WO2017166886A1

WO2017166886A1 - Image processing system and method

Info

Publication number: WO2017166886A1
Application number: PCT/CN2016/113745
Authority: WO
Inventors: 唐文峰
Original assignee: 努比亚技术有限公司
Priority date: 2016-03-30
Filing date: 2016-12-30
Publication date: 2017-10-05
Also published as: CN105825485B; CN105825485A

Abstract

Disclosed are an image processing system and method. The system comprises: an extraction module (02), configured to extract a contrast ratio and an illumination parameter of a target image; a calculation module (03), configured to calculate a cropping coefficient of the target image according to the extracted contrast ratio and the illumination parameter; an allocation module (04), configured to reallocate a histogram of the target image according to the calculated cropping coefficient; a mapping module (05), configured to estimate a regulation curve of the target image according to the reallocated histogram and perform global tone mapping on the target image according to the regulation curve; and a dermabrasion processing module (06), configured to perform dermabrasion processing on the target image after the global tone mapping according to a pre-set dermabrasion algorithm.

Description

Image processing system and method

Technical field

This document relates to, but is not limited to, the field of terminal applications, and more particularly to an image processing system and method.

Background technique

Mobile phone selfies have gradually become a living habit of people, and the overall effect of the characters in the photos is the focus of attention. Therefore, the pre- or post-beautification of photos is an indispensable step. The beautification of the characters' pictures mainly includes: microdermabrasion, whitening and skin tone adjustment. However, in different environments, the camera is susceptible to ambient lighting, and the acquired image of the character is poorly contrasted, especially in the scenes of backlight, dark light, etc. when the self-timer is taken, and a single dermabrasion algorithm cannot adapt to such complex scenes.

Summary of the invention

The following is an overview of the topics detailed in this document. This Summary is not intended to limit the scope of the claims.

The embodiment of the invention provides an image processing system and method, which can overcome the shortcomings that the current single dermabrasion technology cannot adapt to complex scenes, and improve the user experience of the camera.

An image processing system (01) provided by an embodiment of the present invention includes: an extraction module (02), a calculation module (03), an allocation module (04), a mapping module (05), and a dermabrasion processing module. (06).

The extraction module (02) is set to extract the contrast and illumination parameters of the target image.

The calculation module (03) is configured to calculate a clipping coefficient of the target image based on the extracted contrast and illumination parameters.

An allocation module (04) is arranged to redistribute the histogram of the target image based on the calculated clipping factor.

The mapping module (05) is configured to estimate an adjustment curve of the target image according to the re-allocated histogram, and perform global tone mapping on the target image according to the adjustment curve.

The dermabrasion processing module (06) is configured to perform dermabrasion processing on the target image subjected to global tone mapping according to a preset dermabrasion algorithm.

Optionally,

The extraction module (02) is set to achieve contrast of the extracted target image by:

The gray value of the pixel at each coordinate position on the target image is extracted.

The gray value difference of the pixels at each adjacent coordinate position is calculated based on the extracted gray value of the pixel at each coordinate position.

The calculated absolute value of the gray value difference of the pixels at each adjacent coordinate position is compared with a preset empirical value.

Obtaining one or more absolute values of gray value differences greater than a preset empirical value according to the comparison result.

Calculating the sum of the squares of the absolute values of one or more gray value differences greater than the preset empirical value.

The calculated squared value is divided by the total number of pixels of the target image measured in advance, and the quotient obtained by dividing is taken as the contrast of the target image.

The extraction module (02) is set to realize the illumination parameter of the extraction target image by:

The sum of the gray values of the pixels at each coordinate position on the target image is calculated.

The sum of the calculated gradation values is divided by the total number of pixels of the target image that is pre-stated, and the quotient obtained by the division is taken as the illumination parameter of the target image.

Optionally, the calculation module (03) is configured to calculate a cropping coefficient of the target image according to the extracted contrast and illumination parameters by:

The illumination parameter of the extracted target image is divided by the preset normalization coefficient, and the quotient obtained by dividing is used as the contrast coefficient after the normalization of the contrast.

The product of the contrast coefficient and the contrast of the extracted target image is calculated, and the calculated product is used as the cropping coefficient of the target image.

Optionally, the calculation module (03) is further configured to:

Before the allocation module (04) estimates the adjustment curve of the target image according to the histogram, the clipping height of the histogram is calculated, including:

Calculating the product of the clipping factor and the maximum number of pixels corresponding to the pixel value in the histogram, The calculated product is used as the crop height of the histogram.

Optionally,

The preset dermabrasion algorithm includes a single channel dermabrasion algorithm and a three-channel dermabrasion algorithm based on the edge-preserving filter.

Optionally,

The histogram abscissa of the image is a pixel value, and the ordinate is a two-dimensional statistical chart of the number of pixels corresponding to each pixel value.

Optionally,

The extraction module (02) is configured to achieve contrast of the extracted target image by:

Where P _i is the absolute value difference of adjacent pixels, gray ⁱ is the gray value of the pixel at the position of coordinate i, i is a positive integer; gray ^j is the gray value of the pixel at the position of coordinate j, j is a positive integer, i And j is an adjacent coordinate; threshold is a previously obtained empirical value; M is a total amount of pixels of the target image that is pre-stated; C is a contrast of the target image.

Optionally,

The extraction module (02) is configured to implement an illumination parameter of the extraction target image by:

Where, gray ⁱ is the gray value of the pixel at the position of the coordinate i, i is a positive integer; M is the total number of pixels of the target image measured in advance; l is the illumination parameter of the target image, l∈[0, 255].

Optionally,

The calculation module (03) is configured to calculate a clipping coefficient of the target image according to the extracted contrast and illumination parameters by:

L is the contrast ratio after normalization of contrast; l is the illumination parameter of the target image; a a preset normalization coefficient;

Cp=C*L

Cp is the clipping factor; C is the contrast of the target image; L is the contrast factor after the normalization of the contrast.

In addition, an embodiment of the present invention further provides an image processing method, where the method includes the following steps:

The contrast and illumination parameters of the target image are extracted (S101).

The cropping coefficient of the target image is calculated based on the extracted contrast and illumination parameters (S102).

A histogram of the target image is re-allocated according to the calculated cropping coefficient (S103).

The adjustment curve of the target image is estimated based on the re-allocated histogram, and the target image is subjected to global tone mapping according to the adjustment curve (S104).

