CN111246052B - Wide dynamic adjustment method and device, storage medium and electronic device - Google Patents
Wide dynamic adjustment method and device, storage medium and electronic device Download PDFInfo
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Abstract
The embodiment of the application provides a method and a device for wide dynamic adjustment, a storage medium and an electronic device, wherein the method comprises the following steps: acquiring a plurality of exposure images in the same scene at the same time; respectively carrying out noise reduction processing on a plurality of exposure images by using first parameters, wherein each exposure image corresponds to the value of one first parameter, the value of the first parameter is related to a second parameter corresponding to the previous frame of original data corresponding to the exposure image in the wide dynamic fusion process, and the value of the second parameter has a mapping relation with the brightness interval distribution of the previous frame of original data corresponding to the exposure image; the method comprises the steps of obtaining values of second parameters corresponding to original data of a current frame corresponding to a plurality of exposure images in a wide dynamic fusion process, and fusing the plurality of exposure images to obtain a wide dynamic fusion image, so that the problems that in the prior art, wide dynamic adjustment and image denoising operations are independent from each other and only aim at single image operation, the wide dynamic range is small, and the denoising effect is not ideal are solved.
Description
Technical Field
The present application relates to the field of image processing technologies, and in particular, to a method and an apparatus for wide dynamic adjustment, a storage medium, and an electronic apparatus.
Background
The wide dynamic noise reduction is mainly used in the technical field of videos and images, and is used for optimizing the noise reduction effect of video images and improving the human eye impression of the video images. In the prior art, when wide dynamic adjustment is needed for identification, noise reduction is firstly carried out, and then dynamic range adjustment is carried out. The two operations are independent, and only the noise reduction and wide dynamic adjustment are carried out on a single picture, so that the wide dynamic range is small and the noise reduction effect is not ideal.
Aiming at the problems that in the related art, wide dynamic adjustment and image noise reduction operation are independent and only single image operation is required, so that the wide dynamic range is small and the noise reduction effect is not ideal, an effective solution is not available at present.
Disclosure of Invention
The embodiment of the application provides a wide dynamic adjustment method and device, a storage medium and an electronic device, which are used for at least solving the problems that in the related art, wide dynamic adjustment and image noise reduction operation are independent from each other and only aim at single image operation, so that the wide dynamic range is small and the noise reduction effect is not ideal.
According to an embodiment of the present application, there is provided a wide dynamic adjustment method including: acquiring a plurality of exposure images under the same scene at the same time; respectively performing noise reduction processing on the multiple exposure images by using first parameters, wherein each exposure image corresponds to a value of one first parameter, the value of the first parameter is related to a second parameter corresponding to a previous frame of original data corresponding to the exposure image in a wide dynamic fusion process, and the value of the second parameter has a mapping relation with the brightness interval distribution of the previous frame of original data corresponding to the exposure image; and obtaining values of the second parameters respectively corresponding to the current frame original data corresponding to the multiple exposure images in the wide dynamic fusion process, and fusing the multiple exposure images to obtain a wide dynamic fusion image.
Optionally, the performing, by using the first fusion weight parameter, noise reduction processing on the multiple exposure images respectively includes: acquiring an interframe change value of original data corresponding to the target exposure image, wherein the interframe change value represents a difference value between current frame original data and previous frame original data corresponding to the target exposure image; adjusting the interframe change value according to the brightness information of the current frame original data corresponding to the target exposure image; determining a value of a target first parameter corresponding to the target exposure image according to the adjusted mapping relation between the interframe change value and the first parameter; adjusting the first target parameter by using a second target parameter corresponding to the previous frame of original data corresponding to the target exposure image in the wide dynamic fusion process; and fusing the current frame original data and the previous frame original data corresponding to the target exposure image by using the adjusted first target parameter to obtain fused data corresponding to the target exposure image after noise reduction processing.
Optionally, the obtaining of the interframe variation value of the raw data corresponding to the target exposure image includes: dividing a traversal window of n x m from the current frame of original data and the previous frame of original data corresponding to the target exposure image, wherein n and m are positive integers, and dividing the traversal window according to r, g and b color components; calculating a difference value between the current frame original data and the previous frame original data according to the traversal window, and then dividing the difference value by a larger value between the current frame original data and the previous frame original data to obtain a first result; accumulating the first result according to r, g and b color components respectively and then calculating an average value to obtain an r average value, a g average value and a b average value; and adding the r mean value, the g mean value and the b mean value, and then calculating the mean value to obtain the interframe change value of the original data corresponding to the target exposure image.
Optionally, the adjusting the interframe variation value according to the brightness information of the current frame raw data corresponding to the target exposure image includes: obtaining a numerical value corresponding to a first adjustment coefficient according to a mapping relation between a brightness value of current frame original data corresponding to the target exposure image and the first adjustment coefficient; and multiplying the interframe change value by the first adjusting coefficient to obtain the adjusted interframe change value.
Optionally, adjusting the target first parameter by using a target second parameter corresponding to the previous frame of original data corresponding to the target exposure image in a wide dynamic fusion process, including: obtaining a value of a second target parameter of the previous frame of original data corresponding to the target exposure image in the wide dynamic fusion process; performing histogram statistics on pixel points of the previous frame of original data, of which the value of the target second parameter is not 0, and obtaining integral brightness interval distribution of the previous frame of original data according to the number of pixel points of different brightness intervals in the previous frame of original data; obtaining a value of a second adjustment coefficient according to the mapping relation between the brightness interval distribution of the whole original data of the previous frame and the second adjustment coefficient; and multiplying the target first parameter by the second adjusting coefficient to obtain the adjusted target first parameter.
