CN110728635B - Contrast enhancement method for dark and weak target - Google Patents
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Abstract
The invention discloses a contrast enhancement method of a dark and weak target. Firstly, each frame of gray level image continuously output by an image sensor is obtained, local features of each pixel are extracted, and local feature vectors of each pixel are constructed. And (3) completing calculation of all pixels to obtain a local feature vector diagram of the image, and carrying out pooling operation on the local feature vector diagram. And carrying out local similarity calculation on the feature vector diagrams of the front frame and the rear frame of the sequence image, carrying out local feature probability accumulation according to the similarity, and outputting an enhanced image after each continuous processing of N frames. According to the method, the dark and weak targets are subjected to multidimensional expression, target probability dispersion and matching accumulation through local features, so that randomly distributed space-time noise is reduced, the contrast of the dark and weak targets in an image is improved, and the discovery capability of the dark and weak targets is remarkably improved.
Description
Technical field:
the invention belongs to the technical field of infrared image processing, and relates to a contrast enhancement method of a dark and weak target, which is particularly suitable for image preprocessing enhancement in the process of detecting a target with low signal to noise ratio of an infrared image.
The background technology is as follows:
because the infrared imaging system has the characteristics of long acting distance, high imaging precision, passive imaging and the like, the infrared imaging system is widely applied to detection and tracking of various targets. Low signal to noise ratio dim target detection has been a problem in the infrared image processing field. Because the infrared detection system generally has a longer acting distance, the energy of the target reaching the detection system is weak, and the target often occupies only one or a few pixels on the image plane, and the target detection system has no fixed geometric form or texture information and brings great difficulty to the target detection. Meanwhile, due to the existence of background clutter and detection noise of an infrared image, a target point is submerged in the image noise, and interference is brought to target detection.
At present, two main methods for detecting infrared small targets exist: (1) The image is preprocessed by filtering and the like, and target detection is performed after the background clutter noise is suppressed, so that the aim of suppressing false alarms is fulfilled; (2) Performing target detection after improving contrast by performing multi-frame energy accumulation; (3) And carrying out prediction association on the motion trail of the target of the image sequence so as to realize detection of a small target under low signal-to-noise ratio. (1) The main purpose of the method (3) is to reduce the false alarm rate, and the method does not have a good improvement effect on the detection of a dark and weak target; (2) Good effect on the targets with slower movement speed and poor effect on the dim targets with faster movement speed. It is therefore necessary to develop contrast enhancement methods for the detection of dim targets, improving the signal-to-noise ratio to facilitate the detection of dim targets.
The invention comprises the following steps:
in order to overcome the defects of the prior art, the invention provides a contrast enhancement method for a dark and weak target, which utilizes local feature extraction to carry out multidimensional expression on each pixel, realizes probability dispersion and matching accumulation of the target through feature pooling, reduces randomly distributed space-time noise, improves the contrast of the dark and weak target in an image, and improves the discovery capability of the dark and weak target.
The above object of the present invention is achieved by the following technical solutions:
1. a method for contrast enhancement of a dim target, characterized by: the method comprises the following steps:
(1) Designing a feature extractor and a feature scoring device according to the data or priori information of the dark and weak targets and the clutter background; the data of the dark and weak targets and the clutter background are images shot by a sensor with the same imaging wave band and close resolution; the shooting background type is consistent with the actual type; the prior information is a spatial distribution model of the target and the background and typical information which can characterize the target: intensity, size, energy concentration, shape, speed, background intensity of the target; the feature extractor can be designed manually by using priori knowledge, and can also be designed by using data in an unsupervised machine learning manner, wherein the machine learning method mainly comprises sparse coding, a support vector machine and a deep neural network; the feature scoring device can be designed manually by using priori knowledge, and can also be designed by using unsupervised machine learning through data, wherein the machine learning method mainly comprises sparse coding, a support vector machine and a deep neural network;
(2) Acquiring an output sequence image of an image sensor, wherein the image sensor is an area array staring type image sensor, the response spectrum is not limited, and the output frame frequency is not less than 30 frames per second;
(3) Extracting local features of each pixel in the image by using a feature extractor; the local feature of each pixel is that a W1 & W1 window neighborhood taking each pixel as a center is subjected to slice extraction, and the local feature in the slice is extracted, wherein W1 is the window width and is set manually;
(4) Pooling the local features to obtain a local feature map; the local feature pooling means that the feature of the pixel with the highest local score is selected in each W2-W2 window neighborhood to be used as the output of the neighborhood block, wherein W2 is the window width and is set manually;
(5) Carrying out neighborhood similarity matching on the local feature map of the current frame and the local feature accumulation map of the previous frame, and calculating probability accumulation for updating local features; the neighborhood similarity matching of the previous and the next frames means that the FAI of the characteristic accumulation graph of the previous frame is calculated n-1 Local feature map FI with current frame n The similarity degree of the local characteristics of the pixels in the neighborhood of the same position W3, wherein W3 is a matching window range, the matching window range is set by manpower, and the similarity degree is evaluated as follows:
where n is the current frame number, x 1 、y 1 To accumulate pixel positions of the map, x 2 、y 2 M is the number of frames accumulated currently and is the position of the local feature map of the current frame; performing feature accumulation on pixels most similar to the local features to complete a feature accumulation map FAI n Updating, wherein a, b is an accumulation coefficient, a+b=1;
FAI n ＝a·FAI n-1 +b·FI n
(6) After the local features of N frames of images are accumulated, feature scoring output is carried out, and image enhancement output is completed, wherein N is the accumulated frame number and can be set in advance; wherein enhancing the image refers to outputting the feature score for each pixel of the local feature map as an image response.
