CN110782414A - A dark-light image denoising method based on densely connected convolution - Google Patents
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Abstract
Description
技术领域technical field
本发明涉及数字图像处理技术领域,特别是一种基于密集连接卷积的暗光图像去噪方法。The invention relates to the technical field of digital image processing, in particular to a dark-light image denoising method based on densely connected convolution.
背景技术Background technique
图像噪声是指存在于图像数据中不必要的或者多余的干扰信息,噪声的存在严重影响了图像的质量,降噪显得尤为重要。Image noise refers to unnecessary or redundant interference information existing in image data. The existence of noise seriously affects the quality of the image, and noise reduction is particularly important.
现有的图像去噪方法,普遍利用图像的局部信息来平滑处理,或者把图像分成一定大小的块,根据图像块之间的相似性,把具有相似结构的二维图像块组合在一起形成三维数组,然后用联合滤波的方法对这些三维数组进行处理,通过逆变换,把处理后的结果返回到原图像中,从而得到去噪后的图像。The existing image denoising methods generally use the local information of the image for smoothing, or divide the image into blocks of a certain size, and combine two-dimensional image blocks with similar structures to form a three-dimensional image block according to the similarity between the image blocks. Then use the method of joint filtering to process these three-dimensional arrays, and return the processed results to the original image through inverse transformation, thereby obtaining the denoised image.
然而这两种图像去噪方法在处理暗光或者逆光场景下的图像时,往往使图像丢失了很多细节信息,不能得到高质量的去噪图像,不能达到良好的去噪效果。However, when these two image denoising methods deal with images in dark light or backlight scenes, the images often lose a lot of detail information, cannot obtain high-quality denoised images, and cannot achieve good denoising effects.
发明内容SUMMARY OF THE INVENTION
有鉴于此,本发明的目的是提出一种基于密集连接卷积的暗光图像去噪方法,实现了即使在处理暗光或者逆光场景下的图像时,也能保留大量图像细节信息,得到高质量的去噪图像,达到良好的去噪效果。In view of this, the purpose of the present invention is to propose a dark-light image denoising method based on densely connected convolution, which realizes that even when processing images in dark-light or backlight scenes, a large amount of image detail information can be retained, and high-quality images can be obtained. High-quality denoised images to achieve good denoising effects.
本发明采用以下方案实现:一种基于密集连接卷积的暗光图像去噪方法,包括以下步骤:The present invention adopts the following scheme to realize: a dark light image denoising method based on dense connection convolution, comprising the following steps:
构建密集连接去噪卷积神经网络模型,并对其进行训练;Build a densely connected denoising convolutional neural network model and train it;
获取待处理图像,并对所述待处理图像进行预处理,得到目标待处理图像;Obtaining an image to be processed, and preprocessing the image to be processed to obtain a target image to be processed;
通过训练好的密集连接去噪卷积神经网络模型对目标待处理图像进行处理,得到去噪图像。The target image to be processed is processed through the trained densely connected denoising convolutional neural network model to obtain a denoised image.
进一步地,所述构建密集连接去噪卷积神经网络模型具体为:所述密集连接去噪卷积神经网络模型包括八个密集连接卷积块,将四通道图像作为网络的输入,进行多个密集连接卷积块的处理;其中,前四个块中,每进行一个块,再进行一次下采样,后四个块中,先和前面同一尺寸的结果连接进行一个上采样,再进入块处理;最后输出3通道RGB图像。Further, the construction of the densely connected denoising convolutional neural network model is specifically: the densely connected denoising convolutional neural network model includes eight densely connected convolutional blocks, the four-channel image is used as the input of the network, and multiple The processing of densely connected convolution blocks; in which, in the first four blocks, each block is subjected to downsampling, and in the last four blocks, it is connected with the previous results of the same size to perform an upsampling, and then enters the block processing ; Finally output a 3-channel RGB image.
进一步地,每个密集连接卷积块包括四个卷积层,每个卷积层通过在块内跳跃连接到之后的每个卷积层,最后一个卷积层为1*1卷积层,该1*1卷积层的输出与块的输入相加得到当前密集连接卷积块的输出。Further, each densely connected convolutional block includes four convolutional layers, each convolutional layer is connected to each subsequent convolutional layer by skipping within the block, and the last convolutional layer is a 1*1 convolutional layer, The output of this 1*1 convolutional layer is added to the input of the block to obtain the output of the current densely connected convolutional block.
