Abstract:
A decryption engine includes an update circuit, a key generator, a decryption circuit and a detection circuit. The update circuit generates a first updating information based on a premise of that a currently received frame is encrypted, and generates a second updating information based on a premise of that the currently received frame is non-encrypted. The key generator produces a first key according to the first updating information, and produces a second key according to the second updating information. The decryption circuit generates a first decrypted frame according to the first key and the currently received frame, and generates a second decrypted frame according to the second key and the currently received frame. The detection circuit detects whether the currently received frame is decrypted according to the first decrypted frame and the second decrypted frame, to generate an encryption detection result.

Description:
BACKGROUND OF THE INVENTION 
       [0001]    1. Field of the Invention 
         [0002]    The disclosed embodiments of the present invention relate to an image system, and more particularly, to a de-noising method and a related image system. 
         [0003]    2. Description of the Prior Art 
         [0004]    In the real-time digital image process, there are mainly two kinds of de-noising methods. The first kind of de-noising method is performed in a spatial domain, such as Gaussian filtering, median filtering, bilateral filtering, and non-local means (NLM) filtering with good effect. However, these spatial domain de-noising method needs a huge calculation amount to obtain a better effect, and there are side effects of image blur and details loss inevitably. 
         [0005]    The second kind of de-noising method is performed in a time domain, which considers a previous frame and a current frame at the same time with an appropriate weighted average in order to achieve the de-noising effect. Compared to the first kind of de-noising method, the top advantage is that it almost does not cause image blur or the detail loss, but the time domain de-noising method may easily increase the ghosting, or make the image not natural. Minimizing the side effects often requires very complex operation. 
         [0006]    In order to improve problems of the de-noising methods of time domain and space domain, it is also practical to merge the two kinds of methods, but a de-noising method using the time domain and the space domain at the same time will have three major problems: the first problem is a serious ghosting effect; the second problem is low image resolution; and the third problem is that when the noise is bigger, especially when the image capturing device is in a low light environment, or the image is affected by the lens shading around, the de-noising effect will be reduced. 
         [0007]    Thus, a de-noising method with low complexity and high efficiency is required in this field to improve the above problems. 
       SUMMARY OF THE INVENTION 
       [0008]    It is therefore one of the objectives of the present invention to provide a de-noising method and a related image system, so as to solve the above-mentioned problem. 
         [0009]    In accordance with a first embodiment of the present invention, an exemplary de-noising method is disclosed. The de-noising method comprises: receiving a pixel of a current frame; deriving a de-noising coefficient according to a specific information corresponding to the pixel; and generating an output pixel by allocating a weight of the pixel and a weight of at least one pixel of a previous frame according to the de-noising coefficient, wherein the at least one pixel of the previous frame includes a co-located pixel. 
         [0010]    In accordance with a second embodiment of the present invention, an exemplary image system is disclosed. The image system comprises: a lens module, an image and signal processor, and a de-noising unit. The lens module is utilized for capturing an image information. The image and signal processor is coupled to the lens module, and utilized for converting the image information to a frame. The de-noising unit is coupled to the image and signal processor, and utilized for: receiving a pixel of the frame; deriving a de-noising coefficient according to a specific information corresponding to the pixel; and generating an output pixel by allocating a weight of the pixel and a weight of at least one pixel of a previous frame according to the de-noising coefficient, wherein the at least one pixel of the previous frame includes a co-located pixel. 
         [0011]    In accordance with a second embodiment of the present invention, an exemplary image system is disclosed. The image system comprises: a lens module, an image and signal processor, a brightness adjusting unit, and a de-noising unit. The lens module is utilized for capturing an image information. The image and signal processor is coupled to the lens module, and utilized for converting the image information to a frame. The brightness adjusting unit is coupled between the image and signal processor and the lens module, and utilized for generating an exposure control signal to the lens module according to an automatic exposure information and generating a frame rate information to a de-noising unit. The de-noising unit is utilized for: receiving a pixel of the frame; deriving a de-noising coefficient according to a specific information corresponding to the pixel; and generating an output pixel by allocating a weight of the pixel and a weight of at least one pixel of a previous frame according to the de-noising coefficient, wherein the at least one pixel of the previous frame includes a co-located pixel, and at least one pixel of the previous frame further comprises at least one pixel surround the co-located pixel. 
         [0012]    In accordance with a second embodiment of the present invention, an exemplary image system is disclosed. The image system comprises: a lens module, an image and signal processor, a brightness adjusting unit, and a de-noising unit. The lens module is utilized for capturing an image information. The image and signal processor is coupled to the lens module, and utilized for converting the image information to a frame. The brightness adjusting unit is coupled between the image and signal processor and the lens module, and utilized for generating an exposure control signal to the lens module according to an automatic exposure information and generating a frame rate information to a de-noising unit. The de-noising unit is utilized for performing a spatial domain de-noising process and a time domain de-noising process at least according to the frame rate information and a pixel of the frame, so as to generate an output pixel. 
         [0013]    Briefly summarized, the spirit of the present invention is using an adaptivity method to dynamically determine a ratio of the time domain de-noising, and further adding the spatial domain de-noising to achieve a real-time 3D de-noising method. 
         [0014]    These and other objectives of the present invention will no doubt become obvious to those of ordinary skill in the art after reading the following detailed description of the preferred embodiment that is illustrated in the various figures and drawings. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0015]      FIG. 1  is a simplified schematic diagram illustrating a real-time adaptability 3D dynamic de-noising method according to the present invention. 
           [0016]      FIG. 2  is a diagram of a filtering function ƒ 2  in accordance with an embodiment of the present invention. 
           [0017]      FIG. 3  shows a flowchart of an exemplary real-time adaptability 3D dynamic de-noising method in accordance with a first embodiment of the present invention 
           [0018]      FIG. 4  is a relation diagram of the brightness and the Weber threshold value of the present invention. 
           [0019]      FIG. 5  is a relation diagram of the motion strength and the preposed de-noising coefficient of the present invention. 
           [0020]      FIG. 6  is a relation diagram of the distance from the center point of the frame and the adjusting coefficient in accordance with an embodiment of the present invention. 
           [0021]      FIG. 7  is a relation diagram of the distance from the center point of the frame and the adjusting coefficient in accordance with another embodiment of the present invention. 
           [0022]      FIG. 8  shows a flowchart of an exemplary real-time adaptability 3D dynamic de-noising method in accordance with a second embodiment of the present invention. 
           [0023]      FIG. 9  is a block diagram of an image system in accordance with an embodiment of the present invention. 
           [0024]      FIG. 10  shows a flowchart of an exemplary real-time adaptability 3D dynamic de-noising method in accordance with a third embodiment of the present invention. 
       