Optionally, fusing current frame raw data and previous frame raw data corresponding to the target exposure image by using the adjusted first target parameter to obtain fused data corresponding to the target exposure image after the noise reduction processing, including: multiplying the adjusted target first parameter with the original data of the previous frame to obtain fusion data of the previous frame; multiplying a third parameter by the current frame original data to obtain current frame fusion data, wherein the sum of the third parameter and the target first parameter is 1; and adding the previous frame fusion data and the current frame fusion data to obtain fusion data corresponding to the target exposure image after noise reduction processing.
Optionally, after obtaining values of the second parameters respectively corresponding to the current frame raw data corresponding to the multiple exposure images in the wide dynamic fusion process, the method further includes: performing histogram statistics on pixel points, in which the value of the second parameter is not 0, in the current frame original data corresponding to the first exposure image in the multiple exposure images according to the value of the second parameter corresponding to the current frame original data corresponding to the first exposure image in the wide dynamic fusion process; and adjusting the value of the first parameter used by the next frame of original data corresponding to the first exposure image in the noise reduction process according to the result of the histogram statistics.
According to another embodiment of the present application, there is also provided a wide dynamic adjustment apparatus including:
the acquisition module is used for acquiring a plurality of exposure images in the same scene at the same time;
the noise reduction module is used for respectively performing noise reduction processing on the multiple exposure images by using first parameters, wherein each exposure image corresponds to a value of one first parameter, the value of the first parameter is related to a second parameter corresponding to a previous frame of original data corresponding to the exposure image in a wide dynamic fusion process, and the value of the second parameter has a mapping relation with the brightness interval distribution of the previous frame of original data corresponding to the exposure image;
and the fusion module is used for acquiring values of the second parameters respectively corresponding to the current frame original data corresponding to the multiple exposure images in the wide dynamic fusion process, and fusing the multiple exposure images to obtain a wide dynamic fusion image.
Optionally, the noise reduction module comprises:
an obtaining unit, configured to obtain an interframe change value of original data corresponding to the target exposure image, where the interframe change value represents a difference value between current frame original data and previous frame original data corresponding to the target exposure image;
the first adjusting unit is used for adjusting the interframe change value according to the brightness information of the current frame original data corresponding to the target exposure image;
the determining unit is used for determining the value of a target first parameter corresponding to the target exposure image according to the mapping relation between the adjusted interframe change value and the first parameter;
the second adjusting unit is used for adjusting the target first parameter by using a target second parameter corresponding to the previous frame of original data corresponding to the target exposure image in the wide dynamic fusion process;
and the fusion unit is used for fusing the current frame original data and the previous frame original data corresponding to the target exposure image by using the adjusted target first parameter to obtain fusion data corresponding to the target exposure image after noise reduction processing.
Optionally, the obtaining unit includes:
a dividing subunit, configured to divide a traversal window of n × m from current frame original data and previous frame original data corresponding to the target exposure image, where n and m are positive integers, and the traversal window is divided according to r, g, and b color components;
a first calculating subunit, configured to calculate a difference between the current frame original data and the previous frame original data according to the traversal window, and then divide the difference by a larger value between the current frame original data and the previous frame original data to obtain a first result;
the second calculating subunit is used for accumulating the first result according to the r, g and b color components respectively and then calculating an average value to obtain an r average value, a g average value and a b average value;
and the third calculation subunit is used for adding the r mean value, the g mean value and the b mean value and then calculating a mean value to obtain an interframe change value of the original data corresponding to the target exposure image.
Optionally, the first adjusting unit includes:
the adjusting subunit is configured to obtain a value corresponding to a first adjusting coefficient according to a mapping relationship between a brightness value of current frame original data corresponding to the target exposure image and the first adjusting coefficient;
and the fourth calculating subunit is configured to multiply the interframe change value by the first adjustment coefficient to obtain the adjusted interframe change value.
Optionally, the second adjusting unit includes:
the acquisition subunit is used for acquiring the value of a second target parameter of the previous frame of original data corresponding to the target exposure image in the wide dynamic fusion process;
a counting subunit, configured to perform histogram statistics on pixel points of the previous frame of original data where the value of the target second parameter is not 0, and obtain, according to the number of pixel points in different luminance intervals in the previous frame of original data, luminance interval distribution of the previous frame of original data as a whole;
the mapping subunit is configured to obtain a value of a second adjustment coefficient according to a mapping relationship between the brightness interval distribution of the entire previous frame of original data and the second adjustment coefficient;
and the fifth calculating subunit is configured to multiply the target first parameter by the second adjustment coefficient to obtain the adjusted target first parameter.
Optionally, the fusion unit comprises:
a sixth calculating subunit, configured to multiply the adjusted target first parameter with the previous frame original data to obtain previous frame fusion data;
a seventh calculating subunit, configured to multiply the current frame original data by a third parameter to obtain current frame fusion data, where a sum of the third parameter and the target first parameter is 1;
and the eighth calculating subunit is configured to add the previous frame fusion data and the current frame fusion data to obtain fusion data corresponding to the target exposure image after the noise reduction processing.