The target image subjected to global tone mapping is subjected to dermabrasion processing according to a preset dermabrasion algorithm (S105).

Optionally,

The contrast of the extracted target image includes:

The lighting parameters for extracting the target image include:

Divide the sum of the calculated gray values by the total number of pixels of the pre-stated target image and divide by The obtained quotient is used as the illumination parameter of the target image.

Optionally, calculating the cropping coefficients of the target image according to the extracted contrast and illumination parameters includes:

Optionally, the method further includes:

Before estimating the adjustment curve of the target image from the histogram, calculate the clipping height of the histogram by the following steps:

The product of the clipping coefficient and the maximum number of pixels corresponding to the pixel value in the histogram is calculated, and the calculated product is taken as the clipping height of the histogram.

Optionally, the preset dermabrasion algorithm comprises: a single channel dermabrasion algorithm and a three-channel dermabrasion algorithm based on the edge-preserving filter.

Optionally,

The contrast of the extraction target image includes:

Optionally,

The illumination parameters of the extraction target image include:

Optionally,

Calculating the cropping coefficients of the target image according to the extracted contrast and illumination parameters includes:

L is the contrast coefficient after normalization of contrast; l is the illumination parameter of the target image; a is the preset normalization coefficient;

Cp=C*L

An image processing system and method proposed by the embodiments of the present invention includes: an extraction module configured to extract contrast and illumination parameters of a target image. The calculation module is configured to calculate a clipping coefficient of the target image according to the extracted contrast and illumination parameters. An allocation module configured to redistribute a histogram of the target image based on the calculated clipping factor. The mapping module is configured to estimate an adjustment curve of the target image according to the re-allocated histogram, and perform global tone mapping on the target image according to the adjustment curve. The dermabrasion processing module is configured to perform dermabrasion processing on the target image subjected to global tone mapping according to a preset dermabrasion algorithm. The solution of the embodiment of the present invention can overcome the shortcomings that the current single dermabrasion technology cannot adapt to complex scenes, and improve the user experience of the camera.

Other aspects will be apparent upon reading and understanding the drawings and detailed description.

BRIEF abstract

1 is a schematic structural diagram of hardware of an optional mobile terminal implementing an embodiment of the present invention;

2 is a schematic diagram of a wireless communication system of the mobile terminal shown in FIG. 1;

3 is a block diagram showing the composition of an image processing system according to an embodiment of the present invention;

4 is a schematic diagram of histogram clipping of a conventional CLAHE algorithm;

FIG. 5 is a flowchart of an image processing method according to an embodiment of the present invention.

Embodiments of the invention

It is understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

An optional mobile terminal embodying various embodiments of the present invention will now be described with reference to the accompanying drawings. In the following description, the use of suffixes such as "module", "component" or "unit" set to indicate an element is merely an explanation for facilitating embodiments of the present invention, and does not have a specific meaning per se. Therefore, "module" and "component" can be used in combination.

The mobile terminal can be implemented in various forms. For example, the terminal described in the embodiments of the present invention may include, for example, a mobile phone, a smart phone, a notebook computer, a digital broadcast receiver, a PDA (Personal Digital Assistant), a PAD (Tablet), a PMP (Portable Multimedia Player), a navigation device Mobile terminals of the like and fixed terminals such as digital TVs, desktop computers, and the like. In the following, it is assumed that the terminal is a mobile terminal. However, those skilled in the art will appreciate that configurations in accordance with embodiments of the present invention can be applied to fixed type terminals in addition to components that are specifically for mobile purposes.

FIG. 1 is a schematic structural diagram of hardware of an optional mobile terminal implementing an embodiment of the present invention.

The mobile terminal 100 may include a wireless communication unit 110, an A/V (Audio/Video) input unit 120, a user input unit 130, a sensing unit 140, an output unit 150, a memory 160, an interface unit 170, a controller 180, and a power supply unit 190. and many more. Figure 1 illustrates a mobile terminal having various components, but it should be understood that not all illustrated components are required to be implemented. More or fewer components can be implemented instead. The elements of the mobile terminal will be described in detail below.

Wireless communication unit 110 typically includes one or more components that permit radio communication between mobile terminal 100 and a wireless communication system or network. For example, the wireless communication unit may include at least one of a broadcast receiving module 111, a mobile communication module 112, a wireless internet module 113, a short-range communication module 114, and a location information module 115.

The broadcast receiving module 111 receives a broadcast signal and/or broadcast associated information from an external broadcast management server via a broadcast channel. The broadcast channel can include a satellite channel and/or a terrestrial channel. The broadcast management server may be a server that generates and transmits a broadcast signal and/or broadcast associated information or a server that receives a previously generated broadcast signal and/or broadcast associated information and transmits it to the terminal. The broadcast signal may include a TV broadcast signal, a radio broadcast signal, a data broadcast signal, and the like. Moreover, the broadcast signal may further include a broadcast signal combined with a TV or radio broadcast signal. The broadcast associated information may also be provided via a mobile communication network, and in this case, the broadcast associated information may be received by the mobile communication module 112. The broadcast signal may exist in various forms, for example, it may exist in the form of Digital Multimedia Broadcasting (DMB) Electronic Program Guide (EPG), Digital Video Broadcasting Handheld (DVB-H) Electronic Service Guide (ESG), and the like. . The broadcast receiving module 111 can receive a signal broadcast by using various types of broadcast systems. In particular, the broadcast receiving module 111 can use forward link media (MediaFLO) by using, for example, multimedia broadcast-terrestrial (DMB-T), digital multimedia broadcast-satellite (DMB-S), digital video broadcast-handheld (DVB-H) The digital broadcasting system of the @) data broadcasting system, the terrestrial digital broadcasting integrated service (ISDB-T), and the like receives digital broadcasting. The broadcast receiving module 111 can be constructed as various broadcast systems suitable for providing broadcast signals as well as the above-described digital broadcast system. The broadcast signal and/or broadcast associated information received via the broadcast receiving module 111 may be stored in the memory 160 (or other type of storage medium).

The mobile communication module 112 transmits the radio signals to and/or receives radio signals from at least one of a base station (e.g., an access point, a Node B, etc.), an external terminal, and a server. Such radio signals may include voice call signals, video call signals, or various types of data transmitted and/or received in accordance with text and/or multimedia messages.

