Patent Description:
According to statistics-based color constancy technology, a light is estimated using statistical characteristics of an image. For example, statistics based light estimation technology corrects a light by correcting a red, green, and blue (RGB) average of an input image to <NUM>:<NUM>:<NUM>, assuming that the average RGB ratio of the image is achromatic. Since statistics-based research requires a low technical complexity and thus, provides a fast algorithm operation, a lot of research has been conducted, some examples of which are gray-world, gray-edge, shade of gray, and gray-pixel.

When the statistics-based technology is unsuitable for a corresponding statistical model despite its low complexity, the color performance is greatly distorted. In general, to perform light estimation accurately, various colors should exist complexly in an image.

As prior art the following scientific papers are cited:
"<NPL>. In this paper, the authors propose to ameliorate the instability problem of the prior art by regularizing the intersection. Specifically the authors propose to introduce a constraint on the color of the illuminant and sow how the intersection problem in the context of convex and non convex illuminant constraints, based on the distribution of common light sources can be solved. This algorithm coupled with the simplest of segmentations results in good estimation results for a large set of real images. Estimation performance is significantly better than for the unconstrained algorithm.

In this paper, a method for recovering the spectrum of both the interface and the body reflectance for images composed of dichromatic surfaces is presented. An important assumption is that the spectrum of the interface component is the same as that of the illuminant. It is also assumed that the image is presegmented into dichromatic patches, that surfaces possess specularities, and that these highlights change geometrically differently from the shading. The method is based on minimizing the sum of squares of deviations from the dichromatic model over all the patches in the images, by using finite-dimensional linear models to approximate spectral functions. The authors point out the shortcomings in the accuracy of such models when specularities are present in images. Results are presented for synthesized images made up of shaded patches with highlights. It is shown that the method does reasonably well in recovering interface and body colors as well as the illuminant spectrum and body spectral reflectance function.

In this paper, statistics-based color constancy algorithms are presented as working well as long as there are many colors in a scene, yet fail however when the encountering scenes comprise few surfaces. In contrast, physics-based algorithms, based on an understanding of physical processes such as highlights and interreflections, are presented as theoretically able to solve for color constancy even when there are few as two surfaces in a scene. Physics based theories are said to unfortunately rarely work outside the lab. In this paper, the authors show that a combination of physical and statistical knowledge leads to a surprisingly simple and powerful colors constancy algorithm, one that also works well for images of natural scenes.

In one general aspect, there is provided a computer implemented image processing method according to claim <NUM>.

The estimating of the visual spaces may include estimating dichromatic planes of the AC pixels based on a dichromatic model.

The values of the AC pixels may include red component values, green component values, and blue components of the AC pixels, and the estimating of the visual spaces may include estimating the visual spaces based on a linear combination of the red component values, the green component values, and the blue component values of the AC pixels in the frames.

The estimating of the visual spaces may include extracting parameters of the visual spaces that minimize vertical distances between a plane and the values of the AC pixels in the frames.

The parameters may include extracting the parameters based on a least squares method.

The estimating of the information of the AC light may include estimating color information of the AC light.

The information of the AC light may include a ratio of red, green, and blue components of the AC light.

The processing of the image may include correcting a color of the image based on color information of the AC light.

The extracting of the AC pixels may include extracting the AC pixels, from among the pixels, exhibiting a signal distortion by noise that is less than a threshold.

The extracting of the AC pixels may include extracting the AC pixels based on variations of values of the pixels in the frames.

The extracting of the AC pixels includes modeling values of the pixels in the frames into a sinusoid, calculating respective differences between the pixel values of the modeled sinusoid and the values of the pixels in the frames, and extracting, as the AC pixels from the pixels, pixels having a sum of the calculated differences lesser than a threshold,.

The modeling may include modeling the values of the pixels in the frames into the sinusoid based on a Gauss-Newton method.

The estimating of the information of the AC light may include determining light vector candidates corresponding to the AC light based on the visual spaces, determining a light vector from among the light vector candidates, based on prior information of the AC light, and estimating the information of the AC light based on the light vector.

The prior information may be obtained based on Planckian locus information.

The estimating of the information of the AC light may include estimating intersection lines of the visual spaces, determining an intersection line that minimizes a cost function, from among the intersection lines, based on a maximum a posteriori probability (MAP) estimation, and estimating the information of the AC light based on the determined intersection line.

The determining may include calculating probabilities of the intersection lines being perpendicular to the visual spaces, and determining an intersection line which minimizes a cost function, from among the intersection lines, based on prior information of the AC light and the probabilities.

In another general aspect, there is provided an image processing apparatus, including a processor configured to receive an image including a frames captured over time in a light environment including an alternating current (AC) light, to extract AC pixels corresponding to the AC light from a pixels in the image, to estimate visual spaces of the AC pixels based on values of the AC pixels included in the frames, to estimate information of the AC light included in the image based on the visual spaces, and to process the image based on the information of the AC light.

