CN111724333B - Infrared image and visible light image fusion method based on early visual information processing - Google Patents
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Abstract
The invention discloses an infrared image and visible light image fusion method based on early visual information processing, which comprises the following steps: s1, dynamic receptive field treatment of On central neurons and Off central neurons; s2, obtaining a fusion image A and a fusion image B: the On central type neuron response of the visible light image, the Off central type neuron response of the infrared image and the original visible light image are fused to obtain an image A; the On central type neuron response of the infrared image, the Off central type neuron response of the visible light image and three components of the original infrared image are fused to obtain an image B: and S3, adding the fused images A and B to obtain a final fusion result. The invention can effectively combine the remarkable target information in the infrared image and the background information in the visible light image, and provides more effective characteristics for the computer vision task under night vision conditions such as subsequent high-value target detection and recognition.
Description
Technical Field
The invention belongs to the technical field of computer vision and image processing, relates to fusion of an infrared image and a visible light image, and particularly relates to an infrared image and visible light image fusion method based on early vision information processing.
Background
The fusion of infrared images and visible light images is an important image enhancement technology, and features of different wave bands are fused together to obtain an image with more abundant information. The visible image always shows the background in very detail, while the infrared image clearly shows a target that may be camouflaged for visual detection and identification. The fusion of the visible and infrared images may display more information than a single image. The problem of image fusion has evolved over several decades under different schemes, with representative technical routes including methods based on multi-scale feature decomposition, methods based on sparse representation, methods based on salient feature extraction, and other advanced fusion methods. In recent years, with the rise of deep learning, there are methods for applying a neural network to image fusion, and more reliable results are obtained in most cases.
For most fusion methods, most of them are inspired by non-biological visual mechanisms. The Waxman et al then simulate the color antagonism and spatial antagonism information processing mechanism of the biological vision system to fuse the two gray source images into one color image. Reference is made to: the image fused by the method of Waxman AM, gove A N, fay D A, et al color light vision: opponent processing in the fusion of visible and IR imagery [ J ]. Neurol Networks,1997,10 (1): 1-6., however, the image fused by the method of Waxman et al loses information of a part of the source image and is not natural enough in image color appearance, and it is difficult to give a satisfactory result in visual effect.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide the infrared image and visible light image fusion method based on early visual information processing, which can effectively fuse the obvious target information in the infrared image and the background information in the visible light image to obtain a fusion image with more abundant information and provide more effective characteristics for the computer visual tasks under night vision conditions such as follow-up high-value target detection and recognition.
The aim of the invention is realized by the following technical scheme: the method for fusing the infrared image and the visible light image based on early visual information processing comprises the following steps:
dynamic receptive field treatment of S1, on-center neurons and Off-center neurons: respectively carrying out convolution filtering processing on an input infrared image and a visible light image by using a dynamic receptive field model;
s2, obtaining a fusion image A and a fusion image B: fusing the On central type neuron response of the visible light image, the Off central type neuron response of the infrared image and the original visible light image according to the contrast level of the visible light image to obtain a fused image A; fusing the On central type neuron response of the infrared image, the Off central type neuron response of the visible light image and three components of the original infrared image according to the contrast level of the infrared image to obtain a fused image B:
and S3, adding the fused images A and B to obtain a final fusion result of the infrared image and the visible light image.
Further, the specific implementation method of the step S1 is as follows: the method comprises the steps of respectively carrying out convolution filtering processing on an input infrared image and a visible light image by using a dynamic receptive field model, wherein the specific method comprises the following steps:
wherein ,
and />
Representing the response of the visible light image after dynamic receptive field treatment of On-center neurons and Off-center neurons, respectively,/>
and />
The responses of the infrared image after the dynamic receptive field treatment of the On center type neuron and the dynamic receptive field treatment of the Off center type neuron are represented respectively, the superscript symbol V and the IR represent the visible light image and the infrared image respectively, and x and y represent the coordinates of the pixels; CRF (x, y) and SRF (x, y) represent the response of the dynamic receptive field center receptive field and the response of the peripheral receptive field, b (x, y) represents the weight of the peripheral receptive field, and symbol max represents that the response result after the dynamic receptive field treatment of the On-center type neurons and the Off-center type neurons takes a value of greater than or equal to 0;
the response CRF (x, y) of the central receptive field is calculated from the following formula:
CRF(x,y)＝I(x,y)*DRF(x,y；d) (5)
in equation (5), I (x, y) represents an input visible or infrared image, and DRF (x, y; d) represents a dynamic gaussian filter:
d in the formula (6) represents the dimension of the dynamic Gaussian filter, and the value range of d is { sigma, lambda sigma }; the parameter sigma represents the standard deviation of the Gaussian function, and the value range is all real numbers of [0.5, + ]; λ represents the maximum dimension that can be achieved by controlling the dynamic gaussian filter, and the range of values is all real numbers of [1, + -infinity);
the dimension d of the dynamic gaussian filter is related to the local contrast of the image:
d∝ΔI -1 (x,y；σ) (7)
equation (7) shows that the dimension d of the dynamic gaussian filter is inversely proportional to the local contrast Δi (x, y; σ) of the image;
the response SRF (x, y) of the peripheral receptive field is specifically calculated from the following formula:
SRF(x,y)＝I(x,y)*DRF(x,y；3σ) (8)
the weight b (x, y) of the peripheral receptive field is related to the local contrast of the image:
b(x,y)∝ΔI -1 (x,y；3σ) (9)
equation (9) shows that the weight b (x, y) of the peripheral receptive field is inversely proportional to the local contrast Δi (x, y;3σ) of the image.
