value of a column selected by a column decoder is transferred to a variable amplifier through an analog bus.

If a pixel voltage stored in the analog line buffer is small, the variable amplifier, for example a programmable gain amplifier (PGA), amplifies the pixel voltage. A color correction is performed on the analog data passing through the variable amplifier. Then, the ADC 12 converts the analog data into a digital value.

The line memory stores the digitalized RGB image signals based on the lines. The image signal processor 14 performs an error correction, a color interpolation, a gamma correction, a color space conversion, and so on.

Meanwhile, a fixed pattern noise occurs in the image sensor due to an offset voltage, which is caused by a minute difference in the manufacturing process. In order to compensate for the fixed pattern noise, the image sensor employs a correlated double sampling (hereinafter, referred to as a CDS), which reads reset voltage signals and data voltage signals from the pixels of the pixel array 5
AbsHDiff=abs(HDiff1+HDiff2+HDiff3)

The two absolute values AbsVDiff and AbsHDiff are compared with each other. If the value AbsVDiff is larger than the value AbsHDiff, a value of (G22+G24) is used as the G luminance value of the pixel R23. If the value AbsVDiff is smaller than the value AbsHDiff, a value of (G13+G33) is used as the G luminance value of the pixel R23.

As described above, the present invention can detect an edge by using the RGB Bayer signal prior to the color interpolation, without using the brightness signal (Y). Thus, the image is not affected by the noise occurring in the color interpolation. Also, in the edge detection, the noise caused by the detective pixel or the previous-stage imaging devices can be compensated, so that the edge is detected more correctly.

In addition, the edge detection algorithm and the color interpolation can be achieved at the same time.

The present application contains subject matter related to Korean patent application No. 2004-31989, filed in the Korean Patent Office on May 6, 2004, the entire contents of which being incorporated herein by reference.

While the present invention has been described with respect to the particular embodiments, it will be apparent to those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention as defined in the following claims.

Claims

1. An edge detecting method comprising the steps of:

a) setting a first kernel based on a center pixel in pixel data arranged in a mosaic structure;

b) setting a second kernel based on the center pixel within the first kernel;

c) detecting whether a pixel having a green value in the second kernel is a defective pixel, and, if defective, correcting the pixel;

d) converting all pixels of the second kernel into pixels having green value values;

e) calculating a slope value by using a mask for detecting an edge in the second kernel; and

f) detecting an edge by adding the slope value to a luminance value obtained by a color space conversion.

2. The edge detecting method as recited in claim 1, wherein the first kernel is a 5×5 kernel and the second kernel is a 3×3 kernel.

3. The edge detecting method as recited in claim 1, further comprising the step of:

prior to the step d), determining whether all pixels of the second kernel have the G value green values.

4. The edge detecting method as recited in claim 3, further comprising the steps of:

checking whether the pixel having the G value among the pixels a pixel of the second kernel having the green values is a defective pixel or a noise; and

correcting a luminance value of the corresponding a defective or noise pixel.

5. The edge detecting method as recited in claim 4, wherein the step of correcting the luminance value of the corresponding defective or noise pixel includes the steps of:

setting first and second threshold values according to a luminance value of the center pixel so as to determine whether the center pixel having the G green value in the second kernel is distorted or not;

calculating a difference in luminance values of the center pixel and a pixel having the same G green value;

comparing the difference with the first threshold value;

if the difference is smaller than the first threshold value, increasing a count value representing the number of pixels having the same color characteristic as the center pixel;

determining whether there exists a pixel having the same color characteristic in a position adjacent to the center pixel;

if there is no pixel having the same color characteristic, determining whether a count value representing the number of adjacent pixels whose difference in the luminance value from the center pixel is larger than the first threshold value is zero; and

if the count value is zero, making an edge zero and setting a next 3×3 kernel.

6. The edge detecting method as recited in claim 5, further comprising the steps of:

if the difference in the luminance value is larger than the first threshold value, increasing the count value representing the number of adjacent pixels whose difference in the luminance value from the center pixel is larger than the first threshold value.

7. The edge detecting method as recited in claim 5, further comprising the steps of:

if the count value representing the number of the adjacent pixels whose difference in the luminance value from the center pixel is larger than the first threshold value is not zero, determining whether the count number value representing the number of the adjacent pixels larger than the first threshold value is equal to the count value representing the number of the pixel having the same color characteristic as the center pixel and whether the differences in the luminance values of the center pixel and the pixel having the same G green value; and

if the condition is satisfied, multiplying a weight value by the luminance value of the center pixel according to the distortion of using distorted luminance values of derived from pixels arranged in a previous row.

8. The edge detecting method as recited in claim 7, further comprising the step of:

if the condition is not satisfied, setting the edge to 1 and setting a next second kernel.

9. The edge detecting method as recited in claim 1, wherein the slope value is calculated using a Laplacian filter.

10. The edge detecting method as recited in claim 1, wherein a median filter or an average value is used in the step d).

11. A method for operating an image sensor comprising:

setting a first kernel about a target pixel of an image;

setting a second kernel within the first kernel, wherein the second kernel is configured about the target pixel;

determining whether the target pixel is a green pixel;

correcting the target pixel if it is green and defective;

interpolating an effective green value for all non-green pixels of the second kernel using green values of green pixels adjacent each respective non-green pixel; and

using the green values and the effective green values of the second kernel to detect an edge of the image.

12. The method of claim 11, wherein the first kernel is configured so that the target pixel is centrally disposed within the first kernel.

13. The method of claim 11, wherein the second kernel is configured so that the target pixel is centrally disposed within the second kernel.

14. The method of claim 11, wherein said correcting the target pixel comprises correcting the target pixel if it is green and defective or green and noise.

15. The method of claim 11, wherein said step of using the green values and the effective green values of the second kernel to detect the edge of the image comprises calculating a slope value using a mask for detecting an edge in the second kernel using the green values and the effective green values of the second kernel.

16. The method of claim 15, wherein the step of using the green values and effective green values of the second kernel to detect the edge of the image comprises detecting the edge by adding the slope value to a luminance value obtained by a color space conversion operation.

17. A method comprising:

setting a first kernel and a second kernel, wherein a green pixel is located in the first and second kernels, and wherein the second kernel is configured within the first kernel;

setting first and second threshold values based on a luminance value of the green pixel;

determining whether the luminance value of the green pixel exceeds the second threshold value;

adjusting the first threshold value if the luminance value of the green pixel does not exceed the second threshold value;

determining a first luminance difference value between the green pixel and one or more green pixels of the second kernel;

increasing a first count value if the first luminance difference is greater than the first threshold value;

increasing a second count value; and

using the first count value and the second count value to determine whether the green pixel needs correction.

18. The method of claim 17, wherein the first kernel is configured so that the target pixel is centrally disposed within the first kernel.

19. The method of claim 17, wherein the second kernel is configured so that the target pixel is centrally disposed within the second kernel.

20. The method of claim 17, further comprising:

correcting the target pixel if it is green and needs correction;

interpolating an effective green value for all non-green pixels of the second kernel using green values of green pixels adjacent each respective non-green pixel; and

using the green values and the effective green values of the second kernel to detect the edge of an image.