The processor may be configured to estimate visual spaces indicating illumination components and diffuse components of the AC pixels in the frames.

The processor may be configured to estimate dichromatic planes of the AC pixels based on a dichromatic model.

The values of the AC pixels may include red component values, green component values, and blue components of the AC pixels, and the processor may be configured to estimate the visual spaces based on a linear combination of the red component values, the green component values, and the blue component values of the AC pixels in the frames.

The processor may be configured to extract parameters of the visual spaces that minimize vertical distances between a plane and the values of the AC pixels in the frames.

The processor may be configured to estimate color information of the AC light.

The processor may be configured to correct a color of the image based on color information of the AC light.

The processor may be configured to extract, as the AC pixels, pixels showing a signal distortion by noise which is less than a threshold, from the pixels.

The processor may be configured to extract the AC pixels based on variations of values of the pixels included in the frames.

The processor may be configured to model values of the pixels in the frames into a sinusoid, to calculate respective differences between the pixel values of the modeled sinusoid and the values of the pixels in the frames, and to extract, as the AC pixels from the pixels, pixels having sum of the calculated differences lesser than a threshold.

The processor may be configured to model the values of the pixels included in the frames into the sinusoid based on a Gauss-Newton method.

The processor may be configured to determine light vector candidates corresponding to the AC light based on the visual spaces, to determine a light vector from among the light vector candidates, based on prior information of the AC light, and to estimate the information of the AC light based on the light vector.

The processor may be configured to estimate intersection lines of the visual spaces, to determine an intersection line that minimizes a cost function, from among the intersection lines, based on a maximum a posteriori probability (MAP) estimation, and to estimate the information of the AC light based on the determined intersection line.

The processor may be configured to calculate probabilities of the intersection lines being perpendicular to the visual spaces, and to determine an intersection line which minimizes a cost function, from among the intersection lines, based on prior information of the AC light and the probabilities.

In another general aspect, there is provided an image processing apparatus according to claim <NUM>.

In another general aspect, there is provided a non-transitory computer-readable storage medium according to claim <NUM>.

Other features and aspects will be apparent from the following detailed description, the drawings, and the dependent claims.

However, various changes, modifications, of the methods, apparatuses, and/or systems described herein will be apparent after an understanding of the disclosure of this application.

Although terms of "first" or "second" are used to explain various components, the components are not limited to the terms. These terms should be used only to distinguish one component from another component.

In the description, if one component is "connected", "coupled", or "joined" to another component, a third component may be "connected", "coupled", and "joined" between the first and second components, although the first component may be connected, coupled, or joined to the second component. On the contrary, if it is described that one component is "directly connected", "directly coupled", or "directly joined" to another component, a third component may be absent. Likewise, expressions, for example, "between" and "immediately between" and "adjacent to" and "immediately adjacent to" may also be construed as described in the foregoing.

Words describing relative spatial relationships, such as "below", "beneath", "under", "lower", "bottom", "above", "over", "upper", "top", "left", and "right", may be used to conveniently describe spatial relationships of one device or elements with other devices or elements. For example, if the device in the figures is turned over, elements described as "above," or "upper" other elements would then be oriented "below," or "lower" the other elements or features. Thus, the term "above" can encompass both the above and below orientations depending on a particular direction of the figures.

Contents of the description regarding any one axial direction (x, y, or z axis) may also be applied in the same manner to any other axial direction. Such words are to be interpreted as encompassing a device oriented as illustrated in the drawings, and in other orientations in use or operation.

As used herein, the singular forms are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, components or a combination thereof, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.

Hereinafter, the examples will be described in detail with reference to the accompanying drawings, wherein like drawing reference numerals are used for like elements.

<FIG> illustrates an example of an image processing method.

Referring to <FIG>, in a light environment includes an alternating current (AC) light <NUM>. In <FIG>, a color of a light is estimated accurately based on a change in intensity of a pixel value which changes fast depending on a light based on capturing an image including a plurality of frames over time. By estimating the color of the light, the image quality for a color of an captured image <NUM> improves.

The AC light <NUM> refers to a beam whose an intensity changes periodically over time. In an example, the AC light <NUM> is a sinusoidal beam having a frequency of <NUM> hertz (Hz). The AC light <NUM> is generated by an AC light source. In an example, the light environment includes an environment including only an AC light, and a light environment in which a direct current (DC) light and an AC light are mixed.

An image processing method includes capturing operation <NUM>, image analyzing operation <NUM>, and image processing operation <NUM>.

