KR20200103348A - Apparatus for recognizing object and Method thereof - Google Patents

Abstract

The present disclosure relates to an object recognition device and to an object recognition method. Specifically, according to the present disclosure, the object recognition device comprises: a luminance calculation unit receiving a detected image and calculating the luminance of the image; a luminance correction unit determining whether the luminance is included in any one of a plurality of preset luminance ranges, and correcting the luminance based on a luminance correction process corresponding to the luminance range including the luminance; and an object recognition unit recognizing an object corresponding to an object image learned in advance from the image whose luminance is corrected. The object recognition device of the present disclosure provides an effect of accurately recognizing an object by appropriately correcting the luminance of an image.

Description

Object recognition apparatus and object recognition method TECHNICAL FIELD

The present disclosure relates to an object recognition apparatus and an object recognition method. In more detail, the present disclosure relates to a technique in which a process of calculating the luminance of an image and correcting luminance according to a specific value of the luminance is applied differently.

Nowadays, technology for accurately recognizing a desired object from an image captured by a camera or the like is increasing.

In general, object recognition technology divides a specific area from a specific image into an anchor box, a bounding box, etc., filters the images included in the separated area, and pre-learns the stored object image and It is implemented by comparing.

At this time, the luminance of the photographed image changes due to the surrounding environment and the performance of the sensor, but when the luminance is relatively low, it is difficult to recognize the object. Accordingly, there is a demand for a technique for correcting the brightness of an image or effectively mapping the tone of an image.

However, in the related art, since the luminance of the image is not considered and corrected, there is a problem that a recognizable object is not recognized.

The present disclosure devised from the above-described background attempts to accurately recognize an object by appropriately correcting the luminance of an image.

In addition, the present disclosure is intended to provide driving stability and driving convenience to a driver by accurately recognizing an object in an image.

An embodiment for solving the above-described problem is a luminance calculator that receives a detected image and calculates the luminance of the image, and determines whether the luminance is included in any one luminance range among a plurality of preset luminance ranges, and Provides an object recognition apparatus including a luminance correcting unit for correcting luminance based on a luminance correction process corresponding to the luminance range including the luminance range, and an object recognition unit for recognizing an object corresponding to an object image previously learned from the luminance corrected image do.

In addition, another embodiment includes a luminance calculation step of receiving a detected image and calculating the luminance of the image, a determination step of determining whether the luminance is included in any one of a plurality of preset luminance ranges, and luminance. Provides an object recognition method including a luminance correction step of correcting luminance based on a luminance correction process corresponding to the set luminance range, and an object recognition step of recognizing an object corresponding to an object image previously learned from an image whose luminance is corrected. .

As described above, the present disclosure provides an effect of accurately recognizing an object by appropriately correcting the luminance of an image.

In addition, the present disclosure provides an effect of providing driving stability and driving convenience to a driver by accurately recognizing an object in an image.

1 is a block diagram schematically illustrating an object recognition system according to the present disclosure.
2 is a block diagram schematically illustrating an object recognition apparatus according to the present disclosure.
3 is a flowchart illustrating a method of operating an object recognition apparatus according to the present disclosure.
4 is a flowchart illustrating an embodiment of a method for correcting luminance according to the present disclosure.
5 is a flowchart illustrating another embodiment of a method for correcting luminance according to the present disclosure.
6 and 7 are graphs showing a correction function according to the present disclosure.
8 is a diagram illustrating an object recognizable according to the present disclosure.
9 is a diagram showing images representing a result of recognizing an object according to the prior art and each of the present disclosure.

Hereinafter, some embodiments of the present disclosure will be described in detail through exemplary drawings. In describing the constituent elements of the present disclosure, terms such as first, second, A, B, (a), and (b) may be used. These terms are only used to distinguish the component from other components, and the nature, order, or order of the component is not limited by the term. When a component is described as being "connected", "coupled" or "connected" to another component, the component may be directly connected or connected to that other component, but another component between each component It should be understood that elements may be “connected”, “coupled” or “connected”.

1 is a block diagram schematically illustrating an object recognition system 10 according to the present disclosure.

Referring to FIG. 1, the object recognition system 10 according to the present disclosure detects the surroundings of a vehicle such as a vehicle, recognizes a specific object existing around the vehicle, and displays the recognized specific object. It can mean a system to do.

The object recognition system 10 may include an image sensor 100, an object recognition device 200, a display device 300, and the like.