较佳的,不同尺寸的块组合是从输入块的尺寸每次下采样为1/2倍,最后再每次上采样为2倍,直到回到输入的尺寸,在相同尺寸块中,前面的块的输出连接到后面的块。Preferably, the combination of blocks of different sizes is to downsample the size of the input block by 1/2 times each time, and finally upsample by 2 times each time until it returns to the input size. In the same size block, the previous The output of the block is connected to the following block.
进一步地,所述密集连接去噪卷积神经网络模型的训练具体为:Further, the training of the densely connected denoising convolutional neural network model is specifically:
构造实验训练数据集,该数据集包括多组曝光图像,每一组曝光图像包括同一场景的短曝光图像以及长曝光图像;利用构造好的密集连接卷积神经网络模型对实验训练数据集进行训练,其中令损失函数为网络输出结果与长曝光图像的差值绝对值的平均值,训练的目标为损失函数最小。Construct an experimental training data set, which includes multiple sets of exposure images, each set of exposure images includes short-exposure images and long-exposure images of the same scene; use the constructed densely connected convolutional neural network model to train the experimental training data set , where the loss function is the average value of the absolute value of the difference between the network output and the long exposure image, and the training target is the minimum loss function.
进一步地,所述对所述待处理图像进行预处理具体为:Further, the preprocessing of the to-be-processed image is specifically:
将所述待处理图像处理成多个单颜色通道图像;processing the to-be-processed image into a plurality of single-color channel images;
将所述多个单颜色通道图像进行拼接,得到目标待处理图像。The multiple single-color channel images are spliced to obtain the target image to be processed.
进一步地,所述通过训练好的密集连接去噪卷积神经网络模型对目标待处理图像进行处理,得到去噪图像具体为:所述神经网络模型通过一些密集连接卷积,对图像进行不同尺度上的处理,输出去除噪声后的图像。Further, the denoising convolutional neural network model of the trained dense connection is used to process the target image to be processed, and the obtained denoising image is specifically: the neural network model is convolved through some dense connections, and the image is subjected to different scales. The above processing outputs the image after denoising.
与现有技术相比,本发明有以下有益效果:本发明采用构造的密集连接卷积神经网络对输入的噪声图像进行处理,实现了即使在处理暗光或者逆光场景下的图像时,也能保留大量图像细节信息,得到高质量的去噪图像,达到良好的去噪效果。Compared with the prior art, the present invention has the following beneficial effects: the present invention adopts the densely connected convolutional neural network constructed to process the input noise image, and realizes that even when processing the image in the dark light or backlight scene, the Retain a lot of image detail information, get high-quality denoised images, and achieve good denoising effects.
附图说明Description of drawings
图1为本发明实施例的神经网络块的设计图。FIG. 1 is a design diagram of a neural network block according to an embodiment of the present invention.
图2为本发明实施例的密集连接神经网络模型的整体设计图。FIG. 2 is an overall design diagram of a densely connected neural network model according to an embodiment of the present invention.
图3为本发明实施例的原始图像。FIG. 3 is an original image of an embodiment of the present invention.
图4为本发明实施例的采用现有方法还原图3的图像。FIG. 4 is an example of restoring the image of FIG. 3 by using an existing method according to an embodiment of the present invention.
图5为本发明实施例的方法还原的图3图像。FIG. 5 is the image of FIG. 3 restored by the method of the embodiment of the present invention.
图6为本发明实施例的图3中方框内的原始放大图像。FIG. 6 is an original enlarged image in the box in FIG. 3 according to an embodiment of the present invention.
图7为本发明实施例的图3中方框内的采用现有方法还原的放大图像。FIG. 7 is an enlarged image within the box in FIG. 3 that is restored by using an existing method according to an embodiment of the present invention.
图8为本发明实施例的图3中方框内的采用本实施例方法还原的放大图像。FIG. 8 is an enlarged image within the box in FIG. 3 according to an embodiment of the present invention, which is restored by the method of this embodiment.