Optionally, the apparatus further comprises:
the statistical module is used for carrying out histogram statistics on pixel points, of which the value of the second parameter is not 0, in the current frame original data corresponding to the first exposure image in the multiple exposure images according to the value of the second parameter corresponding to the current frame original data corresponding to the first exposure image in the wide dynamic fusion process;
and the adjusting module is used for adjusting the value of the first parameter used by the next frame of original data corresponding to the first exposure image in the noise reduction processing process according to the result of the histogram statistics.
According to another embodiment of the application, there is also provided a computer-readable storage medium, in which a computer program is stored, wherein the computer program is arranged to perform the steps of any of the above-mentioned method embodiments when executed.
According to another embodiment of the present application, there is also provided an electronic device comprising a memory and a processor, wherein the memory stores a computer program, and the processor is configured to execute the computer program to perform the steps of any of the above method embodiments.
According to the embodiment of the application, a plurality of exposure images in the same scene at the same time are obtained; respectively carrying out noise reduction processing on a plurality of exposure images by using first parameters, wherein each exposure image corresponds to the value of one first parameter, the value of the first parameter is related to a second parameter corresponding to the previous frame of original data corresponding to the exposure image in the wide dynamic fusion process, and the value of the second parameter has a mapping relation with the brightness interval distribution of the previous frame of original data corresponding to the exposure image; the method comprises the steps of obtaining values of second parameters respectively corresponding to current frame original data corresponding to a plurality of exposure images in a wide dynamic fusion process, fusing the plurality of exposure images to obtain a wide dynamic fusion image, solving the problems that in the prior art, wide dynamic adjustment and image noise reduction operation are independent and only aim at single image operation, so that the wide dynamic range is small and the noise reduction effect is not ideal, adopting non-uniform self-adaptive noise reduction processing, namely determining noise reduction and fusion parameters according to the brightness interval distribution of the exposure images, obtaining effective statistical information of different exposure images by combining with wide dynamic fusion weight, optimizing noise reduction parameters, and greatly improving the noise reduction quality of the wide dynamic image.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
FIG. 1 is a schematic diagram of a hardware environment for an alternative wide dynamic adjustment method according to an embodiment of the present application;
FIG. 2 is a flow chart of an alternative wide dynamic adjustment method according to an embodiment of the present application;
FIG. 3 is a general flow chart of an alternative wide dynamic adjustment method according to an embodiment of the application;
FIG. 4 is a flow chart of an alternative image denoising method according to an embodiment of the present application;
FIG. 5 is a flowchart of an alternative method for calculating interframe variation values according to the embodiment of the present application;
FIG. 6 is a graph illustrating an alternative mapping between an inter-frame variation adjustment coefficient and luminance information according to an embodiment of the present application;
FIG. 7 is a graph illustrating an alternative mapping relationship between denoising fusion weights and interframe variation values according to an embodiment of the application;
FIG. 8 is a block diagram of an alternative wide dynamic adjustment apparatus according to an embodiment of the present application;
fig. 9 is a schematic structural diagram of an alternative electronic device according to an embodiment of the present application.
Detailed Description
The present application will be described in detail below with reference to the accompanying drawings in conjunction with embodiments. It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict.
It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order.
The embodiment of the application provides a wide dynamic adjustment method. Fig. 1 is a schematic diagram of a hardware environment of an alternative wide dynamic adjustment method according to an embodiment of the present invention, as shown in fig. 1, the hardware environment may include, but is not limited to, a video image capture device 102 and a server 104, and the video image capture device 102 may be a high definition camera. The video image acquisition device 102 sends the acquired video image data to the server 104, and the server 104 performs internal processing to perform wide dynamic adjustment on the received image data, including noise reduction processing and wide dynamic fusion, wherein the operations executed in the server 104 mainly include the following steps:
step S102, acquiring a plurality of exposure images in the same scene at the same time;
step S104, noise reduction processing is respectively carried out on a plurality of exposure images by using first parameters, wherein each exposure image corresponds to the value of one first parameter, the value of the first parameter is related to a second parameter corresponding to the previous frame of original data corresponding to the exposure image in the wide dynamic fusion process, and the value of the second parameter has a mapping relation with the brightness interval distribution of the previous frame of original data corresponding to the exposure image;
and S106, obtaining values of second parameters respectively corresponding to the current frame original data corresponding to the multiple exposure images in the wide dynamic fusion process, and fusing the multiple exposure images to obtain a wide dynamic fusion image.
The embodiment of the application provides a wide dynamic adjustment method. Fig. 2 is a flowchart of an optional wide dynamic adjustment method according to an embodiment of the present application, and as shown in fig. 2, the method includes:
step S202, acquiring a plurality of exposure images in the same scene at the same time;
step S204, noise reduction processing is respectively carried out on a plurality of exposure images by using first parameters, wherein each exposure image corresponds to the value of one first parameter, the value of the first parameter is related to a second parameter corresponding to the previous frame of original data corresponding to the exposure image in the wide dynamic fusion process, and the value of the second parameter has a mapping relation with the brightness interval distribution of the previous frame of original data corresponding to the exposure image;
step S206, obtaining values of second parameters respectively corresponding to the current frame original data corresponding to the multiple exposure images in the wide dynamic fusion process, and fusing the multiple exposure images to obtain a wide dynamic fusion image.