The image sensor 100 may sense the surroundings of a vehicle such as a vehicle, and may generate and output an image of the detection result. That is, the image sensor 100 may capture and output image data with a view of the exterior of the vehicle.

The image sensor 100 may be mounted on the front, side or rear of the vehicle. For example, the image sensor 100 may be disposed on the front part of the vehicle to have a view of the front of the vehicle.

The image sensor 100 may mean a camera, a Light Detection and Ranging (LiDAR) sensor, or the like. However, the present invention is not limited thereto, and the image sensor 100 may include any sensor that detects an object using an optical signal or a light source.

Since the image information captured by the image sensor 100 is composed of image data, it may mean image data captured by the image sensor. Hereinafter, in the present disclosure, image information captured from an image sensor means image data captured from an image sensor. Image data captured by the image sensor may be generated, for example, in one of raw format AVI, MPEG-4, H.264, DivX, and JPEG.

Meanwhile, the image sensor 100 may output image data of the captured image to the object recognition device 200 and may output the image data to the display device 300.

The object recognition apparatus 200 may receive an image from the image sensor 100 and recognize a specific object from the received image. Specifically, the object recognition apparatus 200 may learn various objects included in the received image, compare them with a specific object stored therein, and recognize a specific object by classifying and extracting objects corresponding to the learned object.

Here, a specific object may be variously designated by a designer or a driver. For example, the specific object may refer to a preceding vehicle, a pedestrian, an animal, a guard rail, a traffic light, a traffic sign, a road, etc. running in front of the vehicle. However, it is not limited thereto.

A detailed operation method of the object recognition apparatus 200 will be described later with reference to FIGS. 2 to 7.

The object recognition device 200 includes hardware such as a computer including CPU, Ram, Linux operating system, processor, and D-patches, RANSAC method, You Only Look Once (YOLO), Convolution Neural Network (CNN) It can be implemented in software such as.

Here, the processor is hardware, ASICs (application specific integrated circuits), DSPs (digital signal processors), DSPDs (digital signal processing devices), PLDs (programmable logic devices), FPGAs (field programmable gate arrays), controllers (controllers) , Micro-controllers, microprocessors, and the like, may be implemented using at least one of electrical units capable of processing image data and performing other functions.

Meanwhile, the object recognition device 200 may reflect information on the recognized specific object to an image and output image data to the display device 300.

The display device 300 may receive and display image data from the image sensor 100 or may receive and display image data from the object recognition device 200.

The display device 300 may be disposed inside a vehicle such as a vehicle so that the driver and the occupant can easily recognize it.

The display device 300 may be implemented as a display panel, a gate driving circuit, a data driving circuit, a gate line, a data line, a plurality of sub-pixels, and a controller. However, it is not limited thereto.

Hereinafter, the object recognition apparatus 200 according to the present disclosure will be described in detail.

2 is a block diagram schematically illustrating an object recognition apparatus 200 according to the present disclosure.

Referring to FIG. 2, the object recognition apparatus 200 according to the present disclosure may include a luminance calculating unit 210, a luminance correcting unit 220, an object recognition unit 230, and the like.

The luminance calculator 210 may receive an image sensed by the image sensor 100 and calculate luminance of the image.

For example, the luminance calculation unit 210 may calculate the luminance of an image by using a mathematical model expressed as a product of an illumination function and a reflection characteristic function. However, the present invention is not limited thereto, and the luminance of the image may be calculated by software or an algorithm related to image processing.

Here, the unit of luminance may be expressed as candela per area (Cd/m2) or nit. However, it is not limited thereto.

The luminance correcting unit 220 may determine whether the luminance is included in any one of a plurality of preset luminance ranges, and correct the luminance based on a luminance correction process corresponding to the luminance range including the luminance.

For example, when the luminance of the image calculated by the luminance calculation unit 210 is included in the first luminance range, the luminance correction unit 220 corrects the luminance based on the first luminance correction process.

The preset luminance range may mean a certain range having a minimum value and a maximum value. In addition, the luminance range may be divided into at least two in the entire luminance range. For example, when the entire luminance range includes a range of 0 to 100, the first luminance range may be 0 to 50, and the second luminance range may be 51 to 100. However, it is not limited thereto.

The scale of the entire luminance range may be a scale corresponding to an actual luminance value, but may be a scale smaller than the original scale in consideration of computation speed, storage capacity, and the like. For example, the scale of the entire luminance range may be preset by scaling with a real number of 0 or more and 1 or less. However, it is not limited thereto.