In capturing operation <NUM>, an image including a plurality of frames is generated by capturing an object <NUM> over time in the light environment including the AC light <NUM>. In an example, a camera <NUM> captures the object <NUM> at a capturing speed (frames per second (fps)) greater than or equal to the frequency (Hz) of the AC light. For example, when the AC light has a frequency of <NUM>, the camera <NUM> captures the object <NUM> at a speed of <NUM> fps or higher.

In image analyzing operation <NUM>, the image including the plurality of frames <NUM>, <NUM>, and <NUM> captured over time is analyzed in a temporal domain region. The image including the plurality of frames <NUM>, <NUM>, and <NUM> is generated in the light environment including the AC light <NUM> and thus, has image information which changes over time. For example, even pixels corresponding to the same position in the respective frames <NUM>, <NUM>, and <NUM> have different pixel values over time.

A pixel value includes intensity information of a pixel, and is also referred to as a pixel intensity. For example, the pixel value is a value between "<NUM>" and "<NUM>". A greater value indicates a brighter pixel. Further, the pixel value is expressed by a plurality of subpixel values. In an example, a pixel of a color image is expressed as red component pixel value, green component pixel value, blue component pixel value. In another example, a pixel of a color image is expressed in a form of a <NUM>*<NUM> matrix. In a case of a color image, various colors are created by combining a red component, a green component, and a blue component. For example, when the pixel value is expressed by a value between "<NUM>" and "<NUM>", <NUM> colors corresponding to <NUM><NUM> colors are created.

In image analyzing operation <NUM>, values of a plurality of pixels included in a plurality of frames are obtained, and information of an AC light is estimated using changes in the plurality of pixel values. In particular, in image analyzing operations <NUM>, color information of the AC light is estimated. In an example, the color information of the AC light refers to a ratio of red, green, and blue of the color of the AC light.

In image processing operation <NUM>, a corrected image <NUM> is generated by processing the existing image <NUM> based on the information of the AC light. For example, the image <NUM> in which a color of the existing image <NUM> is corrected is generated based on the color information of the AC light. The color-corrected image <NUM> is an image in which a color corresponding to the AC light is removed from the existing image <NUM>.

<FIG> illustrate examples of reflected light. Referring to <FIG>, according to a dichromatic model, an incident beam <NUM> is divided into two reflection components <NUM> and <NUM>. In an example, a reflected light from an object includes the specular reflection component <NUM> and the diffuse reflection component <NUM>. The specular reflection component <NUM> is a reflection component reflected on the surface of the object. The diffuse reflection component <NUM> is a reflection component occurring when the beam is transmitted through the object and scattered.

The reflected light from the object includes a combination of the specular reflection component <NUM> and the diffuse reflection component <NUM>. The reflected light from the object is expressed by a pixel value of an image, and the pixel value of the image is expressed by Equation <NUM>.

In Equation <NUM>, Λc denotes a diffuse chromaticity, Γc denotes a specular chromaticity, md denotes a diffuse parameter, and ms denotes a specular parameter.

According to Equation <NUM>, a pixel value of an image includes an illumination component and a diffuse component. The illumination component corresponds to the specular reflection component <NUM>. A specular chromaticity of the specular reflection component <NUM> includes information related to a color of a light, and a specular parameter of the specular reflection component <NUM> includes information related to an intensity of the light. The diffuse component corresponds to the diffuse reflection component <NUM>. A diffuse chromaticity of the diffuse reflection component <NUM> includes information related to a color of an object, and a diffuse parameter of the diffuse reflection component <NUM> includes information related to a brightness of the object. The specular chromaticity is also referred to as a light vector, and the diffuse chromaticity is also referred to as an object vector.

Referring to <FIG>, in an example, the diffuse chromaticity and the specular chromaticity are expressed in a form of vectors. For example, the diffuse chromaticity and the specular chromaticity are expressed by vectors of size <NUM>, for example, in a form of (red component, green component, blue component) or <MAT>. Thus, a pixel value of an image determined by a combination of an illumination component <NUM> and a diffuse component <NUM> is also expressed in a form of (red component pixel value, green component pixel value, blue component pixel value) or <MAT> matrix.

For example, Equation <NUM> is expressed in a form of matrix, as given by Equation <NUM>.

In an example, a unit vector of the illumination component <NUM> is the specular chromaticity, and a size of the illumination component <NUM> is the specular parameter. Further, a unit vector of the diffuse component <NUM> is the diffuse chromaticity, and a size of the diffuse component <NUM> is the diffuse parameter.

Further, values of pixels included in the image including the plurality of frames captured over time in the light environment including the AC light exist on a space <NUM> indicating the illumination component <NUM> and the diffuse component <NUM>. The space <NUM> is referred to as a visual space or a dichromatic plane. The visual space will be described further below with reference to <FIG>.