By the method, a plurality of exposure images in the same scene at the same time are obtained; respectively carrying out noise reduction processing on a plurality of exposure images by using first parameters, wherein each exposure image corresponds to the value of one first parameter, the value of the first parameter is related to a second parameter corresponding to the previous frame of original data corresponding to the exposure image in the wide dynamic fusion process, and the value of the second parameter has a mapping relation with the brightness interval distribution of the previous frame of original data corresponding to the exposure image; the method comprises the steps of obtaining values of second parameters respectively corresponding to current frame original data corresponding to a plurality of exposure images in a wide dynamic fusion process, fusing the plurality of exposure images to obtain a wide dynamic fusion image, solving the problems that in the prior art, wide dynamic adjustment and image noise reduction operation are independent and only aim at single image operation, so that the wide dynamic range is small and the noise reduction effect is not ideal, adopting non-uniform self-adaptive noise reduction processing, namely determining noise reduction and fusion parameters according to the brightness interval distribution of the exposure images, obtaining effective statistical information of different exposure images by combining with wide dynamic fusion weight, optimizing noise reduction parameters, and greatly improving the noise reduction quality of the wide dynamic image.
Fig. 3 is an overall flowchart of an alternative wide dynamic adjustment method according to an embodiment of the present application, and as shown in fig. 3, the method may include the following steps:
s1, acquiring different exposure images of the same scene according to the exposure mechanism of the sensor: exposure 0 image, exposure 1 image … … exposes n images. It should be noted that, the exposure images in the same scene may be acquired at the same time, and according to the line-wise exposure method, the exposure 0 image, the exposure 1 image … … expose the first line of the n image, and then the exposure 0 image, the exposure 1 image … … expose the second line of the n image until the whole image exposure is completed.
S2, noise reduction processing is performed on the exposure images of different exposures. This step may involve the use of a first parameter.
And S3, performing wide dynamic weight calculation on the different exposure images subjected to noise reduction, and fusing to obtain a wide dynamic image. The wide dynamic weight here may correspond to the aforementioned second parameter.
And S4, performing histogram statistics on the pixels with fusion weight values not being 0 in the original image through the wide dynamic weight values, and guiding the adjustment of the noise reduction parameter (namely the first parameter) of the next frame of image.
Optionally, the performing the noise reduction processing on the multiple exposure images by using the first fusion weight parameter respectively may include:
s1, acquiring an interframe change value of original data corresponding to the target exposure image, wherein the interframe change value represents a difference value between current frame original data and previous frame original data corresponding to the target exposure image;
s2, adjusting the interframe variation value according to the brightness information of the current frame original data corresponding to the target exposure image; determining a value of a target first parameter corresponding to the target exposure image according to the mapping relation between the adjusted interframe change value and the first parameter;
s3, adjusting the target first parameter by using the target second parameter corresponding to the previous frame original data corresponding to the target exposure image in the wide dynamic fusion process;
and S4, fusing the current frame original data and the previous frame original data corresponding to the target exposure image by using the adjusted first target parameter to obtain fused data corresponding to the target exposure image after noise reduction processing.
Fig. 4 is a flowchart of an alternative image denoising method according to an embodiment of the present application, as shown in fig. 4, the method includes:
SA1, calculating a frame change value cur _ pre _ var (frame-to-frame change value) before and after n × m window for the input bayer format raw data, where cur represents the current frame, and pre represents that the raw data of the previous frame is raw data, that is, the CMOS or CCD image sensor converts the captured light source signal into raw data of a digital signal. A specific calculation flow is shown in fig. 5, and fig. 5 is a flow chart of a method for calculating a selectable interframe variation value according to an embodiment of the present application, including the following steps:
s11, dividing a traversal window of n x m for the current frame original data and the previous frame original data corresponding to the target exposure image, wherein n and m are positive integers, and dividing the traversal window according to r, g and b color components;
s12, calculating the difference between the current frame original data and the previous frame original data according to the traversal window, and then dividing the difference by the larger value between the current frame original data and the previous frame original data to obtain a first result;
s13, accumulating the first results according to r, g and b color components respectively, and then calculating the average value to obtain an r average value, a g average value and a b average value;
and S14, adding the r mean value, the g mean value and the b mean value to obtain a mean value, and obtaining an interframe change value of the original data corresponding to the target exposure image.
Optionally, the adjusting the interframe variation value according to the brightness information of the current frame raw data corresponding to the target exposure image includes: obtaining a numerical value corresponding to a first adjustment coefficient according to a mapping relation between a brightness value of current frame original data corresponding to the target exposure image and the first adjustment coefficient; and multiplying the interframe change value by the first adjusting coefficient to obtain the adjusted interframe change value.
SA2 calculates the inter-frame variation value adjustment coefficient r (i.e., the first adjustment coefficient) from the luminance information obtained from the raw data of the current frame. Fig. 6 is a mapping graph of an optional inter-frame variation value adjustment coefficient and luminance information according to an embodiment of the present application, and as shown in fig. 6, the inter-frame variation value adjustment coefficient r may be determined according to the luminance information t obtained from the raw data of the current frame.
SA3, obtaining the regulating coefficient of the interframe change value through mapping, and regulating the interframe change value cur _ pre _ var; by configuring the parameters of the curve, different adjustment weights can be obtained by mapping according to different brightness. Wherein cur _ pre _ var is cur _ pre _ var.
SA4 maps the adjusted inter-frame variation value to obtain a noise reduction fusion weight _ pre (i.e. the aforementioned first parameter), where a fusion curve is as shown in fig. 7, and fig. 7 is a graph of a mapping relationship between an optional noise reduction fusion weight and the inter-frame variation value according to the embodiment of the present application.
