CN110675333A - Microscopic imaging processing method based on neural network super-resolution technology - Google Patents
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Abstract
The invention discloses a microscopic imaging processing method based on a neural network super-resolution technology, which comprises the following steps: training the full convolution neural network and arranging the trained full convolution neural network M on a computer for controlling the microscope, controlling the microscope to shoot pictures, and compensating the shot pictures in real time to obtain clear pictures. The processing method disclosed by the invention greatly improves the shooting speed, improves the picture quality, and inhibits defocusing blur, especially when a plurality of pictures are shot for a sample; even can replace the automatic focusing, remove the motor that controls the lens and move up and down, simplify the optical detection system.
Description
Technical Field
The invention relates to an image processing method, in particular to a microscopic imaging processing method based on a neural network super-resolution technology.
Background
Microscopic imaging techniques are an effective means of observing cells with high spatial and temporal resolution. Before observing the sample, researchers need to focus the microscope once, but when the sample area is too large and needs to be observed continuously, the above operation has problems: if the sample is curved more than the depth of field of the optical detection system used, a sharp partial image or a blurred partial image will occur. This situation can seriously affect the subsequent analysis and judgment.
The super-resolution technology refers to a process of restoring a high-resolution image from a given low-resolution image by using a specific algorithm and a processing flow, using knowledge related to the fields of digital image processing, computer vision, and the like. The method aims to overcome or compensate the problems of imaging image blurring, low quality, insignificant region of interest and the like caused by the limitation of an image acquisition system or an acquisition environment. Super-resolution is directed to blurring due to the resolution degradation caused by bicubic sampling, but can also be applied to overcome defocus blurring in theory.
The solution adopted in the prior art is to move the lens up and down when the imaging system scans, so that there is a small overlapping area in three consecutive images, and the position of the focal plane is determined by calculating the ambiguity of the overlapping area. The method is essentially equivalent to sampling in multiple places, and is slow and time-consuming.
Disclosure of Invention
In order to solve the technical problems, the invention provides a microscopic imaging processing method based on a neural network super-resolution technology, so as to achieve the purposes of greatly improving the shooting speed, improving the picture quality and inhibiting defocusing blur.
In order to achieve the purpose, the technical scheme of the invention is as follows:
a microscopic imaging processing method based on a neural network super-resolution technology comprises the following steps:
(1) training of the full convolution neural network:
shooting a group of clear pictures Y by a microscope, then carrying out Gaussian filtering on the Y to obtain corresponding defocused blurred pictures X, converting image information data X, Y into numpy arrays, then carrying out normalization processing on the arrays, and respectively recording the normalized arrays as Xnorm、YnormWhere X isnorm、YnormAre two of the same shapeThe shape of the matrix is uniformly recorded as L W H C, L is the number of the taken clear pictures, W is the number of rows of the matrix, H is the number of columns of the matrix, if the taken grey pictures are taken, c is 1, and if the taken grey pictures are taken, c is 3;
with XnormBeing an input to the network, YnormFor the output of the network, the learning rate is set to be 3E-4, an Adam optimizer is adopted during training, the network is trained, and a full convolution neural network M is obtained;
(2) microscopic imaging treatment:
arranging the trained full convolution neural network M on a computer for controlling a microscope, controlling the microscope to shoot pictures, simultaneously utilizing the trained full convolution neural network M to compensate the shot pictures in real time, firstly carrying out normalization operation on the shot pictures to obtain an array of 1W H c, taking the array as the input of the network to obtain the output with the shape of 1W H c, carrying out normalization on the output, and mapping pixel values to 0-255 to obtain a clear picture.
In the above scheme, the full convolution neural network convolves the input of the network, and is provided with a two-dimensional matrix a and a matrix B, and a calculation formula of a convolution result matrix C of the two-dimensional matrix a and the matrix B is as follows:
C(j，k)＝∑p∑qA(p，q)B(j-p+1，k-q+1)
wherein p and q are respectively the abscissa and the ordinate of the matrix A, j and k are respectively the abscissa and the ordinate of the matrix C, and if the matrix elements exceed the boundary, the values are replaced by 0.
In a further technical scheme, when the full convolution neural network performs convolution, the input shape is Winput*HinputMatrix of c, with n Wfilter*HfilterConvolution kernels of c are respectively convolved to obtain the shape Woutput*HoutputN, W is the number of rows of the matrix, H is the number of columns of the matrix, subscripts represent the matrix to which the subscripts belong, and c is the number of characteristic channels of the matrix;
the output is regarded as the characteristic extracted by the convolution layer, when the loss function is appointed and the truth value is given, the parameter in the convolution kernel is updated according to the size of the loss function and the gradient descending direction to the fastest descending direction, wherein
Woutput＝(Winput-Wfilter+2P)/S+1
Houtput＝(Hinput-Hfilter+2P)/S+1
P is the padding size and S is the step size.
In a further technical solution, the loss function formula is as follows:
wherein the content of the first and second substances,
as a function of the losses of the network,is the output of the network and is,
is a true value, | × | non-conducting phosphor1Is the norm of L1.
