CN104091340A - Blurred image rapid detection method - Google Patents
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- CN104091340A CN104091340A CN201410344777.XA CN201410344777A CN104091340A CN 104091340 A CN104091340 A CN 104091340A CN 201410344777 A CN201410344777 A CN 201410344777A CN 104091340 A CN104091340 A CN 104091340A
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Abstract
The invention discloses a blurred image rapid detection method. The method includes the steps that sample images are collected, fast Fourier transform is conducted on RGB channels of each sample image, the size of complex data obtained after the transform is reduced, new images are constructed through the size-reduced complex data, the obtained newly-constructed reduced images serve as input images of a convolution neural network, clear-blurred image classification training is conducted, a blurred detection model is obtained, fast Fourier transform is conducted on RGB channels of each image to be detected, the size of complex data obtained after the transform is reduced, new images to be detected are constructed through the size-reduced complex data, and clear-blurred image differentiation is conducted on the new images to be detected through the blurred detection model. Thus, the calculated amount is effectively reduced, the detection speed is increased, meanwhile, the method is particularly suitable for rapid blurred detection of images with the large size, and detection accuracy is improved.
Description
Technical field
The present invention relates to the method for quick of a kind of image processing method, particularly a kind of blurred picture.
Background technology
Digital Image Processing has become the fundamental research object of the numerous areas such as information science, biology, medical science.Along with the arrival of information age, Digital Image Processing is all widely used in fields such as computer vision, machine learning, artificial intelligence, and its importance highlights day by day.Regrettably, in the gatherer process of digital picture, collecting device can produce inevitable slight jitter in the moment of shutter opening, this shake often makes us finally can only obtain an image that details is fuzzy, especially in the situation that illumination condition is undesirable, longer aperture time makes the fog-level of image more violent.Such blurred picture has brought very large puzzlement to human eye vision, has also lost a large amount of detailed information simultaneously, cannot be applied in daily life and scientific research activity.Existing image blurring detection method can be divided into two classes substantially: a class provides the estimation of the fog-level of entire image, another kind of image is divided into several regions, regional is provided respectively to the estimation of fog-level, but mostly computing method more complicated, operand is larger, and processing speed is slow.
Summary of the invention
The present invention, for addressing the above problem, provides a kind of method for quick of blurred picture, and testing result more quick and precisely.
For achieving the above object, the technical solution used in the present invention is:
A method for quick for blurred picture, is characterized in that, comprises the following steps:
10. collect sample image, and tri-passages of RGB of each sample image are carried out respectively to Fast Fourier Transform (FFT), obtain the complex data after conversion;
Complex data described in 20. pairs is carried out size and is dwindled processing, and complex data after dwindling by size builds new images, obtains downscaled images;
30. input pictures using the downscaled images of described new structure as convolutional neural networks, carry out clear-blurred picture classification based training, obtain fuzzy detection model;
40. obtain image to be detected, and treat detected image and carry out the Fast Fourier Transform (FFT) of tri-passages of RGB, the complex data that conversion is obtained is carried out size and is dwindled processing, and complex data after dwindling by size builds new image to be detected, then adopt described fuzzy detection model to carry out the differentiation of clear-blurred picture to described new image to be detected.
Preferably, in described step 40, adopt described fuzzy detection model to carry out the differentiation of clear-blurred picture to described new image to be detected, mainly that the new image block to be detected building after conversion is put into convolutional neural networks system, then calculating this new image to be detected according to described fuzzy detection model is the probability of picture rich in detail or blurred picture, and select image type that probability is larger as described image to be detected clear-vague category identifier.
The invention has the beneficial effects as follows:
The method for quick of a kind of blurred picture of the present invention, it is by collecting sample image, and tri-passages of RGB of each sample image are carried out respectively to Fast Fourier Transform (FFT), then the complex data obtaining after conversion is carried out size and dwindled processing, and complex data after dwindling by size builds new images, and input picture using the downscaled images of obtained new structure as convolutional neural networks, carry out clear-blurred picture classification based training, obtain fuzzy detection model, last and treat detected image and carry out the Fast Fourier Transform (FFT) of tri-passages of RGB, the complex data that conversion is obtained is carried out size and is dwindled processing, and complex data after dwindling by size builds new image to be detected, then adopt described fuzzy detection model to carry out the differentiation of clear-blurred picture to described new image to be detected, thereby effectively reduce calculated amount, accelerate detection speed, the Fast Fuzzy that is simultaneously specially adapted to large-size images detects, improve the accuracy rate detecting.
