KR101488641B1 - Image processing apparatus and Image processing method - Google Patents

Abstract

The present invention relates to an image processing apparatus. The image processing apparatus according to an embodiment of the present invention includes: an image decomposition unit which separates the brightness data of an input image into a luminance component and a reflection component; an image correction unit which calculates the gamma curve by using the luminance component and corrects the input image by using the gamma curve; and a detail enhancement unit which uses the reflection component to enhance the details of the corrected input image.

Description

[0001] The present invention relates to an image processing apparatus and an image processing method,

An embodiment relates to an image processing apparatus.

An embodiment relates to an image processing method.

Recently, as the demand of high performance image processing apparatus has increased, image processing techniques for obtaining a high quality image in various environments have been developed.

However, the image sensor for acquiring the image has a problem that information of a bright region and a dark region can not be expressed together due to a limitation of a dynamic range.

The dynamic range means a brightness ratio of the brightest region and the darkest region in the image. The dynamic range used in the image sensor for acquiring the image can not express all the brightness range of the real world scene.

Conventionally, in order to correct the dynamic range, a gamma curve is used to improve a bright region and a dark region of the image. However, in the image enhancement using the gamma curve, excessive improvement may occur in the dark region, .

The embodiment provides an image processing apparatus for improving image quality in a low-illuminance region.

The embodiment provides an image processing method capable of improving detail of an image.

An image processing apparatus according to an embodiment includes an image decomposition unit for extracting an illumination component from brightness data of an input image; And an image correcting unit for calculating a gamma curve using the roughness component and correcting the input image by the gamma curve.

The image correction unit may perform a relatively large correction in a low-illuminance area of the input image, and may perform a correction of a relatively small value in a high-illuminance area of the input image.

에 의해 영상을 보정하고, 상기 r은 상기 입력영상의 조도성분에 의해 결정되는 가중치일 수 있다.The image correction unit

And r may be a weight determined by the illuminance component of the input image.

에 의해 연산되고, 상기 μ는 오프셋 값일 수 있다.The weight

, And the μ may be an offset value.

The offset value may be an average value of the entire illuminance component.

The image decomposition unit may divide brightness data of the input image into the illumination component and the reflection component.

And a detail improvement unit for improving the details of the corrected input image.

The detail improvement unit may improve the details of the corrected input image using the reflection component extracted from the image decomposition unit.

The detail improvement unit may improve an edge area of the corrected input image.

And an HSV converter for converting RGB data of the input image into HSV data.

And an RGB converter for converting the HSV data of the corrected input image into RGB data.

According to an embodiment of the present invention, there is provided an image processing method comprising: extracting an illuminance component from brightness data of an input image; And calculating a gamma curve using the roughness component, and correcting the input image by the gamma curve.

에 의해 영상을 보정하고, 상기 r은 상기 입력영상의 조도성분에 의해 결정되는 가중치일 수 있다.Wherein the step of correcting the input image comprises:

And r may be a weight determined by the illuminance component of the input image.

And further improving the details of the corrected input image.

The image processing apparatus according to the embodiment can calculate the gamma curve using the illuminance component and correct the input image thereby improving the image quality in the low-illuminance region.

The image processing method according to the embodiment can improve the image quality by improving the detail of the input image corrected using the reflection component.

1 is a view showing an image processing apparatus according to the first embodiment.
2 is a view showing an image separated according to the first embodiment.
3 is a diagram showing a gamma curve calculated by the image correction unit according to the first embodiment.
4 is a view showing an image corrected according to the first embodiment.
5 is a block diagram showing an image processing apparatus according to the second embodiment.
6 is a diagram illustrating an image enhanced by the detail improvement unit of the image processing apparatus according to the second embodiment.
7 is a flowchart showing an image processing method according to the embodiment.
8 is a diagram showing an improved image after performing image processing according to an embodiment.

Hereinafter, specific embodiments of the present invention will be described in detail with reference to the drawings. It will be apparent to those skilled in the art that various modifications and variations can be made in the present invention without departing from the spirit or scope of the inventive concept. Other embodiments falling within the scope of the inventive concept may readily be suggested, but are also considered to be within the scope of the present invention.

The same reference numerals are used to designate the same components in the same reference numerals in the drawings of the embodiments.

1 is a view showing an image processing apparatus according to the first embodiment.

1, the image processing apparatus 1 according to the first embodiment may include an HSV conversion unit 10, an image decomposition unit 20, an image correction unit 30, and an RGB conversion unit 40 have.

An input image is input to the image processing apparatus 1. The input image is input as RGB data.

The HSV converter 10 converts the input image composed of the RGB data into HSV data. In the HSV data, H means Hue, S means Saturation, and V means Value. The H (Hue) has a value of 0 ° or more and less than 360 °, S (Saturation) has a value of 0 or more and 1 or less, and V (Value) has a value of 0 or more and 1 or less. The HSV converting unit 10 converts the input image of RGB data into HSV data as shown in Equations (1) to (6).