Optionally, adjusting the target first parameter by using a target second parameter corresponding to the previous frame of original data corresponding to the target exposure image in a wide dynamic fusion process, including: obtaining a value of a second target parameter of the previous frame of original data corresponding to the target exposure image in the wide dynamic fusion process; performing histogram statistics on pixel points of the previous frame of original data, of which the value of the target second parameter is not 0, and obtaining integral brightness interval distribution of the previous frame of original data according to the number of pixel points of different brightness intervals in the previous frame of original data; obtaining a value of a second adjustment coefficient according to the mapping relation between the brightness interval distribution of the whole original data of the previous frame and the second adjustment coefficient; and multiplying the target first parameter by the second adjusting coefficient to obtain the adjusted target first parameter.
SA5, acquiring the wide dynamic fusion weight (i.e. the second parameter) of the multi-frame image in the wide dynamic fusion process, and then performing multi-frame brightness histogram statistics on the exposure source image pixel points with the fusion weight not being 0;
SA6, obtaining a noise reduction fusion weight adjustment coefficient new3d _ alpha (namely the second adjustment coefficient) through the statistical information (of the previous frame) of the wide dynamic histogram, adjusting the noise reduction fusion weight (the first parameter), obtaining the brightness interval distribution of the whole image through counting the number of image pixel points in different brightness intervals by the histogram, and mapping to obtain a corresponding image global adjustment coefficient new3d _ alpha. In this mapping relation, generally, the larger the overall luminance interval is, the smaller new3d _ alpha is.
new3d _ beta (the first parameter after adjustment) ═ weight _ pre × new3d _ alpha.
SA7, fusing the front and rear frame data to obtain the noise reduction result of each exposure image:
data _ tmp (noise reduction result) ═ pre _ data _ in (previous frame data) × new3d _ beta + cur _ data _ in (current frame data) × (1-new3d _ beta).
Optionally, fusing current frame original data and previous frame original data corresponding to the target exposure image by using the adjusted target first parameter to obtain fused data corresponding to the target exposure image after the noise reduction processing, including: multiplying the adjusted target first parameter with the original data of the previous frame to obtain fusion data of the previous frame; multiplying a third parameter by the current frame original data to obtain current frame fusion data, wherein the sum of the third parameter and the target first parameter is 1; and adding the previous frame fusion data and the current frame fusion data to obtain fusion data corresponding to the target exposure image after noise reduction processing.
Optionally, after obtaining values of the second parameters respectively corresponding to the current frame raw data corresponding to the multiple exposure images in the wide dynamic fusion process, the method further includes: performing histogram statistics on pixel points, in which the value of the second parameter is not 0, in the current frame original data corresponding to the first exposure image in the multiple exposure images according to the value of the second parameter corresponding to the current frame original data corresponding to the first exposure image in the wide dynamic fusion process; and adjusting the value of the first parameter used by the next frame of original data corresponding to the first exposure image in the noise reduction process according to the result of the histogram statistics.
It should be noted that the first parameter adjusted by the second adjustment coefficient each time can be used as a noise reduction parameter of the next frame image.
According to the embodiment of the application, non-uniform self-adaptive noise reduction processing is adopted, effective statistical information of different exposure images is obtained by combining with wide dynamic fusion weight, noise reduction parameters are optimized, and noise reduction quality and self-adaptability of wide dynamic images are greatly improved. Compared with the linear noise reduction characteristic, the embodiment of the application performs noise reduction processing with different intensities on different levels of brightness in the wide dynamic image, and is more in line with the noise characteristic of the nonlinear image.
It should be noted that, for simplicity of description, the above-mentioned method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the present application is not limited by the order of acts described, as some steps may occur in other orders or concurrently depending on the application. Further, those skilled in the art should also appreciate that the embodiments described in the specification are preferred embodiments and that the acts and modules referred to are not necessarily required in this application.
Through the above description of the embodiments, those skilled in the art can clearly understand that the method according to the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but the former is a better implementation mode in many cases. Based on such understanding, the technical solutions of the present application or portions thereof that contribute to the prior art may be embodied in the form of a software product, where the computer software product is stored in a storage medium (such as a ROM/RAM, a magnetic disk, and an optical disk), and includes several instructions for enabling a terminal device (which may be a mobile phone, a computer, a server, or a network device) to execute the method described in the embodiments of the present application.
According to another aspect of the embodiments of the present application, there is also provided a wide dynamic adjustment apparatus for implementing the above-described wide dynamic adjustment method. Fig. 8 is a block diagram of an alternative wide dynamic adjustment apparatus according to an embodiment of the present application, as shown in fig. 8, the apparatus includes:
an obtaining module 802, configured to obtain multiple exposure images in the same scene at the same time;
a denoising module 804, configured to perform denoising on the multiple exposure images respectively by using first parameters, where each exposure image corresponds to a value of one first parameter, the value of the first parameter is related to a second parameter corresponding to a previous frame of original data corresponding to the exposure image in a wide dynamic fusion process, and the value of the second parameter has a mapping relationship with luminance interval distribution of the previous frame of original data corresponding to the exposure image;
the fusion module 806 is configured to obtain values of the second parameters respectively corresponding to the current frame raw data corresponding to the multiple exposure images in a wide dynamic fusion process, and fuse the multiple exposure images to obtain a wide dynamic fusion image.