In a further technical scheme, when the full convolution neural network performs convolution, the number of convolution kernels in each of up-sampling and down-sampling is 32; the down-sampling comprises two convolution layers and a maximum pooling layer, the size of the convolution kernel is 3 x 3, and the maximum pooling step length is 2 x 2; adding a convolution layer behind the tensor obtained by jump connection, synthesizing the characteristics between different layers, wherein the size of a convolution kernel is 3 x 3, and then performing up-sampling; the up-sampling uses deconvolution, with convolution kernel size of 2 x 2 and step size of 2 x 2.
By the technical scheme, the microscopic imaging processing method based on the neural network super-resolution technology compensates image blur by the super-resolution technology, the trouble of focusing at each shooting position can be avoided by the technology, the shooting speed is greatly improved, and especially when a plurality of pictures are shot on a sample; even can replace the automatic focusing, remove the motor that controls the lens and move up and down, simplify the optical detection system. During shooting, the method carries out convolution processing on the shot picture by using a full convolution neural network, and carries out real-time compensation on the picture, thereby obtaining a clear picture.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below.
FIG. 1 is a schematic diagram of a full convolution neural network according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a convolution process according to an embodiment of the present invention;
FIG. 3 is a pre-processed image as disclosed in an embodiment of the present invention;
fig. 4 is a processed image as disclosed in an embodiment of the invention.
Detailed Description
The technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.
The invention provides a microscopic imaging processing method based on a neural network super-resolution technology, which can improve the picture quality and inhibit defocusing blur.
A microscopic imaging processing method based on a neural network super-resolution technology comprises the following steps:
(1) training of the full convolution neural network:
shooting a group of clear pictures Y by a microscope, then carrying out Gaussian filtering on the Y to obtain corresponding defocused blurred pictures X, converting image information data X, Y into numpy arrays, then carrying out normalization processing on the arrays, and respectively recording the normalized arrays as Xnorm、YnormHere Y isnorm、YnormThe method comprises the following steps that two matrixes with the same shape are provided, the shapes are uniformly marked as L, W, H and c, L is the number of clear pictures to be shot, W is the number of rows of the matrixes, H is the number of columns of the matrixes, if grey pictures are shot, c is 1, and if color pictures are shot, c is 3;
with XnormBeing an input to the network, YnormAnd (3) setting the learning rate as 3E-4 for the output of the network, and training the network by adopting an Adam optimizer during training to obtain the full convolution neural network M.
Full convolution neural network M As shown in FIG. 1, the input is obtained by successive down-sampling0，0、X1，0、X2，0、X3，0、X4 ，0The down-sampling can increase the robustness to some small disturbances of the input image, such as image translation, rotation, etc., reduce the risk of over-fitting, reduce the amount of computation, and increase the size of the receptive field. Then respectively adding X1，0、X2，0、X3，0、X4，0Upsampling, the effect of which is to re-decode the abstract features to the size of the original image, i.e. X1，0Up-sampling obtains X0，1Is mixing X2，0Up-sampling obtains X in turn1，1、X0，2Is mixing X3，0Up-sampling obtains X in turn2，1、X1，2、X0，3Is mixing X4，0Up-sampling obtains X in turn3，1、X2，2、X1 ，3、X0，4. In addition, in order to integrate features of different layers, a large number of hopping connections, such as X, are added to the network0，0To X0，1、X0，2、X0，3、X0，4There is a jump connection. Finally, in order to make the network converge better, a strategy of deep supervision is added, namely X0 ，1、X0，2、X0，3、X0，4Will be compared with the ground channel and participate in the calculation of the loss function.
The invention adopts convolution-convolution mode, in order to reduce memory occupation and accelerate calculation speed, the number of convolution kernels of each layer of down-sampling is fixed to 32, the down-sampling comprises two layers of convolution layers and one layer of maximum pooling layer, the size of the convolution kernels is 3 x 3, and the maximum pooling step length is 2 x 2. And adding a convolution layer behind the tensor obtained by jump connection, synthesizing the characteristics between different layers, wherein the size of a convolution kernel is 3 x 3, and then performing up-sampling. Up sampling and samplingDeconvolution was used, with convolution kernel size 2 x 2, step size 2 x 2, and the number of convolution kernels in each upsampling was also fixed at 32. In order to reduce the memory occupation, the invention adopts an add mode for jump connection. Finally, the loss function
Defined as L1 norm, specifically defined as follows:
wherein the content of the first and second substances,
is the output of the network and is,
is a true value, | × | non-conducting phosphor1Is the norm of L1.
The full convolution neural network is used for convolving the input of the network, and is provided with a two-dimensional matrix A and a matrix B, and the calculation formula of a convolution result matrix C of the two-dimensional matrix A and the matrix B is as follows:
C(j，k)＝∑p∑qA(p，q)B(j-p+1，k-q+1)
wherein p and q are respectively the abscissa and the ordinate of the matrix A, j and k are respectively the abscissa and the ordinate of the matrix C, and if the matrix elements exceed the boundary, the values are replaced by 0.