Further, the specific calculation method of the local contrasts Δi (x, y; σ) and Δi (x, y;3σ) is to select, in the image I (x, y), local areas with area sizes of σ and 3σ, respectively, around each pixel, and take the local standard deviation calculated by taking the pixel as the center as the local contrast of the pixel.
Further, the specific implementation method of the step S2 is as follows: simulating the cortical and subcortical visual information fusion mechanism of the biological visual system, and responding the On center type neuron of the visible light image
Off-center neuronal response of infrared image +.>
Original visible light image I V And (x, y) fusing the three components according to the contrast level of the visible light image to obtain a fused image A:
similarly, the On-center neuronal response of the infrared image
Off-center neuronal response of visible light image +.>
Original infrared image I IR (x, y) three components,fusing according to the contrast level of the infrared image to obtain a fused image B:
wherein the weight parameter beta is calculated according to the following formula:
Further, the parameters in the step S2
The value range of (2) is 0.5, + -infinity) of any real number within.
Further, the specific implementation method for performing image fusion in the step S3 is as follows: images a and B are added according to the following formula: (A (x, y) +B (x, y))/2.
The beneficial effects of the invention are as follows: the method can realize the online real-time fusion and parameter self-adaption of the infrared image and the visible light image, and can effectively fuse the obvious target information in the infrared image and the background information in the visible light image to obtain a fusion image with richer information. As a multisource information fusion method, the invention can be embedded in equipment such as a night vision device and the like, is applied to effectively detecting and acquiring high-value targets in a night vision environment, and provides more effective characteristics for computer vision tasks under the night vision conditions such as subsequent high-value target detection and identification.
Drawings
FIG. 1 is a flow chart of an image fusion method of the present invention;
FIG. 2 is an original visible image and an infrared image used in the present embodiment;
FIG. 3 is a graph showing the results of the present embodiment after processing the original visible and infrared images using a dynamic receptive field;
FIG. 4 is a fused image A and a fused image B;
fig. 5 is a fusion image finally obtained in this example.
Detailed Description
The technical scheme of the invention is further described below with reference to the accompanying drawings.
As shown in fig. 1, the method for fusing an infrared image and a visible light image based on early visual information processing of the present invention comprises the following steps:
dynamic receptive field treatment of S1, on-center neurons and Off-center neurons: respectively carrying out convolution filtering processing on an input infrared image and a visible light image by using a dynamic receptive field model; the specific implementation method comprises the following steps: the method comprises the steps of respectively carrying out convolution filtering processing on an input infrared image and a visible light image by using a dynamic receptive field model, wherein the specific method comprises the following steps:
wherein ,
and />
Representing the response of visible light images after dynamic receptive field treatment of On-center neurons and dynamic receptive field treatment of Off-center neurons, respectively,/->
and />
The responses of the infrared image after the dynamic receptive field treatment of the On center type neuron and the dynamic receptive field treatment of the Off center type neuron are represented respectively, the superscript symbol V and the IR represent the visible light image and the infrared image respectively, and x and y represent the coordinates of the pixels; CRF (x, y) and SRF (x, y) represent the response of the dynamic receptive field center receptive field and the response of the peripheral receptive field, b (x, y) represents the weight of the peripheral receptive field, and symbol max represents that the response result after the dynamic receptive field treatment of the On-center type neurons and the Off-center type neurons takes a value of greater than or equal to 0;
the response CRF (x, y) of the central receptive field is calculated from the following formula:
CRF(x,y)＝I(x,y)*DRF(x,y；d) (5)
in equation (5), I (x, y) represents an input visible or infrared image, and DRF (x, y; d) represents a dynamic gaussian filter:
d in the formula (6) represents the dimension of the dynamic Gaussian filter, and the value range of d is { sigma, lambda sigma }; the parameter sigma represents the standard deviation of the Gaussian function, and the value range is all real numbers of [0.5, + ]; λ represents the maximum dimension that can be achieved by controlling the dynamic gaussian filter, and the range of values is all real numbers of [1, + -infinity);
the dimension d of the dynamic gaussian filter is related to the local contrast of the image:
d∝ΔI -1 (x,y；σ) (7)
equation (7) shows that the dimension d of the dynamic gaussian filter is inversely proportional to the local contrast Δi (x, y; σ) of the image; examples of which are as follows: the local contrast is normalized to the range of [0,1] and divided into N layers, and the value range of N is all integers of [2,64 ]. Taking n=3 as an example, the normalized local contrast is divided into [0,13], [13,23], [23,1]3 levels. Since d e { σ, λσ }, here taking λ=3 as an example, the value of d is d=3σ in the local contrast [0,13] range, d=2σ in the local contrast [13,23] range, d=σ in the local contrast [23,1] range, i.e. the value of d is inversely proportional to the local contrast of the image, the smaller the local contrast, the larger the value of d, and vice versa.