The wireless internet module 113 supports wireless internet access of the mobile terminal. The module can be internally or externally coupled to the terminal. The wireless Internet access technologies involved in the module may include WLAN (Wireless LAN) (Wi-Fi), Wibro (Wireless Broadband), Wimax (Worldwide Interoperability for Microwave Access), HSDPA (High Speed Downlink Packet Access), etc. .

The short range communication module 114 is a module that is configured to support short range communication. Examples of short-range communication technologies include BluetoothTM, Radio Frequency Identification (RFID), Infrared Data Association (IrDA), Ultra Wide Band (UWB), ZigbeeTM, and the like.

The location information module 115 is a module configured to check or acquire location information of the mobile terminal. Bit A typical example of a placement module is GPS (Global Positioning System). According to the current technology, the GPS module 115 calculates distance information and accurate time information from three or more satellites and applies triangulation to the calculated information to accurately calculate three-dimensional current position information based on longitude, latitude, and altitude. Currently, the method for calculating position and time information uses three satellites and corrects the calculated position and time information errors by using another satellite. Further, the GPS module 115 is capable of calculating speed information by continuously calculating current position information in real time.

The A/V input unit 120 is arranged to receive an audio or video signal. The A/V input unit 120 may include a camera 121 and a microphone 1220 that processes image data of still pictures or video obtained by the image capturing device in a video capturing mode or an image capturing mode. The processed image frame can be displayed on the display unit 151. The image frames processed by the camera 121 may be stored in the memory 160 (or other storage medium) or transmitted via the wireless communication unit 110, and two or more cameras 1210 may be provided according to the configuration of the mobile terminal. The microphone 122 can receive sound (audio data) via a microphone in an operation mode of a telephone call mode, a recording mode, a voice recognition mode, and the like, and can process such sound as audio data. The processed audio (voice) data can be converted to a format output that can be transmitted to the mobile communication base station via the mobile communication module 112 in the case of a telephone call mode. The microphone 122 can implement various types of noise cancellation (or suppression) algorithms to cancel (or suppress) noise or interference generated during the process of receiving and transmitting audio signals.

The user input unit 130 may generate key input data according to a command input by the user to control various operations of the mobile terminal. The user input unit 130 allows the user to input various types of information, and may include a keyboard, a pot, a touch pad (eg, a touch sensitive component that detects changes in resistance, pressure, capacitance, etc. due to contact), a scroll wheel , rocker, etc. In particular, when the touch panel is superimposed on the display unit 151 in the form of a layer, a touch screen can be formed.

The sensing unit 140 detects the current state of the mobile terminal 100 (eg, the open or closed state of the mobile terminal 100), the location of the mobile terminal 100, the presence or absence of contact (ie, touch input) by the user with the mobile terminal 100, and the mobile terminal. The orientation of 100, the acceleration or deceleration movement and direction of the mobile terminal 100, and the like, and generates a command or signal for controlling the operation of the mobile terminal 100. For example, when the mobile terminal 100 is implemented as a slide type mobile phone, the sensing unit 140 can sense whether the slide type phone is turned on or off. In addition, the sensing unit 140 can detect whether the power supply unit 190 provides power or whether the interface unit 170 is coupled to an external device. The sensing unit 140 may include a proximity sensor 1410 will be described below in connection with a touch screen.

The interface unit 170 serves as an interface through which at least one external device can connect with the mobile terminal 100. For example, the external device may include a wired or wireless headset port, an external power (or battery charger) port, a wired or wireless data port, a memory card port, a port configured to connect a device having an identification module, and an audio input/output. (I/O) port, video I/O port, headphone port, and more. The identification module may be stored to verify various information used by the user using the mobile terminal 100 and may include a User Identification Module (UIM), a Customer Identification Module (SIM), a Universal Customer Identity Module (USIM), and the like. In addition, the device having the identification module (hereinafter referred to as "identification device") may take the form of a smart card, and thus the identification device may be connected to the mobile terminal 100 via a port or other connection device. The interface unit 170 may be arranged to receive input (eg, data information, power, etc.) from an external device and transmit the received input to one or more components within the mobile terminal 100 or may be configured to be at the mobile terminal and external device Transfer data between.

In addition, when the mobile terminal 100 is connected to the external base, the interface unit 170 may function as a path through which power is supplied from the base to the mobile terminal 100 or may be used as a transmission of various command signals allowing input from the base to the mobile terminal 100 The path to the terminal. Various command signals or power input from the base can be used as signals for identifying whether the mobile terminal is accurately mounted on the base. Output unit 150 is configured to provide an output signal (eg, an audio signal, a video signal, an alarm signal, a vibration signal, etc.) in a visual, audio, and/or tactile manner. The output unit 150 may include a display unit 151, an audio output module 152, an alarm unit 153, and the like.

The display unit 151 can display information processed in the mobile terminal 100. For example, when the mobile terminal 100 is in a phone call mode, the display unit 151 can display a user interface (UI) or a graphical user interface (GUI) related to a call or other communication (eg, text messaging, multimedia file download, etc.). When the mobile terminal 100 is in a video call mode or an image capturing mode, the display unit 151 may display a captured image and/or a received image, a UI or GUI showing a video or image and related functions, and the like.

Meanwhile, when the display unit 151 and the touch panel are superposed on each other in the form of a layer to form a touch screen, the display unit 151 can function as an input device and an output device. The display unit 151 may include at least one of a liquid crystal display (LCD), a thin film transistor LCD (TFT-LCD), an organic light emitting diode (OLED) display, a flexible display, a three-dimensional (3D) display, and the like. Some of these displays can It is configured to be transparent to allow a user to view from the outside, which may be referred to as a transparent display, and a typical transparent display may be, for example, a TOLED (Transparent Organic Light Emitting Diode) display or the like. According to a particular desired embodiment, the mobile terminal 100 may include two or more display units (or other display devices), for example, the mobile terminal may include an external display unit (not shown) and an internal display unit (not shown) . The touch screen can be set to detect touch input pressure as well as touch input position and touch input area.

The audio output module 152 may convert audio data received by the wireless communication unit 110 or stored in the memory 160 when the mobile terminal is in a call signal receiving mode, a call mode, a recording mode, a voice recognition mode, a broadcast receiving mode, and the like. The audio signal is output as sound. Moreover, the audio output module 152 can provide audio output (eg, call signal reception sound, message reception sound, etc.) associated with a particular function performed by the mobile terminal 100. The audio output module 152 can include a speaker, a buzzer, and the like.