Brief description of the drawings
Accompanying drawing described herein is used to provide a further understanding of the present invention, forms a part of the present invention, and schematic description and description of the present invention is used for explaining the present invention, does not form inappropriate limitation of the present invention.In the accompanying drawings:
Fig. 1 is the general flow chart of the method for quick of a kind of blurred picture of the present invention;
Fig. 2 is the image to be detected for effect of the present invention is described;
Fig. 3 is the complex data of Fig. 2 after Fast Fourier Transform (FFT).
Embodiment
In order to make technical matters to be solved by this invention, technical scheme and beneficial effect clearer, clear, below in conjunction with drawings and Examples, the present invention is further elaborated.Should be appreciated that specific embodiment described herein, only in order to explain the present invention, is not intended to limit the present invention.
As shown in Figure 1, the method for quick of a kind of blurred picture of the present invention, it comprises the following steps:
10. collect sample image, and tri-passages of RGB of each sample image are carried out respectively to Fast Fourier Transform (FFT), obtain the complex data after conversion;
Complex data described in 20. pairs is carried out size and is dwindled processing, and
withcomplex data after size is dwindled builds new images, obtains downscaled images;
30. input pictures using the downscaled images of described new structure as convolutional neural networks, carry out clear-blurred picture classification based training, obtain fuzzy detection model;
40. obtain image to be detected (as Fig. 2), and treat detected image and carry out the Fast Fourier Transform (FFT) of tri-passages of RGB, the complex data (as Fig. 3) that conversion is obtained is carried out size and is dwindled processing, and complex data after dwindling by size builds new image to be detected, then adopt described fuzzy detection model to carry out the differentiation of clear-blurred picture to described new image to be detected.
In described step 40, adopt described fuzzy detection model to carry out the differentiation of clear-blurred picture to described new image to be detected, mainly that the new image block to be detected building after conversion is put into convolutional neural networks system, then calculating this new image to be detected according to described fuzzy detection model is the probability of picture rich in detail or blurred picture, and select image type that probability is larger as described image to be detected clear-vague category identifier.
Input picture using the downscaled images of described new structure as convolutional neural networks in described step 30, carry out clear-blurred picture classification based training, mainly that the downscaled images of new structure is carried out to manual sort, and input convolutional neural networks and carry out class test, and, the downscaled images of classification error in class test is collected and re-starts artificial mark, adjust network structure, again the downscaled images after manual sort is again carried out to training study again, so repeat the process of " training-> adjusts network structure-> retraining " until classification is correct.
In the present embodiment, network order is K the full articulamentum-> of the layer-> of group SoftMax layer of input layer->, and wherein K is more than or equal to 1; Group's layer comprises convolutional layer, active coating, down-sampling layer, normalization layer; In convolutional layer, active coating, down-sampling layer, normalization layer, the core of each layer size and output size can carry out regulating arbitrarily, and each layer has an input and produce an output, and the output of every one deck is as the input of lower one deck.
Wherein, the input size of input layer is Height x Weight x Channel, and wherein Weight, Height are the wide and high of input layer image, and Channel is the Color Channel of input layer image; Because the present invention uses the hard-wired reason of GPU, Weight=Height; The channel of input picture can only be 1 or 3.
Convolutional layer:
1) size of core must be odd number, and is not more than the wide or high of this layer of input;
2) when intermediate representation is by convolutional layer, do not change widely and high, port number is variable can be constant; Can be any positive integer in theory, because the present invention uses the hard-wired reason of GPU, be 16 multiple here.
Active coating:
1) that active coating does not change that convolutional layer represents is wide, height or port number;
2) activation function that active coating is used includes but not limited to following type function:
f(x)＝1/(1+e
-x)
F (x)=a*tanh (b*x), a, b is any non-zero real
f(x)＝max(0，x)
f(x)＝min(a，max(0，x))
f(x)＝log(1+e
x)
f(x)＝|x|
f(x)＝x
2
f(x)＝ax+b
3) active coating is followed at convolutional layer or after full connection.