Some methods estimate a light by estimating a plane or a straight line principally using pixels at different positions in an image. In this example, to accurately estimate the plane or the straight line, pixels being at different positions in the image and having the same diffuse chromaticity and the same specular chromaticity need to be extracted. For this, the light is estimated by detecting a specular region and estimating the plane or the straight line using pixels in the detected region. However, when a specular region in the image is insufficient, the light estimation performance greatly decreases.

According to an example, rather than using pixels included in the specular region, a light is estimated by selecting AC pixels through modeling of a change in intensity of an AC light of the pixels to minimize an effect of noise on the image, and thus, the light is estimated more accurately.

<FIG> illustrates an example of a visual space.

Referring to <FIG>, values of pixels included in a plurality of frames exist on a visual space <NUM>. The visual space <NUM> includes a space in which the values of the pixels included in the plurality of frames exist.

In an example, values of pixels included in an image including a plurality of frames captured over time in a light environment including an AC light are expressed in a temporal domain, as given by Equation <NUM>.

Since a specular chromaticity <NUM> includes information related to a color of a light and a diffuse chromaticity <NUM> includes information related to a color of an object, the specular chromaticity <NUM> and the diffuse chromaticity <NUM> do not change over time. In addition, since a specular parameter and a diffuse parameter include information related to an intensity of a reflected light with respect to the AC light, the specular parameter and the diffuse parameter change over time. In detail, the specular parameter and the diffuse parameter are expressed by Equation <NUM>.

In Equation <NUM>, E(λ) denotes a spectral energy distribution.

Since the specular chromaticity <NUM> and the diffuse chromaticity <NUM> do not change over time, the values of the pixels included in the plurality of frames exist on the visual space <NUM>. For example, I(t) is the same as a vector <NUM>, I(t+<NUM>) is the same as a vector <NUM>, and I(t+<NUM>) is the same as a vector <NUM>. As described above, the visual space <NUM> is determined if values of pixels included in two frames are provided with respect to pixels present at a position.

<FIG> illustrate an example of estimating information of an AC light included in an image based on visual spaces.

Referring to <FIG>, a single visual space <NUM>, <NUM> is determined for each pixel included in an image. A plurality of pixels included in the image have different diffuse chromaticities including information related to a color of an object depending on positions thereof, but have the same specular chromaticity including information related to a color of a light. That is because the color of the light is unchanged irrespective of the positions of the pixels. Thus, an intersection line <NUM> of the visual space <NUM> of a first pixel and the visual space <NUM> of a second pixel indicates the specular chromaticity including the information related to the color of the light. In an example, temporal spaces of N pixels have a single intersection line, and the intersection line indicates the specular chromaticity.

In an example, shown in <FIG>, noise such as color noise causes a plurality of intersection lines. In an example, temporal spaces of N pixels have up to NC<NUM> intersection lines. Thus, to more accurately estimate the specular chromaticity including the information related to the color of the light, additional constraints may be needed. The additional constraints will be described further below with reference to <FIG>.

<FIG> illustrates an example of an image processing method. The operations in <FIG> may be performed in the sequence and manner as shown. Many of the operations shown in <FIG> may be performed in parallel or concurrently. The blocks of the image processing method of <FIG>, and combinations of the blocks, are performed by an image processing apparatus. In an example, the image processing apparatus is implemented by special purpose hardware-based computer, and devices such as a processor, that perform the specified functions, or combinations of special purpose hardware and computer instructions included in the image processing apparatus. In addition to the description of <FIG> below, the descriptions of <FIG> is also applicable to <FIG>. Thus, the above description may not be repeated here.

In operation <NUM>, the image processing apparatus receives an image including a plurality of frames captured over time in a light environment including an AC light.

In operation <NUM>, the image processing apparatus extracts AC pixels corresponding to the AC light from a plurality of pixels included in the image. As described above with reference to <FIG>, color noise makes it difficult to estimate accurate AC light information. Thus, pixels expected to have little noise are extracted as the AC pixels from the plurality of pixels included in the image. An example of extracting AC pixels will be described further below with reference to <FIG>.

In operation <NUM>, the image processing apparatus estimates visual spaces of the AC pixels based on values of the AC pixels included in the plurality of frames. For each AC pixel, a single visual space corresponding thereto is estimated. An example of estimating visual spaces will be described further below with reference to <FIG>.

In operation <NUM>, the image processing apparatus estimates information of the AC light included in the image based on the visual spaces. The information of the AC light includes color information of the AC light, and the color information of the AC light indicates a ratio of red, green, and blue of the color of the AC light. An example of estimating information of an AC light will be described further below with reference to <FIG> and <FIG>.