The alarm unit 153 can provide an output to notify the mobile terminal 100 of the occurrence of an event. Typical events may include call reception, message reception, key signal input, touch input, and the like. In addition to audio or video output, the alert unit 153 can provide an output in a different manner to notify of the occurrence of an event. For example, the alarm unit 153 can provide an output in the form of vibrations, and when a call, message, or some other incoming communication is received, the alarm unit 153 can provide a tactile output (ie, vibration) to notify the user of it. By providing such a tactile output, the user is able to recognize the occurrence of various events even when the user's mobile phone is in the user's pocket. The alarm unit 153 can also provide an output of the notification event occurrence via the display unit 151 or the audio output module 152.

The memory 160 may store a software program or the like for processing and control operations performed by the controller 180, or may temporarily store data (for example, a phone book, a message, a still image, a video, etc.) that has been output or is to be output. Moreover, the memory 160 can store data regarding vibrations and audio signals of various manners that are output when a touch is applied to the touch screen.

The memory 160 may include at least one type of storage medium including a flash memory, a hard disk, a multimedia card, a card type memory (eg, SD or DX memory, etc.), a random access memory (RAM), a static random access memory ( SRAM), read only memory (ROM), electrically erasable programmable read only memory (EEPROM), programmable read only memory (PROM), magnetic storage , disk, CD, and more. Moreover, the mobile terminal 100 can cooperate with a network storage device that performs a storage function of the memory 160 through a network connection.

The controller 180 typically controls the overall operation of the mobile terminal. For example, the controller 180 performs the control and processing associated with voice calls, data communications, video calls, and the like. Additionally, the controller 180 can include a multimedia module 1810 that is configured to reproduce (or play back) multimedia data, and the multimedia module 1810 can be constructed within the controller 180 or can be configured to be separate from the controller 180. The controller 180 may perform a pattern recognition process to recognize a handwriting input or a picture drawing input performed on the touch screen as a character or an image.

The power supply unit 190 receives external power or internal power under the control of the controller 180 and provides appropriate power required to operate the various components and components.

The various embodiments described herein can be implemented in a computer readable medium using, for example, computer software, hardware, or any combination thereof. For hardware implementations, the embodiments described herein may be through the use of application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays ( An FPGA, a processor, a controller, a microcontroller, a microprocessor, at least one of the electronic units designed to perform the functions described herein, in some cases, such an embodiment may be at the controller 180 Implemented in the middle. For software implementations, implementations such as procedures or functions may be implemented with separate software modules that permit the execution of at least one function or operation. The software code can be implemented by a software application (or program) written in any suitable programming language, which can be stored in memory 160 and executed by controller 180.

So far, the mobile terminal has been described in terms of its function. Hereinafter, for the sake of brevity, a slide type mobile terminal among various types of mobile terminals such as a folding type, a bar type, a swing type, a slide type mobile terminal, and the like will be described as an example. Therefore, the embodiment of the present invention can be applied to any type of mobile terminal, and is not limited to a slide type mobile terminal.

The mobile terminal 100 as shown in FIG. 1 may be configured to operate using a communication system such as a wired and wireless communication system and a satellite-based communication system that transmits data via frames or packets.

A communication system in which a mobile terminal is operable according to an embodiment of the present invention will now be described with reference to FIG.

Such communication systems may use different air interfaces and/or physical layers. For example, air interfaces used by communication systems include, for example, Frequency Division Multiple Access (FDMA), Time Division Multiple Access (TDMA), Code Division Multiple Access (CDMA), and Universal Mobile Telecommunications System (UMTS) (in particular, Long Term Evolution (LTE)). ), Global System for Mobile Communications (GSM), etc. As a non-limiting example, the following description relates to a CDMA communication system, but such teachings are equally applicable to other types of systems.

Referring to FIG. 2, a CDMA wireless communication system can include a plurality of mobile terminals 100, a plurality of base stations (BS) 270, a base station controller (BSC) 275, and a mobile switching center (MSC) 280. The MSC 280 is configured to interface with a public switched telephone network (PSTN) 290. The MSC 280 is also configured to interface with a BSC 275 that can be coupled to the base station 270 via a backhaul line. The backhaul line can be constructed in accordance with any of a number of well known interfaces including, for example, E1/T1, ATM, IP, PPP, Frame Relay, HDSL, ADSL, or xDSL. It will be appreciated that the system as shown in FIG. 2 may include multiple BSC 2750s.

Each BS 270 can serve one or more partitions (or regions), each of which is covered by a multi-directional antenna or an antenna directed to a particular direction radially away from the BS 270. Alternatively, each partition may be covered by two or more antennas that are set to receive diversity. Each BS 270 can be configured to support multiple frequency allocations, and each frequency allocation has a particular frequency spectrum (eg, 1.25 MHz, 5 MHz, etc.).

The intersection of partitioning and frequency allocation can be referred to as a CDMA channel. BS 270 may also be referred to as a Base Transceiver Subsystem (BTS) or other equivalent terminology. In such a case, the term "base station" can be used to generally refer to a single BSC 275 and at least one BS 270. A base station can also be referred to as a "cell station." Alternatively, each partition of a particular BS 270 may be referred to as a plurality of cellular stations.

As shown in FIG. 2, a broadcast transmitter (BT) 295 transmits a broadcast signal to the mobile terminal 100 operating within the system. A broadcast receiving module 111 as shown in FIG. 1 is provided at the mobile terminal 100 to receive a broadcast signal transmitted by the BT 295. In Figure 2, several Global Positioning System (GPS) satellites 300 are shown. The satellite 300 helps locate at least one of the plurality of mobile terminals 100.

In Figure 2, a plurality of satellites 300 are depicted, but it is understood that useful positioning information can be obtained using any number of satellites. The GPS module 115 as shown in Figure 1 is typically configured to cooperate with the satellite 300 to obtain desired positioning information. Instead of GPS tracking technology or in addition to GPS tracking technology, other techniques that can track the location of the mobile terminal can be used. Additionally, at least one GPS satellite 300 can selectively or additionally process satellite DMB transmissions.