    
    
     DETAILED DESCRIPTION 
       [0025]    Certain terms are used throughout the description and following claims to refer to particular components. As one skilled in the art will appreciate, manufacturers may refer to a component by different names. This document does not intend to distinguish between components that differ in name but not function. In the following description and in the claims, the terms “include” and “comprise” are used in an open-ended fashion, and thus should be interpreted to mean “include, but not limited to . . . ”. Also, the term “couple” is intended to mean either an indirect or direct electrical connection. Accordingly, if one device is coupled to another device, that connection may be through a direct electrical connection, or through an indirect electrical connection via other devices and connections. 
         [0026]    In general, in order to obtain a better de-noising effect, the characteristics of the noises have to be analyzed at first. There are two kinds of common static state image noises: salt and pepper noise and Gaussian noise. However, for the general image capturing devices, since the captured images are dynamic and the noises of each frame might be different, and the noises of each point are twinkling constantly for the vision (i.e. the whole frame is full of twinkling noises), the effect of using the spatial domain to perform the de-noising process will not be ideal in this condition, and it is more proper to use the time domain filter or use the time domain plus the spatial domain to perform the de-noising process. 
         [0027]    The spirit of the present invention is using an adaptivity method to dynamically determine a ratio of the time domain de-noising, and further adding the spatial domain de-noising to achieve a real-time 3D de-noising method. In the 3D de-noising method, the way of how to allocate the time domain de-noising strength (effect) will directly affect the user feeling. The present invention is suitable for all camera modules and shot environments. In a low light environment, for example, two different time points of captured frame are not only full of static noise, buy also contains dynamic twinkling noises. Therefore, the present invention can reduce the dynamic twinkling noise to enhance the visual perception in as far as possible under the condition of no loss of image details. In addition, the computational cost of the present invention is very low, and the present invention can be used in a variety of different ways of implementation, such as a hardware (such as a chip), a software (such as a driver, an application) or a firmware or a part or all of their combination. 
         [0028]    Please refer to  FIG. 1 .  FIG. 1  is a simplified schematic diagram illustrating a real-time adaptability 3D dynamic de-noising method according to the present invention. Equation (1) is the basic idea of the present invention, based on a current frame and a previous frame for a filter process. Please note that the previous frame is not limited to previous one frame. The filter process can be expressed as follows: 
         [0000]        P   out   =P   in   ×C   denoising +ƒ 3 ( q )×(1− C   denoising )  (1)
 
