Patent Description:
Image fusion refers to that image data about the same target is collected by multi-source channels, favorable information in each channel is extracted to the maximum extent through image processing and computer technologies, and a high-quality image is finally synthesized. In this way, the utilization rate of image information is enhanced, the accuracy and reliability of computer interpretation are improved, and the spatial resolution and spectral resolution of the original image are improved, which are conducive to monitoring.

Most of image fusion techniques in the related art are simple fusion of source images, and the inconsistency of the source images in the luminance or the structure is rarely considered, resulting in color distortion, edge blurring or obvious noise of the fused image.

<CIT> discloses a method for producing a full-color smoothed image by combining NIR and visible images decomposed layers. It involves globally shifting the mean value of the NIR image towards that of the visible and then stretching the histogram of the shifted NIR image giving a black and a white point.

Chinese patent "<CIT>" discloses a car anti-halation method that enhances IHS-Curvelet transformation to fuse visible light and infrared images to solve the problem of car halo at night. The technical solution includes: collecting visible light and infrared images of the road conditions in front of the car at night; filtering and denoising the two images; using the infrared image as a reference image to register the visible light image; converting the visible light image to the IHS color space to obtain the brightness I, there are three components of hue H and saturation S; the brightness component I and the enhanced infrared image are decomposed by curvelet to obtain their respective high and low frequency coefficients; the designed weight automatic adjustment strategy is used to fuse the low-frequency coefficients; the modulus-larger strategy is used to fuse the high-frequency coefficients; curvelet reconstruction is performed on the fused high and low frequency coefficients to obtain a new brightness signal component I'; I' is combined with the original hue H and saturation S to perform IHS inverse transformation to obtain the final fused image. The invention removes high-brightness halo information and effectively improves image clarity.

US patent <CIT> discloses a method including: receiving a reference image (e.g., a visible light image) of a scene comprising image pixels identified by pixel coordinates; receiving a lower-resolution target image (e.g., an infrared image) of the scene; resizing the target image to a larger size; determining an adaptive-shape neighborhood for each pixel coordinate, wherein the adaptive-shape neighborhood extends from the each pixel coordinate such that those reference image pixels that are within the shape-adaptive neighborhood meet a regularity condition; determining, for each adaptive-shape neighborhood, a local estimate based on those target image pixels that are within the adaptive-shape neighborhood; and aggregating the local estimates associated with the adaptive-shape neighborhoods to provide a global estimate that corresponds to the target image with an improved resolution. A system configured to perform such a method is also disclosed.

<NPL>) discloses a high-quality image of the images captured in the low lighting conditions. This paper devises a quick method of image fusion which uses FFT for detail layer fusion and BM3D denoising method.

US patent <CIT> discloses a system and a method for image fusion. The method may comprise: obtaining a visible light image and an infrared image relating to a same scene; performing a first decomposition to the visible light image to obtain a first high-frequency component of the visible light image and a first low-frequency component of the visible light image; performing a first decomposition to the infrared image to obtain a first high-frequency component of the infrared image and a first low-frequency component of the infrared image; fusing the first high-frequency component of the visible light image and the first high-frequency component of the infrared image based on a first algorithm to generate a first fused high-frequency component; and performing reconstruction based on the first fused high-frequency component, the first low-frequency component of the visible light image, and the first low-frequency component of the infrared image to generate a fused image.

<NPL>)_discloses a spatial-domain filtering techniques dictate lowlight visible and IR image-fusion performance.

Further aspects of the present invention will become apparent from the following description with reference to the attached drawings.

The present application is described hereinafter in conjunction with drawings and embodiments. The embodiments described herein are intended to explain and not to limit the present application. In addition, for ease of description, only part, not all, of structures related to the present application are illustrated in the drawings.

Before the exemplary embodiments are discussed, it is to be noted that some of the exemplary embodiments are described as processing or methods depicted in flowcharts. Although the flowcharts describe multiple steps as sequentially processed, many of the steps may be implemented concurrently, coincidently or simultaneously. Additionally, the sequence of the multiple steps may be rearranged. The processing may be terminated when operations are completed, but the processing may further have additional steps which are not included in the drawings. The processing may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc..