As a typical operation of a wireless communication system, BS 270 receives reverse link signals from various mobile terminals 100. Mobile terminal 100 typically participates in calls, messaging, and other types of communications. Each reverse link signal received by a particular base station 270 is processed within a particular BS 270. The obtained data is forwarded to the relevant BSC 275. The BSC provides call resource allocation and coordinated mobility management functions including a soft handoff procedure between the BSs 270. The BSC 275 also routes the received data to the MSC 280, which provides additional routing services for interfacing with the PSTN 290. Similarly, PSTN 290 interfaces with MSC 280, which forms an interface with BSC 275, and BSC 275 controls BS 270 accordingly to transmit forward link signals to mobile terminal 100.

Based on the above-described mobile terminal hardware structure and communication system, various embodiments of the method of the present invention are proposed.

Mobile phone selfies have gradually become a living habit of people, and the overall effect of the characters in the photos is the focus of attention. Therefore, the pre- or post-beautification of photos is an indispensable step. The beautification of the characters' pictures mainly includes: microdermabrasion, whitening and skin tone adjustment. However, in different environments, the camera is susceptible to ambient lighting, and the acquired image of the character is poorly contrasted, especially in the scenes of backlights and dark lights during self-timer. A single dermabrasion algorithm cannot adapt to such complex scenes.

In this paper, a character dermabrasion algorithm based on adaptive image enhancement is proposed. Firstly, the contrast and illumination parameters of the target image are extracted and the relevant evaluation is given. Then the image preprocessing is performed according to the contrast and illumination parameters of the currently acquired target image. Then, the pre-processed target image is subjected to dermabrasion processing; finally, the target image is post-processed and the final result is output.

As shown in FIG. 3, the first embodiment of the present invention provides an image processing system 01. As shown in FIG. 1, the system includes: an extraction module 02, a calculation module 03, an allocation module 04, a mapping module 05, and Microdermabrasion treatment module 06.

The extraction module 02 is arranged to extract the contrast and illumination parameters of the target image.

In the embodiment of the present invention, for the target image that needs to be processed, we can obtain the contrast and illumination parameters of the target image.

Contrast refers to the measurement of different brightness levels between the brightest white and the darkest black in the dark and dark areas of an image. The larger the difference range is, the larger the contrast is. The smaller the difference range is, the smaller the contrast is, the better. The contrast ratio of 120:1 makes it easy to display vivid, rich colors, and when the contrast ratio is as high as 300:1, it can support the colors of each order.

The contrast is the brightness at the white screen (the brightest) in the darkroom divided by the black screen (the darkest). More precisely, contrast is the subtraction of the white signal between 100% and 0% saturation, divided by the white value of 0% (in illuminance, ie lux, lumens per square meter). The 0% white signal is actually black), the value obtained. Contrast is the division of the whitest and darkest luminance units. Therefore, the brighter the white, the darker the black, the higher the contrast. Strictly speaking, the contrast we refer to is the ratio of the brightness of the brightest point (white) to the darkest (black) at the same point on the screen, but usually the contrast indicator of the product is for the entire screen, for example, a screen is all white. The brightness of the screen is 500 cd/m2, and the brightness of the black screen is 0.5 cd/m2, so the contrast of the screen is 1000:1. Or, for example, if a monitor displays a full white screen (255) with a measured luminance value of 200 cd/m2 and a full black screen with a measured luminance of 0.5 cd/m2, then its contrast ratio is 400:1.

The effect of contrast on the visual effect is very important. Generally speaking, the contrast is larger, the image is more clear and conspicuous, and the color is more vivid and bright; while the contrast is small, the whole picture will be grayed out. High contrast is very helpful for image sharpness, detail performance, and gray level performance. In some black and white contrast text display, computer-aided design CAD display and black-and-white photo display, high-contrast products have advantages in black and white contrast, clarity and integrity.

The illumination parameter is a parameter that reflects the brightness characteristic of the target image as a whole. The larger the parameter, the brighter the target image, and the smaller the parameter, the darker the target image. Lightness is a property of color, or a dimension of a color space that is related to how bright a color is. In the Lab color space, brightness is defined to reflect the subjective bright feelings of humans. The unit of brightness is candela per square meter or square candle light cd/m2. Brightness is an important indicator of the luminous intensity of a TV screen or computer monitor. The three elements that make up the color of the image: brightness (brightness), saturation (saturation), and hue. Contrast indicates the degree of difference in degree, and the contrast is large, that is, "bright over, dark, dark". Brightness is the brightness of the picture.

Optionally,

The extraction module 02 extracts the contrast of the target image including:

Extracting a gray value of a pixel at each coordinate position on the target image;

Calculating a gray value difference of pixels on each adjacent coordinate position according to the gray value of the pixel at each of the extracted coordinate positions;

Comparing the calculated absolute value of the gray value difference of the pixel at each adjacent coordinate position with a preset empirical value;

Obtaining one or more absolute values of gray value differences greater than a preset empirical value according to the comparison result;

Calculating a sum of squares of absolute values of one or more gray value differences greater than the preset empirical value;

Alternatively, the contrast of the target image can be extracted according to the following equation:

Where P _i is the absolute value difference of adjacent pixels, gray ⁱ is the gray value of the pixel at the position of coordinate i, i is a positive integer; gray ^j is the gray value of the pixel at the position of coordinate j, j is a positive integer, i And j is an adjacent coordinate; threshold is a previously obtained empirical value; M is a total amount of pixels of the target image that is pre-stated; C is a contrast of the target image; a larger C indicates a higher contrast of the target image, C The smaller the value, the lower the contrast of the target image.

The extraction module 02 extracts the illumination parameters of the target image, including:

Calculating a sum of gray values of pixels at each coordinate position on the target image;

Alternatively, the illumination parameters of the target image may be extracted according to the following equation:

The calculation module 03 is configured to calculate a clipping coefficient of the target image according to the extracted contrast and illumination parameters.

In the embodiment of the present invention, the contrast of the target image is realized in a global scope, similar to the concept of Contrast Limited Adaptive histgram equalization (CLAHE). And lighting adjustments. Below we will introduce the CLAHE algorithm in detail.

The CLAHE algorithm achieves contrast-limited adaptive image enhancement by dividing the image into multiple sub-regions, then performing histogram equalization on each sub-region, and then interpolating each pixel to obtain the transformed gray value.