Compared with the prior art, the invention has the beneficial effects that
1. And the target is characterized by local features, and the time space noise is suppressed by the accumulation of similar pixel features of the sequence images, so that the contrast enhancement of the dark and weak target is realized.
2. The image matching operation amount is reduced through local pooling, the operation efficiency of image enhancement and the adaptability of a moving target are improved, the hardware implementation is convenient, and the real-time enhancement is realized.
Drawings
FIG. 1 is a block diagram of an implementation flow of the present invention;
FIG. 2 is an original image of the present invention;
FIG. 3 is an enhanced image of the present invention;
FIG. 4 is a graph showing the enhancement of the present invention for different signal-to-noise targets.
Detailed Description
The following detailed description of the technical solutions according to embodiments of the present invention will be given with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Manually selecting local features, and selecting local maximum values, local average values and local energy concentration;
the manual selection scoring device is a weighted accumulator, three characteristic values are weighted and accumulated, and weight coefficients are respectively [0.3,0.4,0.3];
image resolution 256×256, w1=9, w2=5, w3=3, a=0.8, b=0.2, n=8; the original image is shown in fig. 2, the result after enhancement according to the method flow is shown in fig. 3, and the contrast gain curve of the method is drawn by enhancing dark and weak targets with different signal to noise ratios as shown in fig. 4.
Claims (1)
1. A method for contrast enhancement of a dim target, comprising the steps of:
(1) Designing a feature extractor and a feature scoring device according to the data or priori information of the dark and weak targets and the clutter background; the data of the dark and weak targets and the clutter background are images shot by a sensor with the same imaging wave band and close resolution; the shooting background type is consistent with the actual type; the prior information is a spatial distribution model of the target and the background and typical information which can characterize the target: intensity, size, energy concentration, shape, speed, background intensity of the target; the feature extractor utilizes priori knowledge to carry out manual design or carries out unsupervised machine learning design through data, and the machine learning method comprises sparse coding, a support vector machine and a deep neural network; the feature scoring device utilizes priori knowledge to carry out manual design or carries out unsupervised machine learning design through data, and the machine learning method comprises sparse coding, a support vector machine and a deep neural network;
(2) Acquiring an output sequence image of an image sensor, wherein the image sensor is an area array staring type image sensor, the response spectrum is not limited, and the output frame frequency is not less than 30 frames per second;
(3) Extracting local features of each pixel in the image by using a feature extractor; the local feature of each pixel is that a W1 & W1 window neighborhood taking each pixel as a center is subjected to slice extraction, and the local feature in the slice is extracted, wherein W1 is the window width and is set manually;
(4) Pooling the local features to obtain a local feature map; the local feature pooling means that the feature of the pixel with the highest local score is selected in each W2-W2 window neighborhood to be used as the output of the neighborhood, wherein W2 is the window width and is set manually;
(5) Carrying out neighborhood similarity matching on the local feature map of the current frame and the local feature accumulation map of the previous frame, and calculating probability accumulation for updating local features; the neighborhood similarity matching of the previous and the next frames means that the FAI of the characteristic accumulation graph of the previous frame is calculated n-1 Local feature map FI with current frame n The similarity degree of the local characteristics of the pixels in the neighborhood of the same position W3, wherein W3 is a matching window range, the matching window range is set by manpower, and the similarity degree is evaluated as follows:
where n is the current frame number, x 1 、y 1 To accumulate pixel positions of the map, x 2 、y 2 M is the number of frames accumulated currently and is the position of the local feature map of the current frame; performing feature accumulation on pixels most similar to the local features to complete a feature accumulation map FAI n Updating, wherein a, b is an accumulation coefficient, a+b=1;
FAI n ＝a·FAI n-1 +b·FI n ；
(6) After the local features of N frames of images are accumulated, feature scoring output is carried out, and image enhancement output is completed, wherein N is the accumulated frame number and can be set in advance; wherein image enhancement refers to outputting the feature score of each pixel of the local feature map as an image response.
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