Meanwhile, when the scale of the entire luminance range is 0 or more and 1 or less, the luminance or luminance unit of the image and the scale of the entire luminance range may not match. Accordingly, the total luminance corrector 220 calculates and scales the luminance of the image so that the calculated luminance of the image corresponds to the scale of the entire luminance range, and may compare the scaling value with the luminance range. A specific method will be described later with reference to FIGS. 4 to 7.

The luminance correction process may mean an algorithm for correcting luminance, and the luminance correction process may include a correction function for correcting luminance.

When the luminance of the image is corrected by the luminance correction unit 220, the tone of the corrected image may be brighter.

The object recognition unit 230 may recognize an object corresponding to the previously learned object image from the image whose luminance is corrected.

For example, when an image for a traffic sign exists in the image, the object recognition unit 230 recognizes the learned and stored traffic sign image and the corresponding object within the image.

Here, the learning method used by the object recognition unit 230 may be generally machine learning. In this case, the algorithm for machine learning may be, for example, YOLO, CNN, Faster R-CNN, Deep Learning, Two-Stream Multirate Recurrent Neural Network, SSD model, and the like.

As described above, according to the present disclosure, the object recognition apparatus 200 according to the present disclosure provides an effect of accurately recognizing an object by performing a luminance correction process differently according to the luminance of an image.

3 is a flowchart illustrating a method of operating an object recognition apparatus according to the present disclosure.

3, in step S310, an image is received. For a specific example, the object recognition apparatus 200 receives image data for an image from the image sensor 100.

Then, in step S320, the luminance of the image is calculated. For a specific example, the object recognition apparatus 200 calculates the luminance of an image through the above-described image processing algorithm using the received image data.

Then, in step S330, it is determined whether the calculated luminance is included in any one of the preset luminance ranges. For a specific example, when the luminance of the image is 30 and the entire luminance range having a range from 0 to 100 is equally divided into the first to fourth luminance ranges, the object recognition apparatus 200 may use the luminance (for example, 30) is determined to be included in the second luminance range (eg, 25 to 50 range).

When the luminance range is determined, in step S340, the luminance of the image is corrected based on the luminance correction process according to the specific luminance range. For a specific example, the object recognition apparatus 200 corrects the luminance of the original image to a greater luminance based on the first luminance correction process corresponding to the first luminance range.

When the luminance of the image is corrected, in step S350, an object corresponding to the learned and stored object image is recognized among the objects included in the corrected image. For a specific example, the object recognition apparatus 200 recognizes a road sign from a corrected image using image data of a road sign that has been learned and stored in advance.

Hereinafter, various embodiments of a method for correcting luminance will be described in detail.

4 is a flowchart illustrating an embodiment of a method for correcting luminance according to the present disclosure.

Referring to FIG. 4, the object recognition apparatus 200 according to the present disclosure may divide the entire luminance range into first to third luminance ranges and set in advance. Then, the object recognition apparatus 200 calculates a first luminance of the received image (S410), and calculates a first scaling value by scaling the first luminance (S410).

For a specific example, the luminance correction unit 220 calculates a first scaling value by scaling the first luminance so that the first luminance of the image corresponds to the scale of the entire luminance range.

When the first scaling value is calculated, the object recognition apparatus 200 determines whether the first scaling value is included in a specific luminance range, and substitutes the first scaling value into a correction function according to the specific luminance range.

For example, the object recognition apparatus 200 determines whether the first scaling value is included in the first luminance range (S431), and if the first scaling value is included in the first luminance range, the first scaling value is added to the first correction function. Substitute (S441). As a result of substituting the first scaling value into the first correction function, a second scaling value is calculated. Here, the second scaling value may mean a value corresponding to the corrected luminance.

For a specific example, if the first scaling value is included in the first luminance range, the luminance correction unit 220 calculates the second scaling value by substituting the first scaling value into the first correction function corresponding to the first luminance range. do.

Meanwhile, when the first scaling value is not included in the first luminance range, the object recognition apparatus 200 determines whether the first scaling value is included in the second luminance range (S432), and the first scaling value is the second luminance range. If included in the range, the first scaling value is substituted into the second correction function (S442).

For a specific example, when the first scaling value is included in the second luminance range, the luminance correction unit 220 calculates the second scaling value by substituting the first scaling value into the second correction function corresponding to the second luminance range. do.