         [0029]    P in  is a value of a pixel in the current frame, and q is a value of another pixel in the corresponding position in the previous frame (co-located pixel), P out  is a result generated by the filtering process (i.e. a new value of the pixel in the current frame). More specifically, an integrated de-noising coefficient C denoising  is utilized here, and a dynamic determining method is utilized for determining an integrated de-noising coefficient C denoising  which is most suitable for the pixel. As shown in the equation (1), when the integrated de-noising coefficient C denoising  is larger, the output value is determined more by the value P in  of the pixel in the current frame. When the integrated de-noising coefficient C denoising  is smaller, the output value is determined more by the value q of the pixel in the corresponding position in the previous frame. In other words, when the integrated de-noising coefficient C denoising  in  FIG. 1  is larger, the effect and strength of the filtering process for the 3D time domain are weaker. When the integrated de-noising coefficient C denoising  in  FIG. 1  is smaller, the effect and strength of the filtering process for the 3D time domain are stronger. One of the key figure of the present invention is how to determine the integrated de-noising coefficient C denoising  most suitable for each pixel in the current frame. About the filtering function ƒ 3 , it is utilized for processing another pixel in the corresponding position in the previous frame. For example, the filtering function ƒ 3  can be the conventional de-noising filtering method of spatial domain such as the median filtering method, the bilateral filtering method, and the non-local means (NLM) filtering method, and the present invention is not limited to these filtering methods. In a preferred embodiment, the filtering function ƒ 3  is belong to an edge protection filtering method to keep the details as far as possible. 
         [0030]    The above equation (1) can be further represented in equation (2) as follows. 
         [0000]        P   out   =P   in ×ƒ 1 (ƒ 2 ( C   1   ,C   2   , . . . ,C   n ))+ƒ 3 ( q )×(1−ƒ 1 (ƒ 2 ( C   1   ,C   2   , . . . ,C   n ))  (2)
 