The response SRF (x, y) of the peripheral receptive field is specifically calculated from the following formula:
SRF(x,y)＝I(x,y)*DRF(x,y；3σ) (8)
the weight b (x, y) of the peripheral receptive field is related to the local contrast of the image:
b(x,y)∝ΔI -1 (x,y；3σ) (9)
equation (9) shows that the weight b (x, y) of the peripheral receptive field is inversely proportional to the local contrast Δi (x, y;3σ) of the image, and the value range of b (x, y) is [ -1,0]; examples of which are as follows: the local contrast is normalized to the range of [0,1] and divided into N layers, and the value range of N is all integers of [2,64 ]. Taking n=3 as an example, the normalized local contrast is divided into three layers of [0,1/3], [1/3,2/3], [2/3,1 ]. Since b (x, y) ∈ [ -1,0], the value of b (x, y) is b (x, y) = -1/3 in the local contrast [0,13], b (x, y) = -2/3 in the local contrast [1/3,2/3], and b (x, y) = -1 in the local contrast [2/3,1], that is, the value of bx (, y) is inversely proportional to the local contrast of the image, the larger the local contrast, the smaller the value of b (x, y), and vice versa.
The specific calculation method of the local contrasts delta I (x, y; sigma) and delta I (x, y;3 sigma) is that local areas with the area sizes sigma and 3 sigma are selected around each pixel in the image I (x, y), and the local standard deviation calculated by taking the pixel as the center is used as the local contrast of the pixel.
S2, obtaining a fusion image A and a fusion image B: fusing the On central type neuron response of the visible light image, the Off central type neuron response of the infrared image and the original visible light image according to the contrast level of the visible light image to obtain a fused image A; fusing the On central type neuron response of the infrared image, the Off central type neuron response of the visible light image and three components of the original infrared image according to the contrast level of the infrared image to obtain a fused image B:
the specific implementation method comprises the following steps: simulating the cortical and subcortical visual information fusion mechanism of the biological visual system, and responding the On center type neuron of the visible light image
Off-center neuronal response of infrared image +.>
Original visible light image I V And (x, y) fusing the three components according to the contrast level of the visible light image to obtain a fused image A:
similarly, the On-center neuronal response of the infrared image
Off-center neuronal response of visible light image +.>
Original infrared image I IR And (x, y) fusing the three components according to the contrast level of the infrared image to obtain a fused image B:
wherein the weight parameter beta is calculated according to the following formula:
S3, adding the fused images A and B to obtain a final fusion result of the infrared image and the visible light image; the specific implementation method comprises the following steps: images a and B are added according to the following formula: (A (x, y) +B (x, y))/2.
The present embodiment uses an infrared image and a visible light image with an image size of 270 x 360 (as shown in fig. 2, the image is derived from a TNO image dataset, the image is named "image", (a) and (b) are respectively a visible light image and an infrared image), the parameters in the present embodiment are set as follows σ=0.5, λ=3,
n=3. Results after the dynamic receptive field processing of a pair of visible light images and infrared images named "Vegetation" using On-center type neurons and Off-center type neurons through step S1 (formulas (1) to (4)): />
As shown in the images (a), (B), (c) and (d) of FIG. 3, respectively, and then images A and B are calculated according to formulas (10) to (11), resulting in results A (x, y) (as shown in FIG. 4 (a)) andb (x, y) (shown in fig. 4 (B)), and the two images are added according to the method of S3, as shown in fig. 5.
The simple examples above are mainly described and shown by taking the whole image as an example, and the actual calculation is realized by performing corresponding operations such as local convolution filtering, addition, subtraction, multiplication and division on all pixels of the whole image, and the actual numerical values and results are also experimental results of direct values in program operation. By way of such a simple example, the overall process of an infrared image and visible image fusion method based on an early visual information processing mechanism is illustrated.