<FIG> is a flowchart of an image fusion method according to an embodiment of the present application. The embodiment may be applied to the case of fusing a visible light image and an infrared image. The method may be executed by an image fusion apparatus provided by an embodiment of the present application. The apparatus may be implemented by means of software and/or hardware and may be integrated in an electronic device such as an intelligent terminal.

As shown in <FIG>, the image fusion method includes steps described below.

In S110, a visible light image and an infrared image which are to-be-fused are acquired.

In an embodiment, the visible light image and the infrared image which are to-be-fused may be images acquired for the same target. For example, the visible light image and the infrared image of the target are acquired by activating a visible light camera and an infrared camera simultaneously and respectively. The infrared image and the visible light image may be acquired by supplementing the target with light. For example, in the case of acquiring the infrared image, an infrared illuminator is adopted to supplement the target with light. The visible light image may also be an image obtained by supplementary lighting.

In S120, luminance and chrominance separation is performed on the visible light image to extract a luminance component and a chrominance component.

In an embodiment, the luminance and chrominance separation mainly refers to separating the luminance component and the chrominance component of the visible light image. For example, the original format of the visible light image is YCrCb (YUV), and a Y component of the YUV image may be extracted as the luminance component of the visible light image and a UV component as the chrominance component of the visible light image. Alternatively, the original format of the visible light image is Hue, Saturation, Value (HSV), and a V component of the HSV image may be extracted as the luminance component of the visible light image and a HS component as the chrominance component of the visible light image. In an embodiment, if the original format of the visible light image is Red, Green, Blue (RGB), the RGB image may be converted to an image of the YUV, HSV or other custom color spaces for the separation of the luminance component and the chrominance component.

In S130, luminance fusion is performed on the luminance component of the visible light image and the infrared image to obtain a luminance fusion result.

In an embodiment, after the luminance and chrominance separation is performed on the visible light image, the luminance fusion is performed on the luminance component of the visible light image and the infrared image to obtain the luminance fusion result. In this way, the fused luminance component not only has the relatively high signal-to-noise ratio and contrast, but also keeps more edge details.

In an embodiment, the step in which the luminance fusion is performed on the luminance component of the visible light image and the infrared image to obtain the luminance fusion result includes steps described below. The infrared image is corrected according to the luminance component of the visible light image to obtain a corrected infrared image. Image layer decomposition is performed on the luminance component of the visible light image and the corrected infrared image, respectively, and corresponding fusion is performed on multiple layers of the luminance component of the visible light image obtained after the image layer decomposition and on multiple layers of the corrected infrared image obtained after the image layer decomposition. Results of multiple-layer corresponding fusion are superimposed to obtain the luminance fusion result.

In an embodiment, the infrared image is corrected according to the luminance component of the visible light image to obtain the corrected infrared image. The luminance information of the infrared image is corrected mainly according to the luminance information of the visible light image to eliminate the inconsistency between the visible light image and the infrared image in the luminance and/or the structure, thereby avoiding problems of color distortion, detail loss and pseudo-edge caused by direct fusion. In an embodiment, several correction manners described below are included.

The first manner only considers the inconsistency in the luminance. For example, in a low-illuminance scene at night, the luminance of a visible light image is relatively low, while the luminance of an infrared image is relatively high due to infrared supplementary lighting, but luminance overexposure exits in areas such as the number plate. Therefore, direct fusion may lead to color distortion and other problems, and luminance correction for the infrared image is considered. In an embodiment, a global mapping manner may be used to perform luminance correction on the infrared image. For example, a histogram matching method may be adopted, and the luminance histogram of the visible light image is taken as a matching histogram to correct the infrared luminance; and the average value of the luminance of the visible light image may also be counted, and then linear mapping is performed on the infrared luminance. In addition to the global mapping correction, the infrared luminance may also be locally corrected according to information such as the local luminance or contrast of the luminance component of the visible light.