         [0031]    The integrated de-noising coefficient C denoising  in the equation (1) is represented by ƒ 1 (ƒ 2 (C 1 , C 2 , . . . , C n )). The filtering function ƒ 1  is a global mapping function, and this function can perform a whole adjustment for the de-noising coefficient. For example, it is practical to use the filtering function ƒ 1  to perform a global gain process for an input to directly change the input strength according to the characteristics of the lens and/or the light sensing element, and generate an output to obtain the stable effect and prevent from affected by different lens, and the present invention is not limited to this condition. If the output of the filtering function ƒ 1  is larger than the input, it means that the filtering function ƒ 1  increases the input strength. If the output of the filtering function ƒ 1  is smaller than the input, it means that the filtering function ƒ 1  decreases the input strength. 
         [0032]      FIG. 2  is a diagram of a filtering function ƒ 2  in accordance with an embodiment of the present invention, wherein an input of the filtering function ƒ 2  is a number n of individual de-noising coefficients corresponding to a number n of previous frames (i.e. frame m−1˜frame m-n) of a current frame m. The individual de-noising coefficient C 1  is derived according to the current frame m and the previous frame m−1. The individual de-noising coefficient C 2  is derived according to the current frame m and the previous frame m−2, and so on, where n is a positive integer greater than or equal to 1, and if n is 1, said only refer to the previous frame. The filtering function ƒ 2  is utilized for filtering each individual de-noising coefficient C 1 , C 2 , . . . , C n  to obtain the integrated de-noising coefficient C denoising . The filtering method of the filtering function ƒ 2  can be different method, such as Gaussian filtering method or median filtering method. Or, the output of the filtering function ƒ 2  can be a maximum value of C 1 ˜C n , to reduce the strength of the de-noising effect of the time domain as far as possible, so as to reduce the probability of the occurrence of the ghosting. The output of the filtering function ƒ 2  also can be a mean value of C 1 ˜C n , to average use the individual de-noising coefficients of the current frame and the number n of previous frames, so as to reduce the probability of the occurrence of the error. However, the present invention is not limited to the embodiment in  FIG. 2 , or the above example. In addition, please note that the equation (2) should be performed for each pixel in the current frame, and continue to repeat the calculation when information of a next frame is received. 
         [0033]    Please refer to  FIG. 3 .  FIG. 3  shows a flowchart of an exemplary real-time adaptability 3D dynamic de-noising method in accordance with a first embodiment of the present invention, comprising five main steps of skin recognition, Weber-Fechner Law, motion estimation, distance condition, and 3D de-noising. Provided that substantially the same result is achieved, the steps of the process flowchart do not have to be in the exact order shown in  FIG. 3  and need not be contiguous, meaning that other steps can be intermediate. In addition, some steps in  FIG. 3  can be omitted according to different embodiments or design requirements. 
         [0034]    In the step  302  in  FIG. 3 , the main purpose is to determine the area of the skin color. The area of the skin color is probably the human body part (especially the human face), which tends to have a larger motion, and usually is most attention by the user&#39;s eyes. Thus, the skin recognition can be utilized for prevent the human face from generating un-natural image or ghosting. The step  302  can use the conventional human face identifying method, such as using whether the values of red (R), green (G) and blue (B) channels of the pixel fit R&gt;G&gt;B to determine the area of the skin color. A skin color threshold value thd skin  is set, wherein when an area is closer to the skin color, skin color, the skin color threshold value thd skin  will be lower. When an area is not closer to the skin color, the skin color threshold value thd skin  will be higher. The skin color threshold value thd skin  will be utilized in the motion estimation in the step  306 . 
         [0035]    In the step  304 , the motion adjustment is performed according to the brightness based on Weber-Fechner Law. Weber-Fechner Law applied to image processing can get the following conclusion: for a fixed size of noise, in the place of the higher brightness, the noise is harder to be paid attention by the human&#39;s eyes, and in the place of the lower brightness, the noise is easier to be paid attention by the human&#39;s eyes. Thus, according to the above conclusion, a dynamic Weber threshold value thd weber  is designed in the step  304 , wherein thd weber     —     min ≦Weber threshold value thd weber ≦thd weber     —     max .  FIG. 4  is a relation diagram of the brightness and the Weber threshold value of the present invention. As shown in  FIG. 4 , when the brightness is higher, the Weber threshold value thd weber     —     min  is higher, and when the brightness is lower, the Weber threshold value thd weber     —     min  is lower. The Weber threshold value thd weber     —     min  will be utilized in the motion estimation in the step  306 . 
         [0036]    In the step  306 , a motion strength Difference between the current frame and the previous k (k=1˜n) frame is calculated. When the motion strength Difference is larger, it means that the motion level is higher, and when the motion strength Difference is smaller, it means that the motion level is lower. The motion strength Difference is defined as follows: 
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         [0037]    *is a representative of the rotating calculation, and p i,j  is a representative of a current pixel of coordinate position (i,j), and q i,j  is a representative of a pixel of coordinate position (i,j) in a previous frame. 
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         [0000]    is a representative of together with the surrounding pixels to process the pixels into calculation in order to reduce the error. 
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         [0000]    That is, higher weights are allocated for the pixels to process in the middle, and lower weights are allocated for the surrounding pixels. There are details about process of filling or image for the edge or corner pixels. The details are all well known to those of average skill in this art, and thus further explanation of the details and operations are omitted herein for the sake of brevity. 
         [0038]    As mentioned above, when the motion strength Difference is larger, it means that the motion level is higher, and it means that the pixel tends to not need filtering process in time domain to reduce the side effects of the ghosting, and thus the corresponding filtering coefficient is larger. When the motion strength Difference is smaller, the corresponding filtering coefficient is smaller. A first dynamic threshold value thd dynamic1  is obtained by adding the skin color threshold value thd skin , the Weber threshold value thd weber     —     min , and a first predetermined threshold value thd 1 , and a second dynamic threshold value thd dynamic1  is obtained by adding the skin color threshold value thd skin , the Weber threshold value thd weber     —     min , and a second predetermined threshold value thd 2 , as shown in equation (4) and equation (5). 
         [0000]        thd   dynamic1   =thd 1+ thd   skin   +thd   weber   (4)
 
         [0000]        thd   dynamic2   =thd 2+ thd   skin   +thd   weber   (5)
 