If the first scaling value is also not included in the second luminance range, it may be determined that the first scaling value is included in the third luminance range. Accordingly, the object recognition apparatus 200 calculates the second scaling value by substituting the first scaling value into the third correction function (S443).

When the second scaling value is calculated by substituting the first scaling value into the correction function corresponding to each luminance range, the object recognition apparatus 200 calculates the second luminance by reverse scaling the second scaling value (S450).

Here, the reverse scaling may refer to an operation that reverses an operation of scaling the luminance.

For a specific example, the luminance corrector 220 calculates the second luminance by reverse scaling the second scaling value.

As shown in FIG. 4, the entire luminance range according to the present disclosure is divided into first to third luminance ranges, but is intended to aid understanding of the present disclosure and is not limited thereto.

As described above, the object recognition apparatus 200 according to the present disclosure may recognize a specific object included in a tone-corrected image without omission by appropriately correcting the luminance according to the luminance range.

Meanwhile, the luminance range according to the present disclosure may be classified by a reference luminance corresponding to a boundary point between the range and the range. Here, the reference luminance is luminance included in the entire luminance range, and may mean a threshold predetermined by a designer.

Accordingly, the method of correcting the luminance according to the present disclosure may substitute whether the luminance is included in a specific luminance range by comparing the luminance and the reference luminance.

5 is a flowchart illustrating another embodiment of a method for correcting luminance according to the present disclosure.

Referring to FIG. 5, the object recognition apparatus 200 according to the present disclosure calculates a first luminance of a received image (S510), and calculates a first scaling value by scaling the first luminance (S520).

Then, the object recognition apparatus 200 compares the first scaling value with a preset reference luminance (S530).

For a specific example, the luminance correction unit 220 compares the first luminance and the reference luminance of the image by presetting the reference luminance so that the entire luminance range is divided into two luminance ranges.

If the first scaling value is less than or equal to the reference luminance, the object recognition apparatus 200 calculates a second scaling value by substituting the first scaling value into the nonlinear correction function (S541).

If the first scaling value is greater than the reference luminance, the object recognition apparatus 200 calculates a second scaling value by substituting the first scaling value into the linear correction function (S542).

When the second scaling value is calculated by substituting the first scaling value into the correction function corresponding to each luminance range, the object recognition apparatus 200 calculates the second luminance by reverse scaling the second scaling value (S550).

As shown in FIG. 5, in the present specification, the contents of the present disclosure have been described using one reference luminance, but the present disclosure is not limited thereto. Embodiments for more than one reference luminance are possible.

As described above, the object recognition apparatus 200 according to the present disclosure corrects luminance based on a correction function set according to a luminance range, thereby providing an effect of correcting luminance at a faster calculation speed.

Meanwhile, details of the above-described nonlinear correction function and linear correction function will be described later with reference to FIGS. 6 and 7. However, in the following description, for convenience of description, two luminance ranges are used and the scaling is from 0 to 1.

6 and 7 are graphs showing a correction function according to the present disclosure.

Referring to FIG. 6, the horizontal axis of the graph shown in FIG. 6 is a first scaling value obtained by scaling a first luminance of an image, and a vertical axis denotes a second scaling value obtained by scaling the corrected second luminance.

Here, the threshold value corresponding to the above-described reference luminance is displayed on the horizontal axis, and the total luminance range from 0 to 1 is divided into a first luminance range and a second luminance range by the threshold value. That is, the first luminance range includes the range from 0 to the threshold value, and the second luminance range includes the range from the threshold value to 1.

In this case, if the first luminance is less than or equal to the reference luminance, the luminance correcting unit 220 may correct the first luminance to the second luminance based on a nonlinear correction function included in the first luminance correction process.

For a specific example, when the first scaling value is included in the first luminance range, the luminance correction unit 220 calculates the second scaling value by substituting the first scaling value into the nonlinear correction function.

Here, the nonlinear correction function may be a function including a ratio of a sum of the first luminance and the preset tone mapping parameter and the sum of the constant and tone mapping parameters, and the first luminance. That is, the nonlinear correction function can be expressed as Equation 1 below.

[Equation 1]

Here, Nlum means a second scaling value or a second luminance, Slum means a first scaling value or a first luminance, and C means a tone mapping parameter.