In operation <NUM>, the image processing apparatus processes the image based on the information of the AC light. The image processing apparatus corrects a color of the image based on the color information of the AC light. For example, by removing a color corresponding to the AC light in an existing image, the image quality performance associated with a color of the existing image improves.

<FIG> illustrate an example of extracting AC pixels.

In selecting an AC pixel by analyzing a change in intensity of a light, a pixel showing a least signal distortion by noise is selected to more accurately estimate a visual space.

<FIG> illustrates a pixel value I<NUM>(t) <NUM> of a first pixel at a time t and a pixel value I<NUM>(t+<NUM>) <NUM> of the first pixel at a time t+<NUM>, and <FIG> illustrates a pixel value I<NUM>(t) <NUM> of a second pixel at the time t and a pixel value I<NUM>(t+<NUM>) <NUM> of the second pixel at the time t+<NUM>.

When the pixel value changes relatively slightly over time as shown in <FIG>, an effect of noise is greater. A temporal space generated based on the pixel exhibits a relatively low accuracy. When the pixel value changes relatively greatly over time as shown in <FIG>, an effect of an AC change of a light is greater.

To obtain more accurate AC light information, pixels showing a little signal distortion by noise are extracted as AC pixels, and visual spaces and AC light information are estimated based on the extracted AC pixels.

Referring to <FIG>, a change of a pixel value over time is modeled based on a change in intensity of an AC light. For example, when the change in intensity of the AC light is sinusoidal, the change of the pixel value is modeled using a sinusoid, as expressed by Equation <NUM>.

In Equation <NUM>, F denotes an AC frequency, s denotes a frame rate of capturing, b denotes a phase, c denotes an offset value, and n denotes noise. Since the AC frequency of <NUM> and the frame rate of capturing are known, a frequency is predictable, and thus parameters to be estimated are an amplitude of the sinusoid, the phase, and the offset value.

In a similar manner, the change in the pixel value is also modeled as expressed by Equation <NUM>.

When the Gauss-Newton scheme is used, the parameters are accurately estimated in a short time through an iteration. For example, the change in the pixel value is modeled based on Equation <NUM> as shown in <FIG>, and is modeled based on Equation <NUM> as shown in <FIG>.

estimation is completed, pixels are selected, as AC pixels, from the plurality of pixels if a difference between the modeled sinusoid and a real change in the pixel value is less than a threshold. In an example, with respect to the plurality of pixels, differences between the pixel values of the modeled sinusoid and values of pixels in respective frames are calculated. Pixels with a sum (AC fitting error) of the differences calculated for the respective frames, which is less than a threshold, are extracted as the AC pixels. The AC fitting error is expressed by Equation <NUM>.

To lower the algorithm complexity, sinusoid modeling is performed using a set stride, rather than all the pixels. For example, only <NUM> AC pixels are extracted from the plurality of pixels included in the image.

<FIG> illustrates an example of estimating a visual space.

Referring to <FIG>, in a visual space estimation operation, a light is estimated using temporal samples of AC pixels obtained in a previous operation. Visual spaces of the AC pixels are estimated based on values of the AC pixels included in a plurality of frames. With respect to the AC pixels, visual spaces indicating illumination components and diffuse components of the AC pixels included in the plurality of frames are estimated. The visual spaces are dichromatic planes of the AC pixels.

In an example, the parameters of a plane are three directional parameters of a normal vector of a three-dimensional plane, and are obtained using the least squares method. A plane minimizing an average vertical distance to the values of the AC pixels included in the plurality of frames is estimated based on vertical distances between the plane and the values of the AC pixels included in the plurality of frames.

For example, a normal vector <NUM> of a plane <NUM> minimizes an average vertical distance to a pixel value <NUM> at a first point in time, a pixel value <NUM> at a second point in time, a pixel value <NUM> at a third point in time, a pixel value <NUM> at a fourth point in time, a pixel value <NUM> at a fifth point in time, and a pixel value <NUM> at a sixth point in time, using the least squares method.

<FIG> and <FIG> illustrate examples of estimating information of an AC light.

Referring to <FIG>, three AC pixels <NUM>, <NUM>, and <NUM> are extracted from an image <NUM>. <FIG> illustrates a visual space corresponding to a first pixel <NUM>, <FIG> illustrates a visual space corresponding to a second pixel <NUM>, and <FIG> illustrates a visual space corresponding to a third pixel <NUM>.

Intersection lines of the visual space of the first pixel <NUM>, the visual space of the second pixel <NUM>, and the visual space of the third pixel <NUM> indicate specular chromaticities including information related to a color of the light. The specular chromaticities include color information of an AC light, and the color information of the AC light indicates a ratio of red, green, and blue of the color of the AC light.

Light vector candidates corresponding to the AC light are determined based on the visual spaces. A light vector is determined among the light vector candidates, based on prior information of the AC light. The information of the AC light is estimated based on the light vector.