         [0039]    The first predetermined threshold value thd 1  and the second predetermined threshold value thd 2  can be optimal values adjusted are according to the use of the lens and/or light sensing element. Next, a preposed de-noising coefficient C pre     —     k  is obtained according to the calculated motion strength Difference. Please note that for the current frame and the previous k (k=1˜n) frames, a number n of preposed de-noising coefficients C pre     —     k  (k=1˜n) should be obtained, respectively.  FIG. 5  is a relation diagram of the motion strength and the preposed de-noising coefficient of the present invention. 
         [0040]    In the step  308 , a distance between the pixel in the current frame and the center point of the frame is calculated (i.e. Distance Condition). The purpose of the step  308  is to adjust the coefficient obtained in the step  306  according to the distance between the pixel in the current frame and the center point of the frame. In general, if the pixel is farther from the center point of the frame, the pixel will be affected by the lens shading more seriously, and thus a bigger gain is required to amplify the pixel value, which results in the pixel farther from the center point of the frame has more serious noises than the center point of the frame. Thus, the pixel farther from the center point of the frame needs stronger filtering to improve the above noises. Since the pixel farther from the center point of the frame does not belong to the images of attention due to its position, the caused side effect of the ghosting effect is less easy to be detected. When the pixel is closer to the center point of the frame, the filtering strength is weaker. In this way, in the step  308 , the corresponding adjusting coefficient R is obtained according to the information of the distance from the center point of the frame, to adjust the preposed de-noising coefficients C pre     —     k  (k=1˜n) calculated in the step  306 .  FIG. 6  is a relation diagram of the distance from the center point of the frame and the adjusting coefficient in accordance with an embodiment of the present invention, wherein the distance is calculated by two norm, that is, the distance from the center point of the frame is calculated by using the Pythagorean theorem. 
         [0000]      Distance=√{square root over (( P   x   −C   x ) 2 −( P   y   −C   y ) 2 )}{square root over (( P   x   −C   x ) 2 −( P   y   −C   y ) 2 )}  (6)
 
         [0000]    P x  is X coordinate of the current pixel, and P y  is Y coordinate of the current pixel, and C x  is X coordinate of the current pixel, and C y  is Y coordinate of the current pixel. As shown in  FIG. 6 , if the calculated distance Distance is shorter than a first predetermined distance r, then the adjusting coefficient R will be set to a minimum adjusting coefficient R min . If the calculated distance Distance is longer than a second predetermined distance r+k, then the adjusting coefficient R will be set to a maximum adjusting coefficient R max . If the distance is between r and r+k, then the adjusting coefficient R can be obtained by the using linear interpolation. After the adjusting coefficient R is obtained, the individual de-noising coefficient C k  can be obtained by adjusting the preposed de-noising coefficients C pre     —     k  calculated in the step  306  according to the following equation (7). 
         [0000]        C   k   =C   pre     —     k   *R   (7)
 
         [0041]    However, the lens shading compensation method utilized by the present invention is not limited to the embodiment in  FIG. 6 . For example,  FIG. 7  is a relation diagram of the distance from the center point of the frame and the adjusting coefficient in accordance with another embodiment of the present invention, wherein the distance is calculated by one norm, that is, the distance from the center point of the frame is calculated by using the quadrilateral way. In any case, various modifications and alterations of the compensation method should fall into the disclosed scope of the present invention as long as they are based on the lens shading compensation. 
         [0042]    In the step  310 , the individual de-noising coefficients C k  (k=1˜n) are put in the equation (2) to obtain the result P out . Please refer to the above paragraphs for the details. 
         [0043]    Please refer to  FIG. 8 .  FIG. 8  shows a flowchart of an exemplary real-time adaptability 3D dynamic de-noising method in accordance with a second embodiment of the present invention. The flowchart comprises all the steps in the flowchart of the real-time adaptability 3D dynamic de-noising method in  FIG. 3 , but the order is changed. Specifically, the difference of the flowchart in  FIG. 8  and  FIG. 3  is that the distance is calculated before the Weber-Fechner Law and the motion estimation. Therefore, the equation (4) and the equation (5) are changed to be the following equation (8) and equation (9). 
         [0000]        thd   dynamic1   =thd 1+ thd   skin   +thd   dist   +thd   weber   (8)
 
         [0000]        thd   dynamic2   =thd 2+ thd   skin   +thd   dist   +thd   weber   (9)
 