Optionally, the noise reduction module 804 includes:
an obtaining unit, configured to obtain an interframe change value of original data corresponding to the target exposure image, where the interframe change value represents a difference value between current frame original data and previous frame original data corresponding to the target exposure image;
the first adjusting unit is used for adjusting the interframe change value according to the brightness information of the current frame original data corresponding to the target exposure image;
the determining unit is used for determining the value of a target first parameter corresponding to the target exposure image according to the mapping relation between the adjusted interframe change value and the first parameter;
the second adjusting unit is used for adjusting the target first parameter by using a target second parameter corresponding to the previous frame of original data corresponding to the target exposure image in the wide dynamic fusion process;
and the fusion unit is used for fusing the current frame original data and the previous frame original data corresponding to the target exposure image by using the adjusted target first parameter to obtain fusion data corresponding to the target exposure image after noise reduction processing.
Optionally, the obtaining unit includes:
a dividing subunit, configured to divide a traversal window of n × m from current frame original data and previous frame original data corresponding to the target exposure image, where n and m are positive integers, and the traversal window is divided according to r, g, and b color components;
a first calculating subunit, configured to calculate a difference between the current frame original data and the previous frame original data according to the traversal window, and then divide the difference by a larger value between the current frame original data and the previous frame original data to obtain a first result;
the second calculating subunit is used for accumulating the first result according to the r, g and b color components respectively and then calculating an average value to obtain an r average value, a g average value and a b average value;
and the third calculation subunit is used for adding the r mean value, the g mean value and the b mean value and then calculating a mean value to obtain an interframe change value of the original data corresponding to the target exposure image.
Optionally, the first adjusting unit includes:
the adjusting subunit is configured to obtain a value corresponding to a first adjusting coefficient according to a mapping relationship between a brightness value of current frame original data corresponding to the target exposure image and the first adjusting coefficient;
and the fourth calculating subunit is configured to multiply the interframe change value by the first adjustment coefficient to obtain the adjusted interframe change value.
Optionally, the second adjusting unit includes:
the acquisition subunit is used for acquiring the value of a second target parameter of the previous frame of original data corresponding to the target exposure image in the wide dynamic fusion process;
a counting subunit, configured to perform histogram statistics on pixel points of the previous frame of original data where the value of the target second parameter is not 0, and obtain, according to the number of pixel points in different luminance intervals in the previous frame of original data, luminance interval distribution of the previous frame of original data as a whole;
the mapping subunit is configured to obtain a value of a second adjustment coefficient according to a mapping relationship between the brightness interval distribution of the entire previous frame of original data and the second adjustment coefficient;
and the fifth calculating subunit is configured to multiply the target first parameter by the second adjustment coefficient to obtain the adjusted target first parameter.
Optionally, the fusion unit comprises:
a sixth calculating subunit, configured to multiply the adjusted target first parameter with the previous frame original data to obtain previous frame fusion data;
a seventh calculating subunit, configured to multiply the current frame original data by a third parameter to obtain current frame fusion data, where a sum of the third parameter and the target first parameter is 1;
and the eighth calculating subunit is configured to add the previous frame fusion data and the current frame fusion data to obtain fusion data corresponding to the target exposure image after the noise reduction processing.
Optionally, the apparatus further comprises:
the statistical module is used for carrying out histogram statistics on pixel points, of which the value of the second parameter is not 0, in the current frame original data corresponding to the first exposure image in the multiple exposure images according to the value of the second parameter corresponding to the current frame original data corresponding to the first exposure image in the wide dynamic fusion process;
and the adjusting module is used for adjusting the value of the first parameter used by the next frame of original data corresponding to the first exposure image in the noise reduction processing process according to the result of the histogram statistics.
According to another aspect of the embodiment of the present application, there is also provided an electronic apparatus for implementing the above-mentioned wide dynamic adjustment method, which may be, but is not limited to be, applied in the server 104 shown in fig. 1. As shown in fig. 9, the electronic device comprises a memory 402 and a processor 404, wherein the memory 402 has a computer program stored therein, and the processor 404 is configured to execute the steps of any of the above method embodiments by the computer program.
Optionally, in this embodiment, the electronic apparatus may be located in at least one network device of a plurality of network devices of a computer network.
Optionally, in this embodiment, the processor may be configured to execute the following steps by a computer program:
s1, acquiring a plurality of exposure images in the same scene at the same time;
s2, performing noise reduction processing on a plurality of exposure images respectively by using first parameters, wherein each exposure image corresponds to a value of one first parameter, the value of the first parameter is related to a second parameter corresponding to the previous frame of original data corresponding to the exposure image in the wide dynamic fusion process, and the value of the second parameter has a mapping relation with the brightness interval distribution of the previous frame of original data corresponding to the exposure image;
and S3, obtaining values of second parameters respectively corresponding to the current frame original data corresponding to the multiple exposure images in the wide dynamic fusion process, and fusing the multiple exposure images to obtain a wide dynamic fusion image.
Alternatively, it can be understood by those skilled in the art that the structure shown in fig. 9 is only an illustration, and the electronic device may also be a terminal device such as a smart phone (e.g., an Android phone, an iOS phone, etc.), a tablet computer, a palm computer, a Mobile Internet Device (MID), a PAD, and the like. Fig. 9 is a diagram illustrating a structure of the electronic device. For example, the electronic device may also include more or fewer components (e.g., network interfaces, etc.) than shown in FIG. 9, or have a different configuration than shown in FIG. 9.