In addition, the nonlinear correction function may be a function in which the second luminance increases when the first luminance increases, but the increase rate of the second luminance decreases.

Meanwhile, when the first luminance is greater than the reference luminance, the luminance corrector 220 may correct the first luminance to the second luminance according to a linear correction function included in the second luminance correction process.

For a specific example, when the first scaling value is included in the second luminance range, the luminance correcting unit 220 calculates the second scaling value by substituting the first scaling value into the linear correction function.

Here, the linear correction function may be a function that increases the second luminance when the first luminance increases, and is continuous with the nonlinear correction function based on the reference luminance. For example, the nonlinear correction function and the linear correction function meet at the first coordinate P1 obtained by substituting a threshold value corresponding to the reference luminance to the nonlinear correction function.

Meanwhile, when the maximum value of the second luminance corresponding to the maximum value of the entire luminance range is preset, the linear correction function includes coordinates including the second luminance calculated by reflecting the reference luminance in the reference luminance and nonlinear correction function, and the entire luminance range It may be a function passing through coordinates including the maximum value of and the maximum value of the second luminance.

For example, if both the maximum value of the first scaling value and the maximum value of the second scaling value are 1 and the second coordinate P1 for them is (1,1), the linear correction function is the first coordinate P1 and the second It is a function that passes through the coordinate P2.

As described above, when the linear correction function passes two points, a specific functional expression of the linear correction function can be obtained. That is, when the linear correction function is expressed according to Equation 2 below, and two points through which the linear correction function passes are known, the slope and y-intercept of the linear correction function can be calculated.

[Equation 2]

Here, a is the slope of the linear correction function, and b is the y-intercept.

On the other hand, referring to FIG. 7, when the reference luminance (or the threshold value by which the reference luminance is scaled) is the same and a plurality of correction functions are compared by different values of the aforementioned tone mapping parameter, the value of the tone mapping parameter increases. As the value increases, the second scaling value corresponding to the second luminance decreases.

Meanwhile, in the case of a vehicle to which the above-described object recognition system 10 is applied, for example, a vehicle, road signs existing relatively far from the front during driving can be accurately recognized.

8 is a diagram illustrating an object recognizable according to the present disclosure as an example, and FIG. 9 is a diagram illustrating images representing a result of recognizing an object according to the prior art and the present disclosure.

Objects included in the image according to the present disclosure are various as described above. Among them, in order to recognize only a specific object, a previously learned object image is required. In addition, the learned object image needs to be classified and stored in a specific group.

Referring to FIG. 8, for example, when an object image stored through learning is a traffic sign image, it may be generally classified into a Danger Class, a Mandatory Class, and a Prohibitory Class. However, it is not limited thereto.

The object recognition apparatus 200 according to the present disclosure may store a dataset for an object image as illustrated in FIG. 8.

On the other hand, Figure 9 (a) shows the original image captured by the image sensor 100, Figure 9 (b) is a diagram for an embodiment of recognizing an object without correcting the luminance of the image. 9(c) is a diagram of an embodiment of recognizing an object by correcting the luminance of an image according to the prior art, and FIG. 9(d) is a diagram of an embodiment of recognizing an object according to the present disclosure.

Referring to (a) of FIG. 9, among traffic signs included in the image, a specific traffic sign is relatively dark due to the sky, and other traffic signs are relatively bright.

Referring to FIG. 9B, as described above, a relatively bright traffic sign can be easily recognized, but a relatively dark traffic sign is not recognized.

At this time, if the luminance of the image is not corrected differently according to the luminance correction process according to the present disclosure, a traffic sign that looked relatively dark in the original image is recognized as shown in Fig. 9(c), and is relatively bright in the original image. The traffic signs seen are rather not recognized.

Accordingly, the object recognition apparatus 200 according to the present disclosure may recognize all traffic signs that are relatively dark or bright as shown in FIG. 9D.

As described above, the present disclosure provides an effect of accurately recognizing an object by appropriately correcting the luminance of an image.

In addition, the present disclosure provides an effect of providing driving stability and driving convenience to a driver by accurately recognizing an object in an image.

In the above, even if all the constituent elements constituting the embodiments of the present disclosure have been described as being combined into one or operating in combination, the present disclosure is not necessarily limited to these embodiments. That is, as long as it is within the scope of the object of the present disclosure, one or more of the components may be selectively combined and operated. The above description is merely illustrative of the technical idea of the present disclosure, and those of ordinary skill in the art to which the present disclosure pertains will be able to make various modifications and variations without departing from the essential characteristics of the present disclosure. The scope of protection of the present disclosure should be interpreted by the claims below, and all technical ideas within the scope equivalent thereto should be construed as being included in the scope of the present disclosure.