         [0044]    A distance threshold value thd dist  calculated in the step  804  is increased. Thus, provided that substantially the same result is achieved, the steps of the real-time adaptability 3D dynamic de-noising method flowchart do not have to be in the specific order, and these are all fall within the scope of the present invention. 
         [0045]    In general, in a low brightness environment, the received the pixels will be multiplied by a bigger gain before processed by the real-time adaptability 3D dynamic de-noising method of the present invention, and thus the noises will be amplified synchronously and particularly apparent. Thus, the strength of noise filtering has to be relatively increased in this condition. On the contrary, if the environmental brightness is enough, the noise is not obvious, so in this case the strength of the noise filtering should be relatively reduced, otherwise it may affect the image clarity or cause other side effects. The present invention can make optimization of adjustment according to the ambient light and brightness. In another embodiment, the steps  802 ,  804 , and  806  in  FIG. 8  can be omitted, as shown in  FIG. 10 . 
         [0046]    Please refer to  FIG. 9 .  FIG. 9  is a block diagram of an image system  900  in accordance with an embodiment of the present invention. The image system  900  comprises: a lens  902 , a sensor  904 , an image and signal processor (ISP)  906 , a de-noising unit  908 , and a brightness adjusting unit  910 . For example, the lens  902  and the sensor  904  can be a part or all of a lens module. After the light enters into the sensor  904  via the lens  902 , the sensor  904  will convert the captured image to an image signal I bayer  of a specific image format, wherein the specific image format is a Bayer pattern in this embodiment, but this is not a limitation of the present invention. Next, the image signal I bayer  is transmitted to the ISP  906 , and the ISP  906  converts the image signal I bayer  to an image signal P in  of another specific image format by some image processing procedures, wherein the specific image format is a YUV signal format in this embodiment, but this is not a limitation of the present invention. Meanwhile, the ISP  906  will also further generate an automatic exposure information C ae  to the brightness adjusting unit  910 . The brightness adjusting unit  910  can perform related automatic exposure algorithm according to the automatic exposure information C ae , and generate a frame rate information C fps  to the de-noising unit  908 , and further generate a gain control signal C gain  and a exposure control signal C exp  to the sensor  904 . Next, the de-noising unit  908  will perform the de-noising algorithm according to the received image signal P in  and the frame rate information C fps , so as to generate an image output signal P new     —     out . In general, the brightness adjusting unit  910  can be realized by firmware, and the de-noising unit  908  can be realized by software, such as a software driver, but this is not a limitation of the present invention. 
         [0047]    For the de-noising unit  908 , in order to obtain the environment light source and the environment brightness to achieve the optimal de-noising effect, the frame rate information C fps  can be utilized to derive the environment light source and the environment brightness. Specifically, when the environment brightness is brighter, the frame rate information C fps  will be higher. When the environment brightness is darker, the brightness adjusting unit  910  will actively increase the exposure time of the sensor  904  to lower the frame rate information C fps . In other words, when the environment brightness is brighter, the frame rate information C fps  is higher than that when the environment brightness is darker. 
         [0048]    The de-noising noise unit  908  can only use the real-time adaptability 3D dynamic de-noising method in  FIG. 3  or  FIG. 4  without using the frame rate information C fps  as one of the factors, and directly use the generated real-time adaptability 3D dynamic de-noising image output P out  as an output P new     —     out  of the de-noising noise unit  908 . Besides, the de-noising noise unit  908  also can use the real-time adaptability 3D dynamic de-noising method in  FIG. 3  or  FIG. 4  to calculate the de-noising image output P out  and obtain an optimal output P new     —     out  according to the frame rate information C fps . 
         [0000]        P   new     —     out   =P   in   ×α+P   out ×(1−α)  (8)
 
         [0000]    α is between 0 and 1, and used to determine the strength of the de-noising effect. The calculation of α is as follows: 
         [0000]      α=ƒ 4 ( C   fps )  (9)
 
         [0049]    ƒ 4  is a monotone increasing function. When the frame rate information C fps  is higher, a is bigger, and the optimal output P new     —     out  is closer to P in . That is, when the environment light source is brighter, the de-noising effect will be lower, and when the environment light source is darker, the de-noising effect will be higher. In this embodiment, the environment light source is obtained by the frame rate information C fps , but this is not a limitation of the present invention. In addition, the de-noising noise unit  908  can also use other de-noising methods with the equation (8) and the equation (9) to obtain dynamic results considering the environment light source. Above all fall within the scope of the present invention. 
         [0050]    Those skilled in the art will readily observe that numerous modifications and alterations of the device and method may be made while retaining the teachings of the invention. Accordingly, the above disclosure should be construed as limited only by the metes and bounds of the appended claims.