具体实施方式Detailed ways
下面结合附图及实施例对本发明做进一步说明。The present invention will be further described below with reference to the accompanying drawings and embodiments.
应该指出,以下详细说明都是示例性的,旨在对本申请提供进一步的说明。除非另有指明,本文使用的所有技术和科学术语具有与本申请所属技术领域的普通技术人员通常理解的相同含义。It should be noted that the following detailed description is exemplary and intended to provide further explanation of the application. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
需要注意的是,这里所使用的术语仅是为了描述具体实施方式,而非意图限制根据本申请的示例性实施方式。如在这里所使用的,除非上下文另外明确指出,否则单数形式也意图包括复数形式,此外,还应当理解的是,当在本说明书中使用术语“包含”和/或“包括”时,其指明存在特征、步骤、操作、器件、组件和/或它们的组合。It should be noted that the terminology used herein is for the purpose of describing specific embodiments only, and is not intended to limit the exemplary embodiments according to the present application. As used herein, unless the context clearly dictates otherwise, the singular is intended to include the plural as well, furthermore, it is to be understood that when the terms "comprising" and/or "including" are used in this specification, it indicates that There are features, steps, operations, devices, components and/or combinations thereof.
本实施例提供了一种基于密集连接卷积的暗光图像去噪方法,包括以下步骤:This embodiment provides a dark-light image denoising method based on densely connected convolution, including the following steps:
构建密集连接去噪卷积神经网络模型,并对其进行训练;Build a densely connected denoising convolutional neural network model and train it;
获取待处理图像,并对所述待处理图像进行预处理,得到目标待处理图像;Obtaining an image to be processed, and preprocessing the image to be processed to obtain a target image to be processed;
通过训练好的密集连接去噪卷积神经网络模型对目标待处理图像进行处理,得到去噪图像。The target image to be processed is processed through the trained densely connected denoising convolutional neural network model to obtain a denoised image.
在本实施例中,如图2所示,所述构建密集连接去噪卷积神经网络模型具体为:所述密集连接去噪卷积神经网络模型包括八个密集连接卷积块,将四通道图像作为网络的输入,进行多个密集连接卷积块的处理;其中,前四个块中,每进行一个块,再进行一次下采样,后四个块中,先和前面同一尺寸的结果连接进行一个上采样,再进入块处理;最后输出3通道RGB图像。In this embodiment, as shown in FIG. 2 , the construction of the densely connected denoising convolutional neural network model is specifically: the densely connected denoising convolutional neural network model includes eight densely connected convolutional blocks, and the four-channel The image is used as the input of the network to process multiple densely connected convolution blocks; among them, in the first four blocks, for each block, downsampling is performed again, and in the last four blocks, it is first connected with the previous results of the same size Perform an upsampling, and then enter the block processing; finally output a 3-channel RGB image.
在本实施例中,如图1所示,每个密集连接卷积块包括四个卷积层,每个卷积层通过在块内跳跃连接到之后的每个卷积层,最后一个卷积层为1*1卷积层,该1*1卷积层的输出与块的输入相加得到当前密集连接卷积块的输出。In this embodiment, as shown in Figure 1, each densely connected convolutional block includes four convolutional layers, each convolutional layer is connected to each subsequent convolutional layer by skipping within the block, and the last convolutional layer The layer is a 1*1 convolutional layer, and the output of the 1*1 convolutional layer is added to the input of the block to obtain the output of the current densely connected convolutional block.
较佳的,不同尺寸的块组合是从输入块的尺寸每次下采样为1/2倍,最后再每次上采样为2倍,直到回到输入的尺寸,在相同尺寸块中,前面的块的输出连接到后面的块。Preferably, the combination of blocks of different sizes is to downsample the size of the input block by 1/2 times each time, and finally upsample by 2 times each time until it returns to the input size. In the same size block, the previous The output of the block is connected to the following block.