10: object recognition system
100: image sensor
200: object recognition device
210: luminance calculation unit
220: luminance correction unit
230: object recognition unit
300: display device

Claims (13)

A luminance calculator that receives the detected image and calculates a luminance of the image;
A luminance correction unit that determines whether the luminance is included in any one of a plurality of preset luminance ranges, and corrects the luminance based on a luminance correction process corresponding to the luminance range including the luminance; And
An object recognition apparatus comprising an object recognition unit for recognizing an object corresponding to an object image learned in advance from the luminance-corrected image. The method of claim 1,
The luminance correction unit,
A first scaling value is calculated by scaling the first luminance so that the first luminance of the image corresponds to the scale of the entire luminance range,
When the first scaling value is included in a specific luminance range, a second scaling value is calculated by substituting the first scaling value into a correction function corresponding to the specific luminance range,
And calculating a second luminance by reverse scaling the second scaling value, and applying the second luminance to the image. The method of claim 1,
The luminance correction unit,
A reference luminance is preset so that the entire luminance range is divided into two luminance ranges, and the first luminance of the image is compared with the reference luminance,
If the first luminance is less than or equal to the reference luminance, the first luminance is corrected to a second luminance based on a nonlinear correction function included in a first luminance correction process,
When the first luminance is greater than the reference luminance, the first luminance is corrected to a second luminance according to a linear correction function included in a second luminance correction process. The method of claim 3,
The nonlinear correction function is,
The object recognition apparatus, characterized in that as a function of increasing the second luminance when the first luminance increases, and decreasing the rate of increase of the second luminance. The method of claim 3,
The nonlinear correction function is,
An object recognition apparatus comprising: a ratio of a sum of the first luminance and a preset tone mapping parameter and a ratio of the sum of the tone mapping parameters and the first luminance. The method of claim 3,
The linear correction function,
When the first luminance increases, the second luminance increases, and the object recognition apparatus is a function continuous with the nonlinear correction function based on the reference luminance. The method of claim 6,
The linear correction function,
When the maximum value of the second luminance corresponding to the maximum value of the entire luminance range is set in advance, a coordinate including the reference luminance and a second luminance calculated by reflecting the reference luminance to the nonlinear correction function, and the total luminance range An object recognition device, characterized in that it is a function passing through a coordinate including a maximum value and a maximum value of the second luminance. A luminance calculation step of receiving the detected image and calculating a luminance of the image;
Determining whether the luminance is included in any one of a plurality of preset luminance ranges;
A luminance correction step of correcting the luminance based on a luminance correction process corresponding to the luminance range including the luminance; And
And an object recognition step of recognizing an object corresponding to the previously learned object image from the luminance-corrected image. The method of claim 8,
The determining step,
A first scaling value is calculated by scaling the first luminance so that the first luminance of the image corresponds to the scale of the luminance range,
Check a specific luminance range in which the first scaling value is included,
The luminance correction step,
When the first scaling value is included in the specific luminance range, a second scaling value is calculated by substituting the first scaling value into a correction function corresponding to the specific luminance range,
And calculating a second luminance by reverse scaling the second scaling value, and applying the second luminance to the image. The method of claim 8,
The determining step,
A reference luminance is preset so that the entire luminance range is divided into two luminance ranges, and the first luminance of the image is compared with the reference luminance,
The luminance correction step,
If the first luminance is less than or equal to the reference luminance, the first luminance is corrected to a second luminance based on a nonlinear correction function included in a first luminance correction process,
If the first luminance is greater than the reference luminance, the first luminance is corrected to a second luminance according to a linear correction function included in a second luminance correction process. The method of claim 10,
The nonlinear correction function is,
And a function of increasing the second luminance when the first luminance increases, and decreasing the rate of increase of the second luminance. The method of claim 10,
The nonlinear correction function is,
And a ratio of a sum of the tone mapping parameters and a constant to the sum of the first luminance and a preset tone mapping parameter, and a function including the first luminance. The method of claim 10,
The linear correction function,
When the first luminance increases, the second luminance increases, and is a function continuous with the nonlinear correction function based on the reference luminance.

Images