Down-sampling layer:
1) down-sampling layer does not change the port number of intermediate representation;
2) down-sampling layer is the size of core to the drawdown ratio of image: core is that the down-sampling layer of m x n can cause intermediate representation to be reduced into last layer (1/m) x (1/n), m and n can be random natural number in theory, because the present invention uses the hard-wired reason of GPU, m=n.For example, 15x15x32, by after the down-sampling of 3x3, becomes 5x5x32; 15x15x32, by after the down-sampling of 5x5, becomes 3x3x32; But 15x15x32 can not carry out the down-sampling of 2x2, because 15 can not be divided exactly by 2; Be not, input size must be 2 inferior power, 16,32,64 etc., as long as input size guarantees to be sampled by all down-sampling layers.
Normalization layer:
1) normalization layer does not change any size of intermediate representation;
2) normalization layer not necessarily, must, add normalization layer and conventionally can improve precision and increase calculated amount; Whether add normalization layer, see the precision of actual lifting after adding and the speed of loss.
General combination is: convolution-> activation-> down-sampling-> normalization.
Following situation is special:
1) when interpolation normalization layer has but increased a lot of operand to precision improvement is less, cancel normalization layer, adopt following combination: convolution-> activation-> down-sampling;
2) in advance, effect is basic identical for normalization layer, adopts following combination: convolution-> activation-> normalization-> down-sampling.
3) cancel down-sampling layer: convolution-> activates; Or convolution-> activation-> normalization; Down-sampling essence is in order to increase robustness, has in passing the effect of the operand that reduces succeeding layer simultaneously; In a network, conventionally have which floor down-sampling, but not all " convolution-> activates " all to follow down-sampling below.
Full articulamentum:
1) can become 1 dimension by the intermediate representation after full articulamentum, be no longer 3 dimensions;
2) the full output connecting can be any;
3) once enter full connection, just cannot carry out convolution, down-sampling or normalization;
4) full connection below can connect active coating, or continues to connect full connection.
SoftMax layer:
After being connected on full articulamentum, effect is the probability connecting between real-valued the becoming [0,1] producing complete.
The last network structure using of the present invention is as shown in table 1.
Table 1 convolutional neural networks structure
| The number of plies | Type | Core size | Output size | Explain |
| 1 | Input layer | ? | 32x32x3 | ? |
| 2 | Convolutional layer | 5x5 | 32x32x32 | ? |
| 3 | Active coating | ? | 32x32x32 | ? |
| 4 | Down-sampling layer | 2x2 | 16x16x32 | f(x)＝max(0，x) |
| 5 | Normalization layer | ? | 16x16x32 | Use local normalization |
| 6 | Convolutional layer | 5x5 | 16x16x16 | ? |
| 7 | Active coating | ? | 16x16x16 | ? |
| 8 | Down-sampling layer | 2x2 | 8x8x16 | f(x)＝max(0，x) |
| 9 | Normalization layer | ? | 8x8x16 | Use local normalization |
| 10 | Full articulamentum | ? | 2 data | ? |
| 11 | SoftMax layer | ? | 2 data | ? |
Described fast fourier transform (Fast Fouier Transform), is called for short FFT, and it is the fast algorithm of discrete Fourier transformation, also can be used for calculating the inverse transformation of discrete Fourier transformation.Fast Fourier Transform (FFT) is widely used, as digital signal processing, calculate large multiplication of integers, solve partial differential equation etc.The basic thought of fft algorithm design, makes full use of periodicity and the symmetry of DFT exactly, reduces the calculated amount repeating; And the long sequence of N point is divided into several short sequences, and reduce each sequence length, can greatly reduce calculated amount.In practice, using maximum FFT is " base 2 " algorithm.So-called " base 2 ", makes the points N of DFT meet N=2M (M is natural number) exactly.FFT 2-base algorithm is divided into time domain extraction method (Decimation In Time) and frequency domain extraction method (Decimation In Frequency) two large classes.Fast Fourier Transform (FFT) (FFT) greatly reduces the operand in digital signal processing, its value has been to save the processing time of CPU, more more complicated digital signals are processed fast, for vast potential for future development has been opened up in the real-time processing that realizes information.