In an example, an intersection line of visual spaces should correspond to a single light vector since all the visual spaces share the same light vector. However, in reality, there are a number of intersection lines due to noise in a video and camera characteristics. To estimate an optimal light vector among the intersection lines, in an example, a maximum a posteriori probability (MAP) estimation is used.

The intersection lines of the visual spaces are estimated. In an example, an intersection line which minimizes a cost function is determined among the intersection lines based on an MAP estimation. The information of the AC light is estimated based on the determined intersection line.

The MAP estimation is a method for detecting an optimal parameter by combining a provided observation result and "prior knowledge (prior probability)". The MAP estimation is expressed by Equation <NUM>.

Referring to Equation <NUM>, the MAP estimation includes two terms, in detail, a physics term indicating physical properties and a statistical term indicating statistical properties.

The physics term indicates a physical relationship between an AC light and a visual space. In an example, a vector of a light should be perpendicular to normal vectors of all visual spaces. Thus, an angle using inner products of the light vector and the normal vectors of the visual spaces is calculated and used. In an example, when the angle is close to <NUM> degrees, the intersection line is determined to be of a more accurate light. When a cosine value obtained using a cosine function is close to "<NUM>", that is, when the value is small, the intersection line is estimated to be of a more accurate light. The foregoing is expressed by Equation <NUM>.

The statistical term uses a Planckian locus of <FIG> as the prior information. The Planckian locus indicates changes in colors of a black body with respect to temperature. These colors include colors of a majority of light sources that may exist in nature. In an example, on an ultraviolet (UV) domain, an intersection line at a short vertical distance from the Planckian locus is estimated to be of an accurate light.

An intersection line minimizing a sum of values of the physics term and the statistical term is detected, and the intersection line is estimated as a light vector.

Referring to <FIG>, an image processing method includes operation <NUM> of extracting AC pixels corresponding to an AC light from a plurality of pixels included in an image, operation <NUM> of estimating visual spaces of the AC pixels based on values of the AC pixels included in the plurality of frames, and operation <NUM> of estimating information of the AC light included in the image based on the visual spaces, and processing the image based on the information of the AC light.

In operation <NUM>, AC pixels are extracted from a plurality of pixels included in an image <NUM> including a plurality of frames captured over time in a light environment including an AC light. In an example, pixels with a sum of differences between pixel values of a modeled sinusoid <NUM> and pixel values of the frames, which is less than a threshold, are extracted as the AC pixels.

In operation <NUM>, visual spaces <NUM> are estimated based on pixel values of extracted AC pixels <NUM> over time.

In operation <NUM>, a set <NUM> of intersection lines of the visual spaces of the AC pixels is extracted. A single intersection line is extracted from the intersection lines based on an MAP estimation. The extracted intersection line includes color information of the AC light, and correction <NUM> of a color of the image is performed based on the color information of the AC light.

<FIG> illustrates an example of an image processing apparatus.

Referring to <FIG>, an image processing apparatus <NUM> includes a processor <NUM>. The image processing apparatus <NUM> further includes, an input/output interface <NUM>, a memory <NUM>, a communication interface <NUM>, and sensor(s) <NUM>. The processor <NUM>, the memory <NUM>, the communication interface <NUM>, and the sensor(s) <NUM> communicate with each other through a communication bus <NUM>.

The processor <NUM> is, for example, an apparatus configured to execute instructions or programs, or to control the image processing apparatus <NUM>. The processor <NUM> includes, for example, a central processing unit (CPU), a processor core, a multi-core processor, a reconfigurable processor, a multicore processor, a multiprocessor, an application-specific integrated circuit (ASIC), and a field programmable gate array (FPGA), a graphics processing unit (GPU), or any other type of multi- or single-processor configuration. Further details regarding the processor <NUM> is provided below.

The processor <NUM> receives an image including a plurality of frames captured over time in a light environment including an AC light, extracts AC pixels corresponding to the AC light from a plurality of pixels included in the image, estimates visual spaces of the AC pixels based on values of the AC pixels included in the plurality of frames, estimates information of the AC light included in the image based on the visual spaces, and processes the image based on the information of the AC light.

The memory <NUM> includes an enrollment database including the image and the pixel values. The memory <NUM> is a volatile memory or a non-volatile memory. The memory <NUM> includes a large capacity storage medium such as a hard disk to store the variety of data. Further details regarding the memory <NUM> is provided below.

The sensor(s) <NUM> include, for example, a camera configured to capture an image at a capturing speed (fps) greater than or equal to a frequency (Hz) of the AC light. The sensor(s) <NUM> collect a variety of image information.