The memory 402 may be used to store software programs and modules, such as program instructions/modules corresponding to the method and apparatus for wide dynamic adjustment in the embodiment of the present application, and the processor 404 executes various functional applications and data processing by running the software programs and modules stored in the memory 402, that is, implementing the above-mentioned method for wide dynamic adjustment. The memory 402 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 402 may further include memory located remotely from the processor 404, which may be connected to the terminal over a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof. The memory 402 may be, but not limited to, specifically configured to store program steps of the wide dynamic adjustment method. As an example, as shown in fig. 9, the memory 402 may include, but is not limited to, an obtaining module 802, a denoising module 804, a fusion module 806, and the like in the wide dynamic adjustment apparatus. In addition, other module units in the wide dynamic adjustment apparatus may also be included, but are not limited to, and are not described in detail in this example.
Optionally, the transmission device 406 is used for receiving or sending data via a network. Examples of the network may include a wired network and a wireless network. In one example, the transmission device 406 includes a Network adapter (NIC) that can be connected to a router via a Network cable and other Network devices to communicate with the internet or a local area Network. In one example, the transmission device 406 is a Radio Frequency (RF) module, which is used to communicate with the internet in a wireless manner.
In addition, the electronic device further includes: the display 408 is used for displaying the alarm push of the suspicious account; and a connection bus 410 for connecting the respective module parts in the above-described electronic apparatus.
Embodiments of the present application further provide a computer-readable storage medium having a computer program stored therein, wherein the computer program is configured to perform the steps of any of the above method embodiments when executed.
Alternatively, in the present embodiment, the storage medium may be configured to store a computer program for executing the steps of:
s1, acquiring a plurality of exposure images in the same scene at the same time;
s2, performing noise reduction processing on a plurality of exposure images respectively by using first parameters, wherein each exposure image corresponds to a value of one first parameter, the value of the first parameter is related to a second parameter corresponding to the previous frame of original data corresponding to the exposure image in the wide dynamic fusion process, and the value of the second parameter has a mapping relation with the brightness interval distribution of the previous frame of original data corresponding to the exposure image;
and S3, obtaining values of second parameters respectively corresponding to the current frame original data corresponding to the multiple exposure images in the wide dynamic fusion process, and fusing the multiple exposure images to obtain a wide dynamic fusion image.
Optionally, the storage medium is further configured to store a computer program for executing the steps included in the method in the foregoing embodiment, which is not described in detail in this embodiment.
Alternatively, in this embodiment, a person skilled in the art may understand that all or part of the steps in the methods of the foregoing embodiments may be implemented by a program instructing hardware associated with the terminal device, where the program may be stored in a computer-readable storage medium, and the storage medium may include: flash disks, Read-Only memories (ROMs), Random Access Memories (RAMs), magnetic or optical disks, and the like.
The above-mentioned serial numbers of the embodiments of the present application are merely for description and do not represent the merits of the embodiments.
The integrated unit in the above embodiments, if implemented in the form of a software functional unit and sold or used as a separate product, may be stored in the above computer-readable storage medium. Based on such understanding, the technical solution of the present application may be substantially implemented or a part of or all or part of the technical solution contributing to the prior art may be embodied in the form of a software product stored in a storage medium, and including instructions for causing one or more computer devices (which may be personal computers, servers, network devices, or the like) to execute all or part of the steps of the method described in the embodiments of the present application.
In the above embodiments of the present application, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the several embodiments provided in the present application, it should be understood that the disclosed client may be implemented in other manners. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one type of division of logical functions, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, units or modules, and may be in an electrical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit may be implemented in the form of hardware, or may also be implemented in the form of a software functional unit.
The foregoing is only a preferred embodiment of the present application and it should be noted that, as will be apparent to those skilled in the art, numerous modifications and adaptations can be made without departing from the principles of the present application and such modifications and adaptations are intended to be considered within the scope of the present application.
Claims (8)
1. A method for wide dynamic adjustment, comprising:
acquiring a plurality of exposure images in the same scene at the same time;
respectively performing noise reduction processing on the multiple exposure images by using first parameters, wherein each exposure image corresponds to a value of one first parameter, the value of the first parameter is related to a second parameter corresponding to a previous frame of original data corresponding to the exposure image in a wide dynamic fusion process, and the value of the second parameter has a mapping relation with the brightness interval distribution of the previous frame of original data corresponding to the exposure image;
obtaining values of the second parameters respectively corresponding to the current frame original data corresponding to the multiple exposure images in a wide dynamic fusion process, and fusing the multiple exposure images to obtain a wide dynamic fusion image;
wherein the performing the noise reduction processing on the plurality of exposure images respectively by using the first fusion weight parameter comprises:
acquiring an interframe change value of original data corresponding to a target exposure image, wherein the interframe change value represents a difference value between current frame original data and previous frame original data corresponding to the target exposure image;
adjusting the interframe change value according to the brightness information of the current frame original data corresponding to the target exposure image;
determining a value of a target first parameter corresponding to the target exposure image according to the adjusted mapping relation between the interframe change value and the first parameter;
adjusting the target first parameter by using a target second parameter corresponding to the previous frame of original data corresponding to the target exposure image in the wide dynamic fusion process;
fusing current frame original data and previous frame original data corresponding to the target exposure image by using the adjusted first target parameter to obtain fused data corresponding to the target exposure image after noise reduction processing;
adjusting the first target parameter by using a second target parameter corresponding to the previous frame of original data corresponding to the target exposure image in the wide dynamic fusion process, wherein the method comprises the following steps:
obtaining a value of a second target parameter of the previous frame of original data corresponding to the target exposure image in the wide dynamic fusion process;
performing histogram statistics on pixel points of the previous frame of original data, of which the value of the target second parameter is not 0, and obtaining integral brightness interval distribution of the previous frame of original data according to the number of pixel points of different brightness intervals in the previous frame of original data;
obtaining a value of a second adjustment coefficient according to the mapping relation between the brightness interval distribution of the whole original data of the previous frame and the second adjustment coefficient;
and multiplying the target first parameter by the second adjusting coefficient to obtain the adjusted target first parameter.