在本实施例中,所述密集连接去噪卷积神经网络模型的训练具体为:In this embodiment, the training of the densely connected denoising convolutional neural network model is specifically:
构造实验训练数据集,该数据集包括多组曝光图像,每一组曝光图像包括同一场景的短曝光图像以及长曝光图像;即用户上传的图像样本集中,每一组曝光图像包括同一场景的短曝光图像以及长曝光图像。同一场景的短曝光图像是指在拍摄的场景、光线条件(如逆光、顺光、暗光等)、曝光时间、感光度、曝光量等相同的情况下拍摄的图像,同一场景的长曝光图像是指与短曝光图像拍摄的场景相同,其他条件明显优于短曝光图像的拍摄条件下拍摄得到的图像;利用构造好的密集连接卷积神经网络模型对实验训练数据集进行训练,其中令损失函数为网络输出结果与长曝光图像的差值绝对值的平均值,训练的目标为损失函数最小。Construct the experimental training data set, which includes multiple sets of exposure images, each set of exposure images includes short-exposure images and long-exposure images of the same scene; that is, in the image sample set uploaded by the user, each set of exposure images includes short-exposure images of the same scene. exposure images as well as long exposure images. A short exposure image of the same scene refers to an image taken under the same conditions as the shooting scene, light conditions (such as backlight, front light, dark light, etc.), exposure time, sensitivity, exposure, etc., and a long exposure image of the same scene. Refers to the image taken under the same scene as the short-exposure image, and other conditions are significantly better than the short-exposure image; the constructed densely connected convolutional neural network model is used to train the experimental training data set, in which the loss The function is the average value of the absolute value of the difference between the network output and the long exposure image, and the training goal is to minimize the loss function.
在本实施例中,所述对所述待处理图像进行预处理具体为:In this embodiment, the preprocessing of the to-be-processed image is specifically:
将所述待处理图像处理成多个单颜色通道图像;processing the to-be-processed image into a plurality of single-color channel images;
将所述多个单颜色通道图像进行拼接,得到目标待处理图像。The multiple single-color channel images are spliced to obtain the target image to be processed.
特别的,终端可以通过调用预设函数将获取的待处理图像的通道模式转换为多个单颜色通道图像。保存图像颜色信息的通道称为颜色通道,每个颜色通道都存放着图像中颜色元素的信息。只由一种颜色元素的信息组成的颜色通道图像为单颜色通道图像。如RGB模式图像中,R为一个红色通道,G为一个绿色通道,B 为一个蓝色通道。Particularly, the terminal can convert the acquired channel mode of the image to be processed into multiple single-color channel images by calling a preset function. The channel that saves the color information of the image is called the color channel, and each color channel stores the information of the color elements in the image. A color channel image composed of information of only one color element is a single color channel image. For example, in an RGB mode image, R is a red channel, G is a green channel, and B is a blue channel.
例如,终端获取的待处理图像为原始图像(RAW Image Format,RAW),即未经处理、未经压缩的原始图像信息,此时,终端调用预设函数将原始图像的多颜色单通道模式转换为多个单颜色通道图像。具体地,终端通过调用的预设函数对原始图像中的每个颜色进行提取,并生成多个单颜色通道图像。其中,该预设函数可以采用现有技术中的函数。For example, the image to be processed obtained by the terminal is a raw image (RAW Image Format, RAW), that is, the raw image information that is unprocessed and uncompressed. At this time, the terminal calls the preset function to convert the multi-color single-channel mode of the raw image. for multiple single-color channel images. Specifically, the terminal extracts each color in the original image by calling the preset function, and generates multiple single-color channel images. Wherein, the preset function may adopt a function in the prior art.
在本实施例中,所述通过训练好的密集连接去噪卷积神经网络模型对目标待处理图像进行处理,得到去噪图像具体为:所述神经网络模型通过一些密集连接卷积,对图像进行不同尺度上的处理,输出去除噪声后的图像。In this embodiment, the denoising convolutional neural network model that has been trained to process the target image to be processed to obtain a denoised image is specifically: the neural network model convolutions the image through some dense connections Perform processing on different scales, and output the image after noise removal.
采用本实施例的方法能够实现即使在处理暗光或者逆光场景下的图像时,也能保留大量图像细节信息,得到高质量的去噪图像,达到良好的去噪效果。By using the method of this embodiment, even when processing an image in a dark light or backlight scene, a large amount of image detail information can be retained, a high-quality denoised image can be obtained, and a good denoising effect can be achieved.