In an example, the input/output interface <NUM> may be a display that receives an input from a user or provides an output of the image processing apparatus <NUM>. In an example, the input/output interface <NUM> may function as an input device and receives an input from a user through a traditional input method, for example, a keyboard and a mouse, and a new input method, for example, a touch input, a voice input, and an image input. Thus, the input/output interface <NUM> may include, for example, a keyboard, a mouse, a touchscreen, a microphone, and other devices that may detect an input from a user and transmit the detected input to the image processing apparatus <NUM>.

In an example, the input/output interface <NUM> may function as an output device, and provide an output of the processed image based on the information of the AC light to a user. The input/output interface <NUM> may include, for example, a display, a touchscreen, and other devices that may provide an output to a user. However, the input/output interface <NUM> are not limited to the example described above, and any other displays, such as, for example, computer monitor and eye glass display (EGD) that are operatively connected to the image processing apparatus <NUM> may be used without departing from the scope of the illustrative examples described. In an example, the input/output interface <NUM> is a physical structure that includes one or more hardware components that provide the ability to render a user interface, render a display, and/or receive user input.

In an example. the image processing apparatus <NUM> is connected to an external device (for example, a microphone, a keyboard, or an image sensor) via the communication interface <NUM>, and exchanges data.

In an example, the processor <NUM> estimates visual spaces indicating illumination components and diffuse components of the AC pixels included in the plurality of frames. In an example, the processor <NUM> estimates dichromatic planes of the AC pixels based on a dichromatic model. In an example, the processor <NUM> estimates the visual spaces to be produced through a linear combination of red component values, green component values, and blue component values of the AC pixels included in the plurality of frames. In an example, the processor <NUM> estimates parameters of the visual spaces which minimize vertical distances between a plane and the values of the AC pixels included in the plurality of frames. In an example, the processor <NUM> estimates color information of the AC light. In an example, the processor <NUM> corrects a color of the image based on color information of the AC light. In an example, the processor <NUM> extracts the AC pixels based on variations of values of the plurality of pixels included in the plurality of frames. In an example, the processor <NUM> models values of the plurality of pixels included in the plurality of frames into a sinusoid, calculates differences between the pixel values of the modeled sinusoid and values of pixels in the frames, with respect to the plurality of pixels, and extracts, as the AC pixels, pixels with a sum of the calculated differences which is less than a threshold, from the plurality of pixels. In an example, the processor <NUM> models the values of the pixels included in the plurality of frames into a sinusoid based on the Gauss-Newton method. In an example, the processor <NUM> determines light vector candidates corresponding to the AC light based on the visual spaces, determines a light vector among the light vector candidates, based on prior information of the AC light, and estimates the information of the AC light based on the light vector. In an example, the processor <NUM> estimates intersection lines of the visual spaces, determines an intersection line which minimizes a cost function, among the intersection lines, based on an MAP estimation, and estimates the information of the AC light based on the determined intersection line. In an example, the processor <NUM> calculates probabilities of the intersection lines being perpendicular to the visual spaces, and determines an intersection line which minimizes a cost function, among the intersection lines, based on prior information of the AC light and the probabilities.

In addition, the processor <NUM> performs one or more methods described with reference to <FIG> or an algorithm corresponding to the at least one method. The processor <NUM> executes a program and controls the image processing apparatus <NUM>. Program codes executed by the processor <NUM> are stored in the memory <NUM>.

The image processing apparatus <NUM> is mounted on various devices and/or systems such as, for example, a smart phone, a mobile phone, a wearable device, (such as, a ring, a watch, a pair of glasses, glasses-type device, a bracelet, an ankle bracket, a belt, a necklace, an earring, a headband, a helmet, a device embedded in the cloths, or an eye glass display (EGD)), a computing device, for example, a server, a laptop, a notebook, a subnotebook, a netbook, an ultra-mobile PC (UMPC), a tablet personal computer (tablet), a phablet, a mobile internet device (MID), a personal digital assistant (PDA), an enterprise digital assistant (EDA), an ultra mobile personal computer (UMPC), a portable lab-top PC, electronic product, for example, a robot, a digital camera, a digital video camera, a portable game console, an MP3 player, a portable/personal multimedia player (PMP), a handheld e-book, a global positioning system (GPS) navigation, a personal navigation device, portable navigation device (PND), a handheld game console, an e-book, a television (TV), a high definition television (HDTV), a smart TV, a smart appliance, a smart home device, or a security device for gate control, a smart speaker, a robot, various Internet of Things (IoT) devices, or a kiosk and may be performed by an application, middleware, or an operating system installed on a user device, or a program of a server interoperating with the corresponding application.