When the fully convolutional neural network performs convolution, as shown in FIG. 2, the input shape is Winput*HinputMatrix of c, with n Wfilter*HfilterConvolution kernels of c are respectively convolved to obtain the shape Woutput*HoutputN, W is the number of rows of the matrix, H is the number of columns of the matrix, subscripts represent the matrix to which the subscripts belong, and c is the number of characteristic channels of the matrix;
the output is regarded as the characteristic extracted by the convolution layer, when the loss function is appointed and the truth value is given, the parameter in the convolution kernel is updated according to the size of the loss function and the gradient descending direction to the fastest descending direction, wherein
Woutput＝(Winput-Wfilter+2P)/S+1
Houtput＝(Hinput-Hfilter+2P)/S+1
P is the padding size and S is the step size.
(2) Microscopic imaging treatment:
the trained full convolution neural network M is arranged on a computer for controlling a microscope, the microscope is controlled to shoot pictures, the obtained pictures are shown in figure 3, meanwhile, the shot pictures are compensated in real time by the trained full convolution neural network M, namely, the shot pictures are firstly normalized to obtain an array of 1W H c, the array is used as the input of the network to obtain the output with the shape of 1W H c, the output is subjected to normalization, and the pixel value is mapped to 0-255 to obtain a clear picture, which is shown in figure 4.
To further save processing time, two threads or two processes may be broken up in the computer's CPU. One thread/process is responsible for controlling the microscope to take pictures; and the other one is responsible for compensating the shot picture in real time and improving the picture quality, so that the shooting time cannot be increased.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (5)
1. A microscopic imaging processing method based on a neural network super-resolution technology is characterized by comprising the following steps:
(1) training of the full convolution neural network:
shooting a group of clear pictures Y by a microscope, and then carrying out Gaussian filtering on the Y to obtain corresponding defocused blurred picturesFor slice X, image information data X, Y is converted into numpy array, then the array is normalized, and the normalized array is marked as Xnorm、YnormWhere X isnorm、YnormThe method comprises the following steps that two matrixes with the same shape are provided, the shapes are uniformly marked as L, W, H and c, L is the number of clear pictures to be shot, W is the number of rows of the matrixes, H is the number of columns of the matrixes, if grey pictures are shot, c is 1, and if color pictures are shot, c is 3;
with XnormBeing an input to the network, YnormFor the output of the network, the learning rate is set to be 3E-4, an Adam optimizer is adopted during training, the network is trained, and a full convolution neural network M is obtained;
(2) microscopic imaging treatment:
arranging the trained full convolution neural network M on a computer for controlling a microscope, controlling the microscope to shoot pictures, simultaneously utilizing the trained full convolution neural network M to compensate the shot pictures in real time, firstly carrying out normalization operation on the shot pictures to obtain an array of 1W H c, taking the array as the input of the network to obtain the output with the shape of 1W H c, carrying out normalization on the output, and mapping pixel values to 0-255 to obtain a clear picture.
2. A microscopic imaging processing method based on neural network super-resolution technology according to claim 1, characterized in that the fully convolutional neural network convolves the input of the network, and has a two-dimensional matrix a and a matrix B, and the calculation formula of the convolution result matrix C is as follows:
C(j，k)＝∑p∑qA(p，q)B(j-p+1，k-q+1)
wherein p and q are respectively the abscissa and the ordinate of the matrix A, j and k are respectively the abscissa and the ordinate of the matrix C, and if the matrix elements exceed the boundary, the values are replaced by 0.
3. The microscopic imaging processing method based on the neural network super-resolution technology as claimed in claim 2, wherein when the fully-convolutional neural network is convolved, the input shape isWinput*HinputMatrix of c, with n Wfilter*HfilterConvolution kernels of c are respectively convolved to obtain the shape Woutput*HoutputN, W is the number of rows of the matrix, H is the number of columns of the matrix, subscripts represent the matrix to which the subscripts belong, and c is the number of characteristic channels of the matrix;
the output is regarded as the characteristic extracted by the convolution layer, when the loss function is appointed and the truth value is given, the parameter in the convolution kernel is updated according to the size of the loss function and the gradient descending direction to the fastest descending direction, wherein
Woutput＝(Winput-Wfilter+2P)/S+1
Houtput＝(Hinput-Hfilter+2P)/S+1
P is the padding size and S is the step size.
4. The microscopic imaging processing method based on the neural network super-resolution technology as claimed in claim 3, wherein the loss function formula is as follows:
wherein the content of the first and second substances,as a function of the losses of the network,
is the output of the network and is,
is a true value, | × | non-conducting phosphor1Is the norm of L1.
5. The microscopic imaging processing method based on the neural network super-resolution technology as claimed in claim 3, wherein when the fully convolutional neural network performs convolution, the number of convolution kernels in each of the up-sampling and the down-sampling is 32; the down-sampling comprises two convolution layers and a maximum pooling layer, the convolution kernel size is 3 x 3, and the maximum pooling step size is 2 x 2; adding a convolution layer behind the tensor obtained by jump connection, synthesizing the characteristics between different layers, wherein the size of a convolution kernel is 3 x 3, and then performing up-sampling; the up-sampling uses deconvolution, with convolution kernel size of 2 x 2 and step size of 2 x 2.
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