V can be calculated using Equation (1), and the V obtained through the HSV converter 10 is transferred to the image decomposition unit 20. The image decomposition unit 20 may extract an illumination component from the luminance (V) data of the HSV data according to the Retinex theory. The image decomposition unit 20 may extract an illumination component from the brightness V data and transmit the extracted illumination component to the image correction unit 30.

The Retinex Theory is based on the theory that the color and brightness of a specific region recognized by a human retina are independent of the intensity of the illumination and are determined by the ratio to the value of the surrounding region. The logarithmic function is used to convert the brightness data (V) of the input image into an illumination component and a reflectance component according to Equation (8).

In Equation (2), V (x, y) denotes brightness data of the input image, I (x, y) denotes an illuminance component of brightness data of the input image, and R Means the reflection component of the brightness data of the input image, and x and y denote the coordinates of the pixel existing in the image.

The brightness data (V) of the input image can be separated into an illuminance component and a reflection component according to Equation (2) as shown in FIG.

The illuminance component of the input image can be calculated by the following equation (3).

In Equation (3), G (x, y) can be calculated by the following Equation (4) as a Gaussian function.

In Equation (4), κ denotes a normalization constant, and σ denotes a standard deviation.

The image decomposition unit 20 transmits the illumination component to the image correction unit 30. The image correcting unit 30 calculates a gamma curve using the illuminance component of the brightness data of the input image, and corrects the input image by the gamma curve. The image correction unit 30 may calculate the gamma curve based on the illumination component.

The image correcting unit 30 may calculate a gamma curve by the following equation (5).

In Equation (5), V '(x, y) denotes brightness data of the corrected input image. In Equation (5), r denotes a weight. The weight r is determined by the illuminance component of the image. The weight r can be calculated by the following equation (6).

In Equation (6),? Denotes an offset value. The offset value may be an average value of the entire illuminance component. The weight r reflects the illuminance component of each pixel. The corrected brightness data V '(x, y) is compared with the brightness data V (x, y) before correction if the weight r has a large value according to Equations 5 and 6, (X, y) after the correction has a smaller value than the brightness data V (x, y) before correction, if the difference has a large value and the weight r has a small value. Value.

The image correction unit 30 may transmit the corrected image data to the RGB conversion unit 40. [

Since the image data corrected by the image correction unit 30 is HSV data reflecting the improved V, the RGB conversion unit 40 converts the corrected image data composed of the HSV data into RGB data and outputs the RGB data.

4 is a view showing an image corrected according to the first embodiment.

4, when an image obtained by correcting an input image by applying a conventional gamma curve and an image obtained by applying a gamma curve according to the first embodiment are compared with each other, And is outputted as an image similar to the input image in the high contrast region.

In the case of the correction of the image according to the first embodiment, the bright image is corrected to a bright image to reveal the detail, and in the high image area, the image is outputted as an image similar to the input image to maintain the detail.

5 is a block diagram showing an image processing apparatus according to the second embodiment.

The image processing apparatus according to the second embodiment is the same as the first embodiment except that it further includes a detail improvement unit as compared with the first embodiment. Therefore, in describing the second embodiment, the same reference numerals are assigned to the same components as those of the first embodiment, and a detailed description thereof is omitted.

5, the image processing apparatus according to the second embodiment includes an HSV conversion unit 10, an image decomposition unit 20, an image correction unit 30, a detail improvement unit 35, and an RGB conversion unit 40, . ≪ / RTI >

The HSV converter 10 converts the input image composed of the RGB data into HSV data.

The image decomposition unit 20 may extract illumination (V) data from the HSV data according to the Retinex theory. The image decomposition unit 20 may extract an illumination component from the brightness V data and transmit the extracted illumination component to the image correction unit 30.

The image correcting unit 30 calculates a gamma curve using the illuminance component of the brightness data of the input image, and corrects the input image by the gamma curve.

The image correction unit 30 may transmit the corrected image data to the detail improvement unit 35. [

The detail improvement unit 35 improves the details of the corrected image data using the reflection component acquired by the image decomposition unit 20. [

The detail improvement unit 35 improves the detail of the image data according to Equation (7) and outputs the improved image data D (x, y) to the RGB conversion unit 40. Referring to Equation (7), the detail improvement unit 35 calculates the brightness of the input image by adding the corrected brightness data V '(x, y) and the logarithm of the reflection component R (x, y) It is possible to calculate the image data D (x, y).

Since the reflection component R (x, y) includes a high frequency component such as an edge area of the image, the detail improvement unit 35 corrects the brightness data (R (x, y) V '(x, y)), thereby improving the edge area of the image.

6 is a diagram illustrating an image enhanced by the detail improvement unit of the image processing apparatus according to the second embodiment.

The detail improvement section 35 applies the logarithm of the reflection component R (x, y) to the brightness data V '(x, y) after the correction by the image correction section 30 of the second embodiment In addition, improved image data D (x, y) with improved detail can be obtained.

The details such as the edge area of the image being clarified by the detail improvement unit 35 are improved and a clear corrected image can be outputted.

Table 1 is a table showing the degree of image quality improvement of the image corrected by the image processing apparatus according to the second embodiment.