The invention provides the fuzzy detection method of a kind of combination Fast Fourier Transform (FFT) and convolutional neural networks, its data after Fourier transform size are dwindled are as the input data of convolutional neural networks (CNN).Data set for fuzzy detection model training includes clear-fuzzy two class data, carries out, after classification based training, obtaining the parameter of learning training with convolutional neural networks, and this parameter model is as fuzzy detection model.Wherein, input data for fuzzy detection model training study are not the data of tri-passages of original image RGB, and employing tri-passages of RGB data after Fast Fourier Transform (FFT) respectively, dwindling of view data size is to carry out after Fast Fourier Transform (FFT) at raw image data, what fuzzy detection model training study adopted is the GPU acceleration version of convolutional neural networks CNN, image to be detected need to do respectively Fast Fourier Transform (FFT) to tri-passages of RGB, and then dwindle the complex data size after conversion, complex data after dwindling by size builds new image to be detected, clear-the fuzzy discrimination that uses fuzzy detection model to carry out.
Using CNN, to carry out fuzzy detection model training very consuming time, original image is reduced to reduced size and trains and can reduce calculated amount.But it is the process of Gaussian transformation and down-sampling that original image is directly dwindled, and has lost high-frequency information in this process, picture can be mistaken for blurred picture in fuzzy testing process clearly originally.Therefore, the present invention uses Fourier transform, transfers raw image data to data in frequency domain space, makes the proud reservation of image low-and high-frequency information required in fuzzy testing process.It is a huge process of calculated amount that image is carried out to Fourier transform, due to the periodicity of Fourier transform, can adopt Fast Fourier Transform (FFT) (FFT) to accelerate this computation process, be specially adapted to large-sized image to carry out fuzzy detection fast, improve Detection accuracy.
Above-mentioned explanation illustrates and has described the preferred embodiments of the present invention, be to be understood that the present invention is not limited to disclosed form herein, should not regard the eliminating to other embodiment as, and can be used for various other combinations, amendment and environment, and can, in invention contemplated scope herein, change by technology or the knowledge of above-mentioned instruction or association area.And the change that those skilled in the art carry out and variation do not depart from the spirit and scope of the present invention, all should be in the protection domain of claims of the present invention.
Claims (2)
1. a method for quick for blurred picture, is characterized in that, comprises the following steps:
10. collect sample image, and tri-passages of RGB of each sample image are carried out respectively to Fast Fourier Transform (FFT), obtain the complex data after conversion;
Complex data described in 20. pairs is carried out size and is dwindled processing, and complex data after dwindling by size builds new images, obtains downscaled images;
30. input pictures using the downscaled images of described new structure as convolutional neural networks, carry out clear-blurred picture classification based training, obtain fuzzy detection model;
40. obtain image to be detected, and treat detected image and carry out the Fast Fourier Transform (FFT) of tri-passages of RGB, the complex data that conversion is obtained is carried out size and is dwindled processing, and complex data after dwindling by size builds new image to be detected, then adopt described fuzzy detection model to carry out the differentiation of clear-blurred picture to described new image to be detected.
2. the method for quick of a kind of blurred picture according to claim 1, it is characterized in that: in described step 40, adopt described fuzzy detection model to carry out the differentiation of clear-blurred picture to described new image to be detected, mainly that the new image block to be detected building after conversion is put into convolutional neural networks system, then calculating this new image to be detected according to described fuzzy detection model is the probability of picture rich in detail or blurred picture, and select image type that probability is larger as described image to be detected clear-vague category identifier.
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| CN104268888B (en) * | 2014-10-09 | 2017-11-03 | 厦门美图之家科技有限公司 | A kind of image blurring detection method |
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| CN104899834A (en) * | 2015-03-04 | 2015-09-09 | 苏州大学 | Blurred image recognition method and apparatus based on SIFT algorithm |
| CN105957086A (en) * | 2016-05-09 | 2016-09-21 | 西北工业大学 | Remote sensing image change detection method based on optimized neural network model |
| CN106372661A (en) * | 2016-08-30 | 2017-02-01 | 北京小米移动软件有限公司 | Method and device for constructing classification model |
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| CN107133948A (en) * | 2017-05-09 | 2017-09-05 | 电子科技大学 | Image blurring and noise evaluating method based on multitask convolutional neural networks |
| CN107133948B (en) * | 2017-05-09 | 2020-05-08 | 电子科技大学 | Image blurring and noise evaluation method based on multitask convolution neural network |
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| CN110648326A (en) * | 2019-09-29 | 2020-01-03 | 精硕科技（北京）股份有限公司 | Method and device for constructing image quality evaluation convolutional neural network |
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