The second manner only considers the inconsistency in the structure. In addition to the inconsistency in the luminance, the inconsistency in the structure also exists between the visible light image and the infrared image due to the difference in reflection characteristics. For example, the information of the number plate in the infrared image is lost. Correction in the structure of the infrared luminance may avoid detail loss of this region due to fusion. Here, a joint filtering algorithm, such as guided filtering, weighted least squares (WLS) filtering or joint bilateral filtering, may be used to perform correction, a filtering operation is performed on the infrared image by taking edge information of the visible light image as reference, and the filtered infrared luminance image has the same edge details as the visible light image. Non-filtering methods such as soft matting may also be used to eliminate the inconsistency in the edge structure.

In the embodiment, the infrared luminance, the infrared luminance image and the infrared image have the same meaning.

It can be seen that the above two manners may be respectively used to correct the infrared image, or the above two manners may be simultaneously used to correct the infrared image, so as to achieve the fusion effect of restraining the pseudo-edge structure, real color and complete details. The above is the process of infrared image correction. After the correction is completed, the image layer decomposition is performed on the luminance component of the visible light image and the corrected infrared image, respectively, and the corresponding fusion is performed on the multiple layers of the luminance component of the visible light image obtained after the image layer decomposition and on the multiple layers of the corrected infrared image obtained after the image layer decomposition. The results of the multiple-layer corresponding fusion are superimposed to obtain the luminance fusion result. Through the correction of the infrared image, the color distortion after direct fusion caused by the overexposure of the infrared image and the structure difference can be avoided.

Based on the above technical solution, optionally, the step in which the infrared image is corrected according to the luminance component of the visible light image to obtain the corrected infrared image includes steps described below. A position of a reference pixel of the luminance component of the visible light image is determined according to a position of each pixel in the infrared image. A luminance correction result of the each pixel is determined according to a preset-range neighborhood block with the positon of the reference pixel as a center and a preset-range neighborhood block with the position of the each pixel as a center. All pixels of the infrared image are traversed to obtain the corrected infrared image.

Based on the above technical solution, optionally, the step in which the luminance correction result of the each pixel is determined according to the preset-range neighborhood block with the positon of the reference pixel as the center and the preset-range neighborhood block with the position of the each pixel as the center includes that the luminance correction result of the each pixel is determined by adopting the following formula: <MAT>.

Yir'(i) represents the luminance correction result of the each pixel, Yvis(i) represents a luminance value of the reference pixel, and αi(<NUM>) and αi(<NUM>) represent a first numerical value and a second numerical value of a matrix αi.

λ represents a preset regularization parameter, Wi represents a preset weight matrix, and Qi represents a matrix formed by luminance values of multiple pixels within the preset-range neighborhood block with the position of the each pixel as the center and a numerical value <NUM>; <MAT> represents a transposed matrix of Qi; pi represents a matrix formed by luminance values of pixels within the preset-range neighborhood block with the position of the reference pixel as the center; I represents an identity matrix; <MAT> represents a local contrast factor formed by a ratio of a luminance value of the each pixel to an average value of the luminance values of the multiple pixels within the preset-range neighborhood block with the position of the each pixel as the center; and R<NUM>×<NUM> represents a linear space formed by all <NUM>×<NUM> matrices over a real number field R.

In an embodiment, the third manner for infrared image correction may be used for illustration.

The third manner considers the inconsistency in the luminance and the structure simultaneously. An embodiment is given here, and implementation steps are described below.

For any pixel i in the infrared luminance image, the conversion formula is as follows: <MAT>.

pi = RiYvis, Ri ∈ Rm2xN, pi ∈ Rm2x1, pi represents a vector composed of luminance values of pixels in the neighborhood block with the pixel i as the center in the visible light luminance image Yvis, Ri represents a linear transformation, and N represents the dimension of Yvis. The range of the neighborhood block is m*m, and the value of m may be <NUM>, <NUM>, etc., and the range of the neighborhood block may also be a larger range with the pixel i as the center. <MAT>, Qi ∈ Rm2x2 , Qi represents a matrix composed of luminance values of pixels in the neighborhood block with the pixel i as the center in an original infrared luminance image <MAT> and column vectors with all elements of <NUM>.