The difference between CLAHE and ordinary adaptive histogram equalization is mainly its contrast limiting. This feature can also be applied to global histogram equalization, which is the so-called Limit Contrast Histogram Equalization (CLHE), but this is rarely used in practice. In CLAHE, contrast is limited for each small area. CLAHE is mainly used to overcome the problem of excessively amplified noise of AHE (Adaptive histgram equalization).

This is mainly achieved by limiting the degree of contrast improvement of the AHE algorithm. The contrast magnification around the specified pixel value is primarily determined by the slope of the transform function. This slope is proportional to the slope of the cumulative histogram of the field. The CLAHE achieves the purpose of limiting the amplification by cropping the histogram with a predefined threshold before calculating the CDF (cumulative histogram function). This limits the slope of the CDF and therefore limits the slope of the transformation function. The value that the histogram is cropped, also known as clipping clipping or clipping height, depends on the distribution of the histogram and therefore on the size of the field.

In general, it is not good to simply ignore the parts that are beyond the clipping limit of the histogram. You can evenly distribute these cropped parts to other parts of the histogram. As shown in Figure 4. This redistribution process may cause those parts that have been cropped to again exceed the crop value (as shown in Figure 4). If this is not desired, repeated cropping can be employed to achieve the desired effect.

The cropping of the histogram in CLAHE can be manually adjusted by hand to obtain a suitable clipping height. Here, the size of the clipping factor determines the slope of the adjustment curve used to adjust the target image. And the CLAHE is a local adjustment, and the local adjustment can better highlight the detailed information of the image, but since the histogram of each image block needs to be calculated multiple times, the calculation amount of the algorithm is large. This paper first extracts the contrast C and illumination parameters l of the target image through the extraction module 02. Then, the clipping coefficient is automatically calculated by the obtained contrast C and illumination parameter l, which greatly reduces the amount of calculation.

Optionally, the calculating module 03 calculates the clipping coefficient of the target image according to the extracted contrast and illumination parameters, including:

Dividing the illumination parameter of the extracted target image by a preset normalization coefficient, and comparing the obtained quotient as a contrast coefficient after normalizing the contrast;

Alternatively, the contrast ratio after the normalization of the contrast can be calculated according to the following equation:

Where L is the contrast coefficient after normalization of contrast; l is the illumination parameter of the target image; a is the preset normalization coefficient.

Calculate the clipping factor of the target image according to the following equation:

Cp=C*L,

Where cp is the clipping factor; C is the contrast of the target image; and L is the contrast coefficient after normalization of the contrast.

The assignment module 04 is arranged to redistribute the histogram of the target image based on the calculated crop coefficients.

A histogram is a two-dimensional statistical graph whose two coordinates are a measure of the statistical sample and the corresponding attribute of the sample. Optionally, the histogram abscissa of the image is a pixel value, and the ordinate is a two-dimensional statistical graph of the number of pixels corresponding to each pixel value.

In the embodiment of the present invention, after the clipping coefficient of the histogram is calculated by the calculation module 03, the histogram of the target image may be re-allocated according to the calculated clipping coefficient, that is, the histogram is calculated according to the calculated clipping coefficient in advance. The height is cut, and the histogram is cropped by the crop height to realize the redistribution of the histogram to achieve the purpose of limiting the magnification.

Optionally, the calculation module 03 is further configured to:

Before estimating the adjustment curve of the target image from the histogram, calculate the histogram by the following steps Crop height: Calculate the product of the clipping factor and the maximum number of pixels corresponding to the pixel value in the histogram, and use the calculated product as the crop height of the histogram.

Alternatively, the crop height of the histogram can be calculated by the following equation:

cpCountNum=cp*countNum,

Where cp is the clipping coefficient; countNum is the maximum number of pixels corresponding to the pixel value in the histogram; cpCountNum is the clipping height of the histogram.

The mapping module 05 is configured to estimate an adjustment curve of the target image according to the re-allocated histogram, and perform global tone mapping on the target image according to the adjustment curve.

Tone mapping is a computer graphics technique that approximates high dynamic range images on a finite dynamic range medium. Print results, CRT or LCD displays, and projectors all have a limited dynamic range. Essentially, the problem with tone mapping is to make a large contrast reduction to change the brightness of the scene to a displayable range, while maintaining image detail and color information that is important for the original scene.

In the embodiment of the present invention, the fitting method of the adjustment curve and the tone mapping method of the target image are not limited, and any fitting method and mapping method that can be implemented are used to realize the fitting of the histogram to the adjustment curve and the target image. Global mapping.

The dermabrasion processing module 06 is configured to perform dermabrasion processing on the target image subjected to global tone mapping according to a preset dermabrasion algorithm.

In the embodiment of the present invention, the pre-processing of the target image is completed by the global mapping of the target image by the mapping module 05, and the pre-processed image can be ground by the dermabrasion processing module 06 of the embodiment of the present invention. The skin was processed.

Microdermabrasion, which uses layers, masks, channels, tools, filters, or other software in the PS software to remove spots, blemishes, and mottled parts of the skin from the characters in the image. Using photoshop to polish the face of a person can make the face of the character more delicate, smooth, and the outline is clearer.

In an embodiment of the invention, the channel dermabrasion algorithm comprises the following steps:

1. Open the image, enter the channel palette, and copy the blue channel;

2. Perform filter \ other \ high contrast retention on the blue channel copy;

3. Use a straw tool to absorb the adjacent color, and then cover the part to be protected with a brush, including the shadows of the eyes, nose, eyebrows, mouth, and hair;

4, image \ adjust \ calculation, generate Alpha1 channel, and set parameters in the channel;

5. Load the selection by default operation (hold down the Ctrl key and click the Alpha1 channel with the mouse), and reverse the selection by default operation (such as Shift+Ctrl+I), return to the layer palette, click to activate the background layer, and then create a Curve the adjustment layer, adjust the curve, and observe the change of the image; at this time, do not rush to completely remove the spots, but greatly reduce them, because the previous operation is repeated below;

6. By stamping the visible layer by pressing the preset operation (press Shift+Ctrl+Alt+E), repeat the previous operation for it; the following operating parameters are performed by own observation; the principle of grasping is to carry out all traces. The adjustment is to achieve the purpose of maintaining the balance of the image tone and the effect of removing the spot. For example, if you find some yellow spots in the dark, including the hair on the face, take the sponge tool in the toolbox, the mode option is to go Color, set a small value to carefully wipe the stain, then use the brush tool to select the adjacent color to paint (the brush uses color mode).