2. The method of claim 1, wherein the obtaining the frame-to-frame variation value of the raw data corresponding to the target exposure image comprises:
dividing a traversal window of n x m from the current frame of original data and the previous frame of original data corresponding to the target exposure image, wherein n and m are positive integers, and dividing the traversal window according to r, g and b color components;
calculating a difference value between the current frame original data and the previous frame original data according to the traversal window, and then dividing the difference value by a larger value between the current frame original data and the previous frame original data to obtain a first result;
accumulating the first results according to r, g and b color components respectively, and then calculating an average value to obtain an r average value, a g average value and a b average value;
and adding the r mean value, the g mean value and the b mean value, and then calculating the mean value to obtain the interframe change value of the original data corresponding to the target exposure image.
3. The method of claim 1, wherein the adjusting the inter-frame variation value according to the brightness information of the current frame raw data corresponding to the target exposure image comprises:
obtaining a numerical value corresponding to a first adjustment coefficient according to a mapping relation between a brightness value of current frame original data corresponding to the target exposure image and the first adjustment coefficient;
and multiplying the interframe change value by the first adjusting coefficient to obtain the adjusted interframe change value.
4. The method of claim 1, wherein fusing current frame raw data and previous frame raw data corresponding to the target exposure image using the adjusted target first parameter to obtain fused data corresponding to the target exposure image after denoising, comprises:
multiplying the adjusted target first parameter with the original data of the previous frame to obtain fusion data of the previous frame;
multiplying a third parameter by the current frame original data to obtain current frame fusion data, wherein the sum of the third parameter and the target first parameter is 1;
and adding the previous frame fusion data and the current frame fusion data to obtain fusion data corresponding to the target exposure image after noise reduction processing.
5. The method according to any one of claims 1 to 4, wherein after obtaining values of the second parameter corresponding to the current frame raw data corresponding to the plurality of exposure images respectively in a wide dynamic fusion process, the method further comprises:
performing histogram statistics on pixel points, in which the value of the second parameter is not 0, in the current frame original data corresponding to the first exposure image in the multiple exposure images according to the value of the second parameter corresponding to the current frame original data corresponding to the first exposure image in the wide dynamic fusion process;
and adjusting the value of the first parameter used by the next frame of original data corresponding to the first exposure image in the noise reduction process according to the result of the histogram statistics.
6. A wide dynamic adjustment device, comprising:
the acquisition module is used for acquiring a plurality of exposure images in the same scene at the same time;
the noise reduction module is used for respectively performing noise reduction processing on the multiple exposure images by using first parameters, wherein each exposure image corresponds to a value of one first parameter, the value of the first parameter is related to a second parameter corresponding to a previous frame of original data corresponding to the exposure image in a wide dynamic fusion process, and the value of the second parameter has a mapping relation with the brightness interval distribution of the previous frame of original data corresponding to the exposure image;
the fusion module is used for acquiring values of the second parameters corresponding to the current frame original data corresponding to the multiple exposure images in a wide dynamic fusion process, and fusing the multiple exposure images to obtain a wide dynamic fusion image;
wherein the noise reduction module is further configured to:
acquiring an interframe change value of original data corresponding to a target exposure image, wherein the interframe change value represents a difference value between current frame original data and previous frame original data corresponding to the target exposure image;
adjusting the interframe change value according to the brightness information of the current frame original data corresponding to the target exposure image;
determining a value of a target first parameter corresponding to the target exposure image according to the adjusted mapping relation between the interframe change value and the first parameter;
adjusting the first target parameter by using a second target parameter corresponding to the previous frame of original data corresponding to the target exposure image in the wide dynamic fusion process;
fusing current frame original data and previous frame original data corresponding to the target exposure image by using the adjusted first target parameter to obtain fused data corresponding to the target exposure image after noise reduction processing;
and adjusting the target first parameter by using a target second parameter corresponding to the previous frame of original data corresponding to the target exposure image in a wide dynamic fusion process in the following way: obtaining a value of a second target parameter of the previous frame of original data corresponding to the target exposure image in the wide dynamic fusion process; performing histogram statistics on pixel points of the previous frame of original data, of which the value of the target second parameter is not 0, and obtaining integral brightness interval distribution of the previous frame of original data according to the number of pixel points of different brightness intervals in the previous frame of original data; obtaining a value of a second adjustment coefficient according to the mapping relation between the brightness interval distribution of the whole original data of the previous frame and the second adjustment coefficient; and multiplying the target first parameter by the second adjusting coefficient to obtain the adjusted target first parameter.
7. A computer-readable storage medium, in which a computer program is stored, wherein the computer program is configured to carry out the method of any one of claims 1 to 5 when executed.
8. An electronic device comprising a memory and a processor, wherein the memory has stored therein a computer program, and wherein the processor is arranged to execute the computer program to perform the method of any of claims 1 to 5.
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