Wi ∈ R<NUM>×m<NUM>, and Wi represents a preset weight matrix. The weight is determined by the distance between a pixel in a neighborhood block and the pixel i, and the larger the distance, the smaller the weight. <MAT>, and <MAT> represents a vector composed of a local contrast factor and <NUM>.

λ represents a preset regularization parameter. The smaller the value, the closer the corrected infrared luminance image to the visible light image, and the lower the degree of inconsistency. The larger the value of λ, the closer the corrected infrared luminance image to the original infrared luminance image, and the higher the signal-to-noise ratio and the contrast. The value of λ may be adjusted according to real scenes. The above optimal formula has an analytical solution in the following form: <MAT>.

A matrix αi of two rows and one column can be obtained, and the luminance of the pixel i of the corrected infrared image Yir is calculated as below: <MAT>.

αi(<NUM>) represents the numerical value of the first row of the matrix αi, αi(<NUM>) represents the numerical value of the second row of the matrix αi, Yir(i) represents the luminance of the pixel i of the corrected infrared image Yir, and Yvis(i) represents the luminance of the pixel i of the visible light image Yvis.

All pixels of the whole infrared image are traversed, the above calculation steps are repeated, and then the corrected infrared image can be obtained.

In the embodiment, optionally, the step in which the image layer decomposition is performed on the luminance component of the visible light image and the corrected infrared image, respectively, and the corresponding fusion is performed on the multiple layers of the luminance component of the visible light image obtained after the image layer decomposition and on the multiple layers of the corrected infrared image obtained after the image layer decomposition includes steps described below. The luminance component of the visible light image is layered into a visible light luminance base layer and a visible light luminance detail layer, and the corrected infrared image is layered into an infrared image base layer and an infrared image detail layer. The visible light luminance base layer and the infrared image base layer are fused, and the visible light luminance detail layer and the infrared image detail layer are fused. In an embodiment, two layers or more layers may be obtained by image layer decomposition, and each layer is fused. Here, that a base layer and a detail layer being obtained by image layer decomposition is taken for illustration. In the embodiment, the base layer and the detail layer of the visible light luminance image and the base layer and the detail layer of the corrected infrared luminance image are mainly obtained by image layer decomposition. For example, multiscale decomposition methods, such as a wavelet transform, a Gaussian pyramid, a Laplacian pyramid and the like, may be used, and filtering algorithms may also be used to achieve the image layer decomposition of the luminance. A linear filtering algorithm, such as mean filtering, Gaussian filtering and the like, may be used. This filtering method has the advantages of simple principles, low computation complexity and excellent performance, and can quickly achieve the smooth of the luminance image. The non-linear filtering algorithm, such as median filtering, non-local mean filtering and bilateral filtering and other edge preserving filtering algorithms, may also be used. This filtering method can protect the edge information of the image while removing small noise or texture details, but has relatively high complexity. The visible light luminance image Yvis being layered by mean filtering is taken as an example, and implementation steps are described below.

Mean filtering is performed on the visible light luminance image Yvis: <MAT>.

w represents a mean filtering template, Ωi represents a mean filtering window with the pixel i as the center, * represents a convolution operation, and Yvisbase(i) represents the luminance of the pixel i of the visible light luminance base layer Yvis_base.

At this time, the visible light luminance detail layer may be obtained through the following formula: <MAT>.

Yvis_det(i) represents the luminance of the pixel i of the visible light luminance detail layer Yvis_det, and Yvis(i) represents the luminance of the pixel i of the visible light image Yvis. Correspondingly, a luminance decomposition operation may also be performed on the corrected infrared image by the above method.

Through this decomposition manner, each layer is fused, thus the fusion effect of the image can be improved, and a more accurate image can be obtained.