Image processing method VCM value Existing video 0.01108 MSRCR 0.02077 NRCIR 0.02027 Second Embodiment 0.02308

In Table 1, the VCM value (visual contrast measure) is a numerical value of brightness and contrast which can be observed with the naked eye. The VCM value may be calculated by Equation (8).

In Equation (8), N denotes the number of masks and is equal to the total number of pixels of the image. i denotes an index of the mask, s _i denotes a standard deviation of the mask having the index, and m _i denotes an average value of the mask having the index.

In Table 1, MSRCR (Multi Scale Retinex with Color Restoration) and NRCIR (Natural Rendering of Color Image Based on Retinex) are types of conventional image enhancement methods.

Referring to Table 1, since the MSRCR, the NRCIR, and the second embodiment have higher VCM values than the existing image, it can be confirmed that the method is improved in contrast compared to the existing image. However, since the image enhancement method according to the second embodiment has a larger VCM value than the image enhancement method of the MSRCR and the NRCIR, the image enhancement method according to the second embodiment has a clearer image quality than the conventional image enhancement method Can be output.

Table 2 is a table showing the processing speed of the image processing apparatus according to the second embodiment.

Image processing method Average throughput (50 images) MSRCR 4.3920 sec. NCRIR 0.8497 sec. Second Embodiment 0.2042 sec.

In Table 2, the processing speed represents an average processing speed of 50 images.

Referring to Table 2, it can be seen that the processing speed of the image enhancement method performed in the image processing apparatus according to the second embodiment is faster than that of MSRCR and NCRIR. Accordingly, the image processing apparatus 1 according to the second embodiment has an effect of processing images at a higher speed than the conventional image improving method.

7 is a flowchart showing an image processing method according to the embodiment.

Referring to FIG. 7, an image processing method according to an embodiment includes converting an input image into HSV data. (S110)

The HSV converter 10 receives an input image of RGB data and converts it into HSV data. The HSV converter 10 transfers the HSV-converted input image to the image decomposer 20.

The image decomposition unit 20 decomposes the HSV data received from the HSV converter 10. (S120)

The image decomposition unit 20 may extract an illumination component from the luminance (V) data of the HSV data according to the Retinex theory. The image decomposition unit 20 may extract an illumination component from the brightness V data and transmit the extracted illumination component to the image correction unit 30.

The image correcting unit (30) corrects the image based on the illuminance component. (S130)

The image correcting unit 30 calculates a gamma curve using the illuminance component of the brightness data of the input image, and corrects the input image by the gamma curve. The image correction unit 30 may calculate the gamma curve based on the illumination component.

The image correcting unit 30 can improve the detail of the input image corrected by the gamma curve. The details of the corrected image data can be improved by using the reflection component acquired by the image decomposition unit 20. [

The image correction unit 30 transmits the corrected input image to the RGB conversion unit 40. [

The RGB conversion unit 40 converts the image data corrected by the image correction unit 30 into RGB data and outputs the RGB data. (S140)

8 is a diagram showing an improved image after performing image processing according to an embodiment. By the image processing method according to the embodiment, the brightness of the image in the low-illuminance area is improved, the detail is improved, and the processing speed is improved.

While the present invention has been described with reference to exemplary embodiments, it is to be understood that the invention is not limited to the disclosed exemplary embodiments, but, on the contrary, It will be apparent to those skilled in the art that changes or modifications may fall within the scope of the appended claims.

1: Image processing device
10: HSV converter
20:
30: image correction unit
35: Detail improvement section
40: RGB conversion section

Claims (14)

An image decomposition unit for separating brightness data of an input image into an illumination component and a reflection component;
An image correction unit for calculating a gamma curve using the illumination component and correcting the input image by the gamma curve; And
And a detail improvement unit for improving the detail of the input image corrected using the reflection component,
Wherein the image correction unit performs a relatively large correction in a low-luminance area of the input image by the gamma curve, and performs correction of a relatively small value in a high-luminance area of the input image. delete The method according to claim 1,
The image correction unit

To correct the image,
And r is a weight determined by an illuminance component of the input image. The method of claim 3,
The weight

Lt; / RTI >
And? Is an offset value. 5. The method of claim 4,
Wherein the offset value is an average value of the entire illuminance component. delete delete delete The method according to claim 1,
Wherein the detail improvement unit improves the edge area of the corrected input image. The method according to claim 1,
And an HSV converter for converting RGB data of the input image into HSV data. The method according to claim 1,
And an RGB conversion unit for converting HSV data of the corrected input image into RGB data. Separating brightness data of an input image into an illumination component and a reflection component;
Calculating a gamma curve using the illumination component, and correcting the input image by the gamma curve; And
And improving the detail of the input image corrected using the reflection component,
Wherein the step of correcting the input image comprises performing a correction of a relatively large value in the low-luminance region of the input image by the gamma curve and performing a correction of a relatively small value in the high-luminance region of the input image. 13. The method of claim 12,
Wherein the step of correcting the input image comprises:

To correct the image,
And r is a weight determined by an illuminance component of the input image. delete

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