The image processing apparatus <NUM>, and other apparatuses, units, modules, devices, and other components described herein with respect to <FIG> are implemented by hardware components. Examples of hardware components that may be used to perform the operations described in this application where appropriate include controllers, sensors, generators, drivers, memories, comparators, arithmetic logic units, adders, subtractors, multipliers, dividers, integrators, and any other electronic components configured to perform the operations described in this application. In other examples, one or more of the hardware components that perform the operations described in this application are implemented by computing hardware, for example, by one or more processors or computers. A processor or computer may be implemented by one or more processing elements, such as an array of logic gates, a controller and an arithmetic logic unit, a digital signal processor, a microcomputer, a programmable logic controller, a field-programmable gate array, a programmable logic array, a microprocessor, or any other device or combination of devices that is configured to respond to and execute instructions in a defined manner to achieve a desired result. In one example, a processor or computer includes, or is connected to, one or more memories storing instructions or software that are executed by the processor or computer. Hardware components implemented by a processor or computer may execute instructions or software, such as an operating system (OS) and one or more software applications that run on the OS, to perform the operations described in this application. The hardware components may also access, manipulate, process, create, and store data in response to execution of the instructions or software. For simplicity, the singular term "processor" or "computer" may be used in the description of the examples described in this application, but in other examples multiple processors or computers may be used, or a processor or computer may include multiple processing elements, or multiple types of processing elements, or both. For example, a single hardware component or two or more hardware components may be implemented by a single processor, or two or more processors, or a processor and a controller. One or more hardware components may be implemented by one or more processors, or a processor and a controller, and one or more other hardware components may be implemented by one or more other processors, or another processor and another controller. One or more processors, or a processor and a controller, may implement a single hardware component, or two or more hardware components. A hardware component may have any one or more of different processing configurations, examples of which include a single processor, independent processors, parallel processors, single-instruction single-data (SISD) multiprocessing, single-instruction multiple-data (SIMD) multiprocessing, multiple-instruction single-data (MISD) multiprocessing, and multiple-instruction multiple-data (MIMD) multiprocessing.

Instructions or software to control a processor or computer to implement the hardware components and perform the methods as described above are written as computer programs, code segments, instructions or any combination thereof, for individually or collectively instructing or configuring the processor or computer to operate as a machine or special-purpose computer to perform the operations performed by the hardware components and the methods as described above. In an example, the instructions or software includes at least one of an applet, a dynamic link library (DLL), middleware, firmware, a device driver, an application program storing the image processing method. In one example, the instructions or software include machine code that is directly executed by the processor or computer, such as machine code produced by a compiler. In another example, the instructions or software include higher-level code that is executed by the processor or computer using an interpreter. Programmers of ordinary skill in the art can readily write the instructions or software based on the block diagrams and the flow charts illustrated in the drawings and the corresponding descriptions in the specification, which disclose algorithms for performing the operations performed by the hardware components and the methods as described above.

Examples of a non-transitory computer-readable storage medium include read-only memory (ROM), random-access programmable read only memory (PROM), electrically erasable programmable read-only memory (EEPROM), random-access memory (RAM), dynamic random access memory (DRAM), static random access memory (SRAM), flash memory, non-volatile memory, CD-ROMs, CD-Rs, CD+Rs, CD-RWs, CD+RWs, DVD-ROMs, DVD-Rs, DVD+Rs, DVD-RWs, DVD+RWs, DVD-RAMs, BD-ROMs, BD-Rs, BD-R LTHs, BD-REs, blue-ray or optical disk storage, hard disk drive (HDD), solid state drive (SSD), flash memory, card type memory such as multimedia card, secure digital (SD) card, or extreme digital (XD) card, magnetic tapes, floppy disks, magneto-optical data storage devices, optical data storage devices, hard disks, solid-state disks, and any other device that is configured to store the instructions or software and any associated data, data files, and data structures in a non-transitory manner and providing the instructions or software and any associated data, data files, and data structures to a processor or computer so that the processor or computer can execute the instructions.

Claim 1:
A computer implemented image processing method, comprising:
receiving an image (<NUM>), the image including frames (<NUM>, <NUM>, <NUM>) captured over time in a light environment including an alternating current AC light (<NUM>);
extracting AC pixels (<NUM>) corresponding to the AC light from pixels in the image;
wherein the extracting of the AC pixels (<NUM>) comprises:
modeling values of the pixels in the frames into a sinusoid;
calculating respective differences between the pixel values of the modeled sinusoid and the values of the pixels in the frames; and
extracting, as the AC pixels from the pixels, pixels having a sum of the calculated differences lesser than a threshold,
estimating visual spaces (<NUM>) of the AC pixels based on values of the AC pixels in the frames,
wherein the estimating of the visual spaces (<NUM>) comprises estimating visual spaces indicating illumination components and diffuse components of the AC pixels in the frames;
estimating information of the AC light (<NUM>) included in the image based on the visual spaces; and
processing the image (<NUM>) based on the information of the AC light.