In an embodiment, the step in which the visible light luminance base layer and the infrared image base layer are fused includes steps described below. A region saliency matrix of the visible light luminance base layer and a region saliency matrix of the infrared image base layer are determined through high-pass filtering, and a first weight <MAT> of the visible light luminance base layer and a first weight <MAT> of the infrared image base layer are determined according to the region saliency matrices. A second weight <MAT> of the visible light luminance base layer and a second weight <MAT> of the infrared image base layer are determined according to a preset optimal luminance value. A fusion weight of the visible light luminance base layer is determined according to the first weight <MAT> of the visible light luminance base layer and the second weight <MAT> of the visible light luminance base layer; and a fusion weight of the infrared image base layer is determined according to the first weight <MAT> of the infrared image base layer and the second weight <MAT> of the infrared image base layer. The visible light luminance base layer and the infrared image base layer are fused according to the fusion weight of the visible light luminance base layer and the fusion weight of the infrared image base layer. In an embodiment, the visible light luminance base layer and the infrared image base layer are fused mainly based on objective criteria such as region saliency and subjective criteria such as a better visual effect. Steps are described below.

First, the calculation of the first weight of the base layer based on the region saliency.

High-pass filtering is performed on Yvis_base and Yir_base by using the Laplace operator <MAT> (other high-pass filtering methods may also be used and are not limited herein) to obtain the saliency matrices Cvis and Cir, and the first weight of Yvis_base and the first weight of Yir_base are respectively as below.

The first weight <MAT> of the visible light luminance base layer is determined as that <MAT>.

The first weight <MAT> of the infrared image base layer is determined as that <MAT>.

Second, the calculation of the second weight of the base layer based on the better-visual-effect theory.

The second weight of Yvis_base and the second weight of Yir_base are respectively obtained according to the following formulas: <MAT><MAT> represents the second weight of the visible light luminance base layer, and <MAT> represents the second weight of the infrared image base layer. µ<NUM> represents a preset optimal luminance value, and the value range of µ<NUM> for an image of <NUM> bits is generally [<NUM>, <NUM>]. <MAT> represents a preset standard deviation. It can be seen that the closer the luminance of the source image to the optimal luminance value, the larger the fusion weight. Therefore, not only the fused picture luminance is more suitable for human eyes to view, but also the color cast of the picture which may be caused by too many infrared components due to overexposed areas (such as the number plate) in the infrared image can be effectively prevented.

Third, the final fusion weights of the base layers are as follow: <MAT> <MAT> <MAT> and <MAT>.

r<NUM> and r<NUM> represent preset control parameters and may control the contribution of the first weight and the second weight to a fusion weight of a final base layer. <MAT> represents the fusion weight of the visible light luminance base layer, and <MAT> represents the fusion weight of the infrared image base layer. The fused base layer satisfies following: Ycomb_base = Bvis'*Yvis_base + Bir'*Yir_base. Ycomb_base represents the fusion result of the base layers, Yvis_base represents the visible light luminance base layer, and Yir_base represents the infrared image base layer.

In this way, the better-visual-effect theory may be considered in the process of fusing the base layers, the final weights for the fusion are adjusted, and subjective factors are taken into account. Therefore, the fused image not only has the relatively high signal-to-noise ratio and clarity, but also can be more suitable for human visual senses.

In an embodiment, the step in which the visible light luminance detail layer and the infrared image detail layer are fused includes steps described below. An edge strength matrix of the visible light luminance detail layer and an edge strength matrix of the infrared image detail layer are calculated, and a first weight <MAT> of the visible light luminance detail layer and a first weight <MAT> of the infrared image detail layer are determined based on the edge strength matrices. A second weight <MAT> of the visible light luminance detail layer and a second weight <MAT> of the infrared image detail layer are determined according to a preset optimal edge strength value. A fusion weight of the visible light luminance detail layer is determined according to the first weight <MAT> of the visible light luminance detail layer and the second weight <MAT> of the visible light luminance detail layer; and a fusion weight of the infrared image detail layer is determined according to the first weight <MAT> of the infrared image detail layer and the second weight <MAT> of the infrared image detail layer. The visible light luminance detail layer and the infrared image detail layer are fused according to the fusion weight of the visible light luminance detail layer and the fusion weight of the infrared image detail layer. Similarly, the visible light luminance detail layer and the infrared image detail layer are fused based on objective criteria such as detail strength and subjective criteria such as a better visual effect, and steps are described below.