特别的,为了验证本实施例的有效性,对如图3所示的原始图像采用现有方法与本实施例方法的去噪处理,其结果如图4至图8所示。其中图4为采用现有方法去噪后的图像,图5为采用本实施例方法去噪后的图像。为了显示更清晰,本实施例对图3中的方框内图像进行放大,如图6所示,图7为采用现有技术去噪后方框内的图像,图8为采用本实施例方法去噪后方框内的图像。由附图可知,本实施例的方法去噪效果明显优于现有技术,得到的图像去噪效果更好。In particular, in order to verify the effectiveness of this embodiment, the denoising processing of the existing method and the method of this embodiment is used for the original image shown in FIG. 3 , and the results are shown in FIGS. 4 to 8 . FIG. 4 is an image denoised by using the existing method, and FIG. 5 is an image denoised by the method of this embodiment. In order to display more clearly, in this embodiment, the image in the box in FIG. 3 is enlarged, as shown in FIG. 6 , FIG. 7 is the image in the box after denoising using the prior art, and FIG. The image inside the denoised box. It can be seen from the drawings that the denoising effect of the method in this embodiment is obviously better than that of the prior art, and the obtained image denoising effect is better.
本领域内的技术人员应明白,本申请的实施例可提供为方法、系统、或计算机程序产品。因此,本申请可采用完全硬件实施例、完全软件实施例、或结合软件和硬件方面的实施例的形式。而且,本申请可采用在一个或多个其中包含有计算机可用程序代码的计算机可用存储介质(包括但不限于磁盘存储器、CD-ROM、光学存储器等)上实施的计算机程序产品的形式。As will be appreciated by those skilled in the art, the embodiments of the present application may be provided as a method, a system, or a computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.
本申请是参照根据本申请实施例的方法、设备(系统)、和计算机程序产品的流程图和/或方框图来描述的。应理解可由计算机程序指令实现流程图和/或方框图中的每一流程和/或方框、以及流程图和/或方框图中的流程和/或方框的结合。可提供这些计算机程序指令到通用计算机、专用计算机、嵌入式处理机或其他可编程数据处理设备的处理器以产生一个机器,使得通过计算机或其他可编程数据处理设备的处理器执行的指令产生用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的装置。The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present application. It will be understood that each flow and/or block in the flowcharts and/or block diagrams, and combinations of flows and/or blocks in the flowcharts and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to the processor of a general purpose computer, special purpose computer, embedded processor or other programmable data processing device to produce a machine such that the instructions executed by the processor of the computer or other programmable data processing device produce Means for implementing the functions specified in one or more of the flowcharts and/or one or more blocks of the block diagrams.
这些计算机程序指令也可存储在能引导计算机或其他可编程数据处理设备以特定方式工作的计算机可读存储器中,使得存储在该计算机可读存储器中的指令产生包括指令装置的制造品,该指令装置实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能。These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory result in an article of manufacture comprising instruction means, the instructions An apparatus implements the functions specified in a flow or flows of the flowcharts and/or a block or blocks of the block diagrams.
这些计算机程序指令也可装载到计算机或其他可编程数据处理设备上,使得在计算机或其他可编程设备上执行一系列操作步骤以产生计算机实现的处理,从而在计算机或其他可编程设备上执行的指令提供用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的步骤。These computer program instructions can also be loaded on a computer or other programmable data processing device to cause a series of operational steps to be performed on the computer or other programmable device to produce a computer-implemented process such that The instructions provide steps for implementing the functions specified in one or more of the flowcharts and/or one or more blocks of the block diagrams.
以上所述,仅是本发明的较佳实施例而已,并非是对本发明作其它形式的限制,任何熟悉本专业的技术人员可能利用上述揭示的技术内容加以变更或改型为等同变化的等效实施例。但是凡是未脱离本发明技术方案内容,依据本发明的技术实质对以上实施例所作的任何简单修改、等同变化与改型,仍属于本发明技术方案的保护范围。The above are only preferred embodiments of the present invention, and are not intended to limit the present invention in other forms. Any person skilled in the art may use the technical content disclosed above to make changes or modifications to equivalent changes. Example. However, any simple modifications, equivalent changes and modifications made to the above embodiments according to the technical essence of the present invention without departing from the content of the technical solutions of the present invention still belong to the protection scope of the technical solutions of the present invention.
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