Those of ordinary skill in the art will recognize that the embodiments described herein are for the purpose of aiding the reader in understanding the principles of the present invention and should be understood that the scope of the invention is not limited to such specific statements and embodiments. Those of ordinary skill in the art can make various other specific modifications and combinations from the teachings of the present disclosure without departing from the spirit thereof, and such modifications and combinations remain within the scope of the present disclosure.
Claims (5)
1. The method for fusing the infrared image and the visible light image based on early visual information processing is characterized by comprising the following steps of:
dynamic receptive field treatment of S1, on-center neurons and Off-center neurons: respectively carrying out convolution filtering processing on an input infrared image and a visible light image by using a dynamic receptive field model; the specific implementation method comprises the following steps: the method comprises the steps of respectively carrying out convolution filtering processing on an input infrared image and a visible light image by using a dynamic receptive field model, wherein the specific method comprises the following steps:
wherein ,
and />
Representing the response of visible light images after dynamic receptive field treatment of On-center neurons and dynamic receptive field treatment of Off-center neurons, respectively,/->
and />
The responses of the infrared image after the dynamic receptive field treatment of the On center type neuron and the dynamic receptive field treatment of the Off center type neuron are represented respectively, the superscript symbol V and the IR represent the visible light image and the infrared image respectively, and x and y represent the coordinates of the pixels; CRF (x, y) and SRF (x, y) represent the response of the dynamic receptive field center receptive field and the response of the peripheral receptive field, b (x, y) represents the weight of the peripheral receptive field, and symbol max represents that the response result after the dynamic receptive field treatment of the On-center type neurons and the Off-center type neurons takes a value of greater than or equal to 0;
the response CRF (x, y) of the central receptive field is calculated from the following formula:
CRF(x,y)＝I(x,y)*DRF(x,y；d) (5)
in equation (5), I (x, y) represents an input visible or infrared image, and DRF (x, y; d) represents a dynamic gaussian filter:
d in the formula (6) represents the dimension of the dynamic Gaussian filter, and the value range of d is { sigma, lambda sigma }; the parameter sigma represents the standard deviation of the Gaussian function, and the value range is all real numbers of [0.5, + ]; λ represents the maximum dimension that can be achieved by controlling the dynamic gaussian filter, and the range of values is all real numbers of [1, + -infinity);
the dimension d of the dynamic gaussian filter is related to the local contrast of the image:
d∝ΔI -1 (x, y; sigma) (7) equation (7) shows that the dimension d of the dynamic gaussian filter is inversely proportional to the local contrast Δi (x, y; sigma) of the image;
the response SRF (x, y) of the peripheral receptive field is specifically calculated from the following formula:
SRF(x,y)＝I(x,y)*DRF(x,y；3σ) (8)
the weight b (x, y) of the peripheral receptive field is related to the local contrast of the image:
b(x,y)∝ΔI -1 (x, y;3σ) (9) equation (9) represents that the weight b (x, y) of the peripheral receptive field is inversely proportional to the local contrast Δi (x, y;3σ) of the image;
s2, obtaining a fusion image A and a fusion image B: fusing the On central type neuron response of the visible light image, the Off central type neuron response of the infrared image and the original visible light image according to the contrast level of the visible light image to obtain a fused image A; fusing the On central type neuron response of the infrared image, the Off central type neuron response of the visible light image and three components of the original infrared image according to the contrast level of the infrared image to obtain a fused image B:
and S3, adding the fused images A and B to obtain a final fusion result of the infrared image and the visible light image.
2. The method for fusing an infrared image and a visible light image based on early visual information processing according to claim 1, wherein the specific calculation method of the local contrasts Δi (x, y; σ) and Δi (x, y;3σ) is to select, in the image I (x, y), local areas having the area sizes of σ and 3σ, respectively, around each pixel, and the local standard deviation calculated with the pixel as the center is taken as the local contrast of the pixel.
3. The method for fusing an infrared image and a visible light image based on early visual information processing according to claim 1, wherein the specific implementation method of step S2 is as follows: simulating the cortical and subcortical visual information fusion mechanism of the biological visual system, and responding the On center type neuron of the visible light image
Off-center neuronal response of infrared image +.>
Original visible light image I V And (x, y) fusing the three components according to the contrast level of the visible light image to obtain a fused image A:
similarly, the On-center neuronal response of the infrared image
Off-center neuronal response of visible light image +.>
Original infrared image I IR And (x, y) fusing the three components according to the contrast level of the infrared image to obtain a fused image B:
wherein the weight parameter beta is calculated according to the following formula:
4. The method for fusing infrared image and visible light image based on early stage visual information processing as claimed in claim 3, wherein said parameters in step S2 are as follows
The value range of (2) is 0.5, + -infinity) of any real number within.
5. The method for fusing infrared image and visible light image based on early visual information processing according to claim 3, wherein the specific implementation method for fusing the image in step S3 is as follows: images a and B are added according to the following formula: (A (x, y) +B (x, y))/2.
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