First, low-pass filtering being performed on the visible light luminance detail layer and the infrared image detail layer respectively to obtain edge strength matrices Evis and Eir Second, the calculation of the first weight of the detail layer based on the detail strength <MAT> and <MAT> <MAT>, and th represents a preset threshold. The value of the visible light edge strength less than the threshold is set to <NUM>, so that the visible light noise can be effectively reduced. <MAT> represents the first weight of the visible light luminance detail layer, and <MAT> represents the first weight of the infrared image detail layer. Third, the calculation of the second weight of the detail layer based on the better-visual-effect theory.

The second weight of Yvis_det and the second weight of Yir_det are respectively obtained according to the following formulas: <MAT>.

µ<NUM> represents a preset optimal edge strength value, and the value range of µ<NUM> for an image of <NUM> bits is generally [<NUM>, <NUM>]. <MAT> represents a preset standard deviation. It can be seen that the closer the local detail strength of the source image is to an optimal strength value, the larger the fusion weight. Therefore, the excessive edge enhancement that may be caused by relying only on the detail strength weight can be effectively prevented. <MAT> represents the second weight of the visible light luminance detail layer, and <MAT> represents the second weight of the infrared image detail layer. Fourth, the final fusion weights of the base layers <MAT> <MAT> <MAT> and <MAT>.

r<NUM> and r<NUM> are preset parameters and may control the contribution of each weight to the final detail layer fusion weights. Dvis represents the second weight of the visible light luminance detail layer, and Dir represents the second weight of the infrared image detail layer. The fused detail layer satisfies following: <MAT>.

Ycomb_det represents the fusion result of the detail layers, Yvis_det represents the visible light luminance detail layer, and Yir_det represents the infrared image detail layer.

Therefore, the fused luminance image satisfies that Ycomb = Ycomb_base + Ycomb_det. The final fused color image can be obtained by combining the fused luminance image and the chrominance component of the chrominance noise-reduced visible light image.

In step S140, image reconstruction is performed according to the luminance fusion result and the chrominance component of the visible light image to obtain a fused image.

After the luminance fusion is performed on the luminance component of the visible light image and the infrared image, the image reconstruction may be performed on the fusion result and the chrominance component of the visible light image to obtain the final fused image.

<FIG> is a schematic diagram of an image fusion flow according to an embodiment of the present application. As shown in <FIG>, after a visible light image and an infrared image of a shooting target are obtained, luminance and chrominance separation processing is performed on the visible light image to obtain visible light luminance and visible light chrominance. Luminance fusion is performed on the visible light luminance and the infrared luminance of the infrared image to obtain a luminance fusion result, and chrominance noise reduction processing may be performed on the visible light chrominance. Image reconstruction is performed on the obtained noise reduction result and the luminance fusion result to obtain a final fused image. In an embodiment, linear filtering algorithm (such as mean filtering, Gaussian filtering and the like) or non-linear edge preserving filtering algorithm (such as bilateral filtering, non-local mean filtering and the like) may be adopted to perform noise reduction on the chrominance component of the visible light image. The image after the chrominance noise reduction has a higher signal-to-noise ratio. In this way, problems can be avoided such as color distortion, edge blurring and pseudo-edge caused by the inconsistency in the luminance and the structure due to direct fusion of the infrared image and the visible light image. Moreover, the excessive picture enhancement caused by that fusion rules only consider objective factors can be avoided, so that the fused image is more suitable for human eyes to view.

In the embodiment, the visible light luminance has the same meaning as the luminance component of the visible light image, and the visible light chrominance has the same meaning as the chrominance component of the visible light image.

<FIG> is a schematic diagram of a luminance fusion flow according to an embodiment of the present application. As shown in <FIG>, after infrared luminance of an infrared image and visible light luminance of a visible light image are obtained, image correction may be first performed on the infrared luminance by using the visible light luminance, and luminance decomposition is performed on the visible light luminance and the corrected infrared image to obtain a visible light detail layer, a visible light base layer, an infrared detail layer and an infrared base layer, respectively. One of the above manners may be adopted to fuse the visible light base layer and the infrared base layer and to perform detail fusion on the visible light detail layer and the infrared detail layer. Moreover, the result of the fusion of the base layers and the result of the fusion of the detail layers are finally fused to obtain the final fused luminance.