In the embodiment of the present invention, the post-skinned image is subjected to post-processing, which mainly performs a series of operations such as image skin color and image sharpening, and the steps are mainly to make the overall style of the characters in the image more natural. At the same time, improve the overall visual effect of the image. For example, it can be implemented in the following manner: Example 1: skin color adjustment, sharpening; example two: whitening, sharpening.

Optionally, the above image processing system may be disposed in the mobile terminal.

The extraction module 02, the calculation module 03, the distribution module 04, the mapping module 05, and the dermabrasion processing module 06 may be disposed in the controller 180 in FIG.

At this point, all the basic features of the solution of the embodiment of the present invention have been described. It should be noted that the above content is only a specific embodiment of the present invention, and may not be the final solution of the present invention. In other embodiments, other The embodiments are the same or similar to the embodiments of the present invention, and any combination of the essential features of the present invention is within the scope of the present invention.

In addition, an embodiment of the present invention further provides an image processing method. As shown in FIG. 5, the method includes the following steps:

S101. Extract contrast and illumination parameters of the target image.

Optionally,

The contrast of the extracted target image includes:

Calculating a sum of squares of absolute values of one or more gray value differences greater than a preset empirical value;

The lighting parameters for extracting the target image include:

Where gray ⁱ is the gray value of the pixel at the position of the coordinate i, i is a positive integer; M is the total number of pixels of the target image that is pre-stated; l is the illumination parameter of the target image.

S102. Calculate a clipping coefficient of the target image according to the extracted contrast and illumination parameters.

Optionally, calculating the cropping coefficient of the target image according to the extracted contrast and illumination parameters includes: dividing the illumination parameter of the extracted target image by a preset normalization coefficient, and normalizing the quotient obtained by dividing the contrast as a contrast Contrast factor in the future.

Cp=C*L

Optionally, the method further includes:

cpCountNum=cp*countNum

S103. Re-allocate a histogram of the target image according to the calculated clipping coefficient.

S104. Estimate an adjustment curve of the target image according to the re-allocated histogram, and perform global tone mapping on the target image according to the adjustment curve.

S105. Perform dermabrasion processing on the target image subjected to global tone mapping according to a preset dermabrasion algorithm.

The solution of the embodiment of the present invention can overcome the shortcomings that the current single dermabrasion technology cannot adapt to complex scenes, improve the user experience of the camera, and effectively improve the product competitiveness of the portable device camera.

The embodiment of the invention further provides a computer storage medium, wherein the computer storage medium stores computer executable instructions, and the computer executable instructions are used to execute the method described in the foregoing embodiments.

It is to be understood that the term "comprises", "comprising", or any other variants thereof, is intended to encompass a non-exclusive inclusion, such that a process, method, article, or device comprising a series of elements includes those elements. It also includes other elements that are not explicitly listed, or elements that are inherent to such a process, method, article, or device. An element that is defined by the phrase "comprising a ..." does not exclude the presence of additional equivalent elements in the process, method, item, or device that comprises the element.

The serial numbers of the embodiments of the present invention are merely for the description, and do not represent the advantages and disadvantages of the embodiments.

Those of ordinary skill in the art will appreciate that all or some of the steps, systems, and functional blocks/units of the methods disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof. In a hardware implementation, the division between functional modules/units mentioned in the above description does not necessarily correspond to the division of physical units; for example, one physical component may have multiple functions Yes, or a function or step can be performed cooperatively by several physical components. Some or all of the components may be implemented as software executed by a processor, such as a digital signal processor or microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on a computer readable medium, which may include computer storage media (or non-transitory media) and communication media (or transitory media). As is well known to those of ordinary skill in the art, the term computer storage medium includes volatile and nonvolatile implemented in any method or technique for storing information, such as computer readable instructions, data structures, program modules or other data. Sex, removable and non-removable media. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disc (DVD) or other optical disc storage, magnetic cartridge, magnetic tape, magnetic disk storage or other magnetic storage device, or may Any other medium used to store the desired information and that can be accessed by the computer. Moreover, it is well known to those skilled in the art that communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal, such as a carrier wave or other transport mechanism, and can include any information delivery media.

The above is only an alternative embodiment of the present invention, and thus does not limit the scope of the invention, and the equivalent structure or equivalent process transformation made by using the specification and the drawings of the present invention, or directly or indirectly applied to other related technologies. The fields are all included in the scope of patent protection of the present invention.

Industrial applicability

The above technical solution can overcome the shortcomings that the single microdermabrasion technology in the current mobile terminal cannot adapt to a complicated scene, especially in a scene such as backlight and dark light when the character is self-photographed, which can beautify the photographed person and improve the user experience of the camera, and can Effectively improve the competitiveness of portable device cameras.

Claims

An image processing system (01), the system (01) comprising: an extraction module (02), a calculation module (03), an allocation module (04), a mapping module (05), and a dermabrasion processing module (06);

The extraction module (02) is configured to extract contrast and illumination parameters of the target image;

The calculating module (03) is configured to calculate a clipping coefficient of the target image according to the extracted contrast and illumination parameters;

The distribution module (04) is configured to reallocate a histogram of the target image according to the calculated clipping coefficient;

The mapping module (05) is configured to estimate an adjustment curve of the target image according to the re-allocated histogram, and perform global tone mapping on the target image according to the adjustment curve;

The dermabrasion processing module (06) is configured to perform dermabrasion processing on the target image subjected to the global tone mapping according to a preset dermabrasion algorithm.
The image processing system (01) of claim 1 wherein said extraction module (02) is arranged to achieve contrast of the extracted target image by:

Extracting a gray value of a pixel at each coordinate position on the target image;

Calculating a gray value difference of pixels on each adjacent coordinate position according to the gray value of the pixel at each of the extracted coordinate positions;

Comparing the calculated absolute value of the gray value difference of the pixel at each adjacent coordinate position with a preset empirical value;

Acquiring one or more absolute values of gray value differences greater than the preset empirical value according to the comparison result;

Calculating a sum of squares of absolute values of the one or more gray value differences greater than the preset empirical value;

The calculated value of the sum of squares is divided by the total number of pixels of the target image counted in advance, and the quotient obtained by dividing is used as the contrast of the target image.
The image processing system (01) according to claim 1, wherein said extraction module (02) is configured to implement an illumination parameter of the extraction target image by:

Calculating a sum of gray values of pixels at each coordinate position on the target image;

The sum of the calculated gradation values is divided by the total number of pixels of the target image counted in advance, and the quotient obtained by dividing is used as the illumination parameter of the target image.
The image processing system (01) according to claim 2 or 3, wherein said calculation module (03) is arranged to calculate a cropping coefficient of said target image based on the extracted contrast and illumination parameters by:

Dividing the extracted illumination parameter of the target image by a preset normalization coefficient, and comparing the obtained quotient as a contrast coefficient after normalizing the contrast;

A product of the contrast coefficient and the contrast of the extracted target image is calculated, and the calculated product is used as a clipping coefficient of the target image.
The image processing system (01) of claim 4,

The calculation module (03) is further configured to calculate, before the adjustment module (04) estimates the adjustment curve of the target image according to the histogram, the clipping height of the histogram, including:

A product of the clipping coefficient and the maximum number of pixels corresponding to the pixel value in the histogram is calculated, and the calculated product is used as the clipping height of the histogram.
The image processing system (01) according to claim 1, wherein

The preset dermabrasion algorithm includes: a single channel dermabrasion algorithm and a three-channel dermabrasion algorithm based on a ridge filter.
The image processing system (01) according to claim 5, wherein

The histogram abscissa of the image is a pixel value, and the ordinate is a two-dimensional statistical chart of the number of pixels corresponding to each pixel value.
The image processing system (01) according to claim 2, wherein said extraction module (02) is arranged to achieve contrast of the extracted target image by:

Where P i is the absolute value difference of adjacent pixels, gray i is the gray value of the pixel at the position of coordinate i, i is a positive integer; gray j is the gray value of the pixel at the position of coordinate j, j is a positive integer, i And j is an adjacent coordinate; threshold is a previously obtained empirical value; M is a total amount of pixels of the target image that is pre-stated; C is a contrast of the target image.
The image processing system (01) according to claim 3, wherein said extraction module (02) is configured to implement an illumination parameter of the extraction target image by:

Where, gray i is the gray value of the pixel at the position of the coordinate i, i is a positive integer; M is the total number of pixels of the target image measured in advance; l is the illumination parameter of the target image, l∈[0, 255].
The image processing system (01) according to claim 4, wherein said calculation module (03) is arranged to calculate a cropping coefficient of said target image based on the extracted contrast and illumination parameters by:

L is the contrast coefficient after normalization of contrast; l is the illumination parameter of the target image; a is the preset normalization coefficient;

Cp=C*L

Cp is the clipping factor; C is the contrast of the target image; L is the contrast factor after the normalization of the contrast.
An image processing method, the method comprising the steps of:

Extracting contrast and illumination parameters of the target image (S101);

Calculating a clipping coefficient of the target image according to the extracted contrast and illumination parameters (S102);

Re-allocating a histogram of the target image according to the calculated clipping coefficient (S103);

Estimating an adjustment curve of the target image according to the re-allocated histogram, and performing global tone mapping on the target image according to the adjustment curve (S104);

The target image subjected to the global tone mapping is subjected to a dermabrasion process according to a preset dermabrasion algorithm (S105).
The image processing method according to claim 11, wherein

The contrast of the extraction target image includes:

Extracting a gray value of a pixel at each coordinate position on the target image;

Calculating a gray value difference of pixels on each adjacent coordinate position according to the gray value of the pixel at each of the extracted coordinate positions;

Comparing the calculated absolute value of the gray value difference of the pixel at each adjacent coordinate position with a preset empirical value;

Acquiring one or more absolute values of gray value differences greater than the preset empirical value according to the comparison result;

Calculating a sum of squares of absolute values of the one or more gray value differences greater than the preset empirical value;

The calculated value of the sum of squares is divided by the total number of pixels of the target image counted in advance, and the quotient obtained by dividing is used as the contrast of the target image.
The image processing method according to claim 11, wherein

The illumination parameters of the extraction target image include:

Calculating a sum of gray values of pixels at each coordinate position on the target image;

The sum of the calculated gradation values is divided by the total number of pixels of the target image counted in advance, and the quotient obtained by dividing is used as the illumination parameter of the target image.
The image processing method according to claim 12 or 13, wherein the calculating the cropping coefficient of the target image based on the extracted contrast and illumination parameters comprises:

Dividing the extracted illumination parameter of the target image by a preset normalization coefficient, and comparing the obtained quotient as a contrast coefficient after normalizing the contrast;

A product of the contrast coefficient and the contrast of the extracted target image is calculated, and the calculated product is used as a clipping coefficient of the target image.
The image processing method according to claim 14, further comprising:

Before estimating the adjustment curve of the target image according to the histogram, the clipping height of the histogram is calculated by the following steps:

Calculating a maximum number of pixels corresponding to the clipping coefficient and the pixel value in the histogram The product of which is the calculated product as the crop height of the histogram.
The image processing method according to claim 11, wherein

The preset dermabrasion algorithm includes: a single channel dermabrasion algorithm and a three-channel dermabrasion algorithm based on a ridge filter.
The image processing method according to claim 15, wherein

The histogram abscissa of the image is a pixel value, and the ordinate is a two-dimensional statistical chart of the number of pixels corresponding to each pixel value.
The image processing method according to claim 12, wherein

The contrast of the extraction target image includes:

Where P i is the absolute value difference of adjacent pixels, gray i is the gray value of the pixel at the position of coordinate i, i is a positive integer; gray j is the gray value of the pixel at the position of coordinate j, j is a positive integer, i And j is an adjacent coordinate; threshold is a previously obtained empirical value; M is a total amount of pixels of the target image that is pre-stated; C is a contrast of the target image.
The image processing method according to claim 13, wherein

The illumination parameters of the extraction target image include:

Where, gray i is the gray value of the pixel at the position of the coordinate i, i is a positive integer; M is the total number of pixels of the target image measured in advance; l is the illumination parameter of the target image, l∈[0, 255].
The image processing method according to claim 14, wherein

Calculating the cropping coefficients of the target image according to the extracted contrast and illumination parameters includes:

L is the contrast coefficient after normalization of contrast; l is the illumination parameter of the target image; a is the preset normalization coefficient;

Cp=C*L

Cp is the clipping factor; C is the contrast of the target image; L is the contrast factor after the normalization of the contrast.