In the embodiment, the visible light detail layer has the same meaning as the visible light luminance detail layer, the visible light base layer has the same meaning as the visible light luminance base layer, the infrared detail layer has the same meaning as the infrared image detail layer, and the infrared base layer has the same meaning as the infrared image base layer.

In an embodiment, the inconsistency in the luminance and the structure of the source image is eliminated, so that color cast, detail loss, pseudo-edge and other problems that may be caused in the fusion techniques in the related art are avoided. In addition, during the fusion, not only objective factors such as region saliency and edge strength are taken into account, but also subjective perception of human eyes of the image is considered, which effectively solve the problem of excessive enhancement of the fused image and make the visual effect of the fused image more natural.

In the embodiment of the present embodiment, a visible light image and an infrared image which are to-be-fused are acquired. Luminance and chrominance separation is performed on the visible light image to extract a luminance component and a chrominance component. Luminance fusion is performed on the luminance component of the visible light image and the infrared image to obtain a luminance fusion result. Image reconstruction is performed according to the luminance fusion result and the chrominance component of the visible light image to obtain a fused image. According to the embodiment provided by the present application, the luminance fusion is performed on the luminance component of the visible light image and the luminance component of the infrared image, so that the signal-to-noise ratio and the contrast of the image obtained after fusion are improved, and edge information is better retained.

<FIG> is a structural diagram of an image fusion apparatus according to an embodiment of the present application. As shown in <FIG>, the image fusion apparatus includes an image acquisition module <NUM>, a luminance and chrominance separation module <NUM>, a luminance fusion module <NUM> and a luminance and chrominance reconstruction module <NUM>. The image acquisition module <NUM> is configured to acquire a visible light image and an infrared image which are to-be-fused. The luminance and chrominance separation module <NUM> is configured to perform luminance and chrominance separation on the visible light image to extract a luminance component and a chrominance component. The luminance fusion module <NUM> is configured to perform luminance fusion on the luminance component of the visible light image and the infrared image to obtain a luminance fusion result. The luminance and chrominance reconstruction module <NUM> is configured to perform image reconstruction according to the luminance fusion result and the chrominance component of the visible light image to obtain a fused image.

The above products may execute the method provided by any embodiment of the present application, and has functional modules corresponding to the executed method.

The embodiment of the present application further provides a_storage medium including computer-executable instructions which, when executed by a computer processer, execute an image fusion method. The method includes steps described below. A visible light image and an infrared image which are to-be-fused are acquired. Luminance and chrominance separation is performed on the visible light image to extract a luminance component and a chrominance component. Luminance fusion is performed on the luminance component of the visible light image and the infrared image to obtain a luminance fusion result. Image reconstruction is performed according to the luminance fusion result and the chrominance component of the visible light image to obtain a fused image.

The storage medium is any one of various types of memory devices or storage devices. The term "storage medium" is intended to include: an installation medium such as a compact disc read-only memory (CD-ROM), a floppy disk or a magnetic tape device; a computer system memory or a random-access memory such as a dynamic random-access memory (DRAM), a double data rate random-access memory (DDR RAM), a static random-access memory (SRAM), an extended data out random-access memory (EDO RAM) and a Rambus random-access memory (Rambus RAM); a non-volatile memory such as a flash memory and a magnetic medium (like a hard disk or an optical storage); a register or other similar types of memory components, etc. The storage medium may further include other types of memories or combinations thereof. In addition, the storage medium may be located in a computer system in which programs are executed, or may be located in a different second computer system connected to the computer system through a network (such as the Internet). The second computer system may provide program instructions to a computer for execution. The term "storage medium" may include two or more storage media which may reside at different positions (for example, in different computer systems connected through a network). The storage medium may store program instructions (for example, embodied as computer programs) which are executable by one or more processors.

Of course, in the storage medium including computer-executable instructions provided by the embodiment of the present application, the computer-executable instructions may implement not only the above image fusion operations but also related operations in the image fusion method provided by any embodiment of the present application.

The embodiment of the present application provides an electronic device in which the image fusion apparatus provided by the embodiment of the present application may be integrated. <FIG> is a structural diagram of an electronic device according to an embodiment of the present application. As shown in <FIG>, the embodiment provides an electronic device <NUM>. The electronic device <NUM> includes one or more processors <NUM> and a storage apparatus <NUM> configured to store one or more programs which, when executed by the one or more processors <NUM>, cause the one or more processors <NUM> to implement the image fusion method provided by the embodiments of the present application. The method includes steps described below. A visible light image and an infrared image which are to-be-fused are acquired. Luminance and chrominance separation is performed on the visible light image to extract a luminance component and a chrominance component. Luminance fusion is performed on the luminance component of the visible light image and the infrared image to obtain a luminance fusion result. Image reconstruction is performed according to the luminance fusion result and the chrominance component of the visible light image to obtain a fused image.

Of course, the processor <NUM> also implements the image fusion method provided by any embodiment of the present application.

The electronic device <NUM> shown in <FIG> is merely an example.

As shown in <FIG>, the electronic device <NUM> includes a processor <NUM>, a storage apparatus <NUM>, an input apparatus <NUM> and an output apparatus <NUM>. One or more processors <NUM> may be disposed in the electronic device, and one processor <NUM> is taken as an example in <FIG>. The processor <NUM>, the storage apparatus <NUM>, the input apparatus <NUM> and the output apparatus <NUM> in the electronic device may be connected through a bus or in other manners. <FIG> uses connection through a bus as an example.

As a computer-readable storage medium, the storage apparatus <NUM> may be configured to store software programs, computer-executable programs and module units, such as program instructions corresponding to the image fusion method in the embodiments of the present application.

The storage apparatus <NUM> may mainly include a program storage area and a data storage area. The program storage area may store an operating system and an application program required for implementing at least one function while the data storage area may store data created depending on use of terminals. In addition, the storage apparatus <NUM> may include a high-speed random-access memory, and may also include a non-volatile memory, such as at least one disk memory, flash memory or another non-volatile solid-state memory. In some examples, the storage apparatus <NUM> may further include memories disposed remotely relative to the processor <NUM>, and these remote memories may be connected through a network. Examples of the above network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network and a combination thereof.

The input apparatus <NUM> may be configured to receive input digital, character or speech information and generate key signal input related to user settings and function control of the electronic device. The output apparatus <NUM> may include devices such as a display screen and a speaker.

According to the electronic device provided by the embodiment of the present application, the luminance fusion may be performed on the luminance component of the visible light image and the luminance component of the infrared image, so that the signal-to-noise ratio and the contrast of the image obtained after fusion are improved, and edge information is better retained.

Claim 1:
A computer-implemented image fusion method, comprising:
acquiring (S110) a visible light image and an infrared image which are to-be-fused;
performing (S120) luminance and chrominance separation on the visible light image to extract a luminance component of the visible light image and a chrominance component of the visible light image;
performing (S130) luminance fusion on the luminance component of the visible light image and the infrared image to obtain a luminance fusion result; and
performing (S140) image reconstruction according to the luminance fusion result and the chrominance component of the visible light image to obtain a fused image;
wherein performing (S130) the luminance fusion on the luminance component of the visible light image and the infrared image to obtain the luminance fusion result comprises:
correcting the infrared image according to the luminance component of the visible light image to obtain a corrected infrared image;
performing image layer decomposition on the luminance component of the visible light image and the corrected infrared image, respectively, and performing corresponding fusion on a plurality of layers of the luminance component of the visible light image obtained after the image layer decomposition and a plurality of layers of the corrected infrared image obtained after the image layer decomposition; and
superimposing results of performing corresponding fusion on the plurality of layers of the luminance component of the visible light image obtained after the image layer decomposition and the plurality of layers of the corrected infrared image obtained after the image layer decomposition to obtain the luminance fusion result;
characterized in that
correcting the infrared image according to the luminance component of the visible light image to obtain the corrected infrared image comprises:
determining a position of a reference pixel in the luminance component of the visible light image according to a position of each pixel in the infrared image; and
determining a luminance correction result of the each pixel according to a preset-range neighborhood block with the position of the reference pixel as a center and a preset-range neighborhood block with the position of the each pixel as a center to obtain the corrected infrared image.