CN112419215A - Image processing method, image processing device, electronic equipment and storage medium - Google Patents
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Abstract
The invention discloses an image processing method, an image processing device, electronic equipment and a storage medium. The method comprises the following steps: the method comprises the steps of obtaining an image to be processed, conducting down-sampling processing on the image to be processed, when the down-sampled image meets a first layering condition, respectively determining pixel codes of a current down-sampled image and the image before down-sampling, determining an image distance between the current down-sampled image and the image before down-sampling based on the pixel codes, conducting iteration down-sampling processing on the current down-sampled image when the current down-sampled image and the image distance meet a second layering condition, forming an image set of a pyramid structure based on the image to be processed and at least one down-sampled image obtained through the down-sampling processing, achieving self-adaptive layering of the image, being applicable to various images and not limited by image characteristics, enabling the layered pyramid structure to be applicable to the fields of image registration and the like, and improving the speed and the precision of the image registration.
Description
Technical Field
The embodiment of the invention relates to the technical field of image processing, in particular to an image processing method and device, electronic equipment and a storage medium.
Background
The image pyramid is one of image multi-scale expression, is an effective and simple-concept data structure for analyzing images by multi-resolution, and can be applied to the field of image registration. One more layer may cause errors in the matching result, and one less layer may reduce the matching speed by several times.
In the prior art, the pyramid layer number is usually manually selected according to experience. The pyramid hierarchy in OpenCV adopts a manual entry mode. Some scholars propose a method for determining the pyramid layer number according to the loss amount of the feature points among the images with different scales. However, this method still requires a large number of experiments to obtain a threshold value of the feature point loss amount as a layering condition, which inherently cannot achieve true self-adaptation. The image processing software widely applied in industry is obtained, the pyramid in Halcon is layered with auto mode, the layer number of the pyramid can be automatically adjusted, but commercial software is not open source and cannot obtain the internal algorithm principle.
Disclosure of Invention
The invention provides an image processing method, an image processing device, electronic equipment and a storage medium, which are used for realizing self-adaptive layering of images, are suitable for various images, so that the speed and the precision of image registration are improved, and meanwhile, the manual selection of the pyramid layer number is replaced, and the operation is simplified.
In a first aspect, an embodiment of the present invention provides an image processing method, including:
acquiring an image to be processed, and performing down-sampling processing on the image to be processed to obtain a down-sampled image;
when the down-sampled image meets a first hierarchical condition, respectively determining pixel codes of a current down-sampled image and an image before down-sampling, and determining an image distance between the current down-sampled image and the image before down-sampling based on the pixel codes;
when the current downsampling image and the image distance meet a second hierarchical condition, performing iterative downsampling processing on the current downsampling image;
and forming an image set with a pyramid structure based on the image to be processed and at least one down-sampled image obtained by down-sampling processing.
In a second aspect, an embodiment of the present invention further provides an image processing apparatus, including:
the image to be processed down-sampling module is used for acquiring an image to be processed and performing down-sampling processing on the image to be processed to obtain a down-sampled image;
the image distance calculation module is used for respectively determining pixel codes of a current downsampled image and an image before downsampling when the downsampled image meets a first hierarchical condition, and determining an image distance between the current downsampled image and the image before downsampling based on the pixel codes;
the current image down-sampling module is used for performing iterative down-sampling processing on the current down-sampled image when the current down-sampled image and the image distance meet a second hierarchical condition;
and the image set forming module is used for forming an image set with a pyramid structure based on the image to be processed and at least one down-sampled image obtained by down-sampling processing.
In a third aspect, an embodiment of the present invention further provides an electronic device, where the electronic device includes:
one or more processors;
a storage device for storing one or more programs,
when the one or more programs are executed by the one or more processors, the one or more processors implement the image processing method provided by the embodiment of the present invention.
In a fourth aspect, the embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, which when executed by a processor implements the image processing method provided by the embodiment of the present invention.
The embodiment of the invention has the following advantages or beneficial effects:
the method comprises the steps of performing down-sampling processing on an image to be processed by acquiring the image to be processed to obtain a down-sampled image; when the down-sampled image meets a first layering condition, respectively determining pixel codes of the current down-sampled image and an image before down-sampling, and determining an image distance between the current down-sampled image and the image before down-sampling based on the pixel codes; when the current down-sampled image and the image distance meet the second hierarchical condition, performing iterative down-sampling processing on the current down-sampled image; the image set of the pyramid structure is formed based on the image to be processed and at least one downsampled image obtained through downsampling processing, self-adaptive layering of the image is achieved, the image set is suitable for various images and is not limited by image characteristics, so that the self-adaptive layered pyramid structure can be suitable for the fields of image registration and the like, the speed and the precision of image registration are improved, meanwhile, the number of layers of the pyramid is selected manually, and operation is simplified.
Drawings
In order to more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, a brief description is given below of the drawings used in describing the embodiments. It should be clear that the described figures are only views of some of the embodiments of the invention to be described, not all, and that for a person skilled in the art, other figures can be derived from these figures without inventive effort.
Fig. 1 is a schematic flowchart of an image processing method according to an embodiment of the present invention;
FIG. 2 is an image set of template images according to an embodiment of the present invention;
FIG. 3 is a diagram of an image set of images to be searched according to an embodiment of the present invention;
fig. 4 is a flowchart illustrating an image processing method according to a second embodiment of the present invention;
fig. 5 is a diagram illustrating a target registration area according to a second embodiment of the present invention;
fig. 6 is a schematic flowchart of processing and registering a template image according to a second embodiment of the present invention;
fig. 7 is a schematic structural diagram of an image processing apparatus according to a third embodiment of the present invention;
fig. 8 is a schematic structural diagram of an electronic device according to a fourth embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.
Example one
Fig. 1 is a schematic flow diagram of an image processing method according to an embodiment of the present invention, which is applicable to a situation where pyramid adaptive layering is required to be performed on an image, and a layered image set is used for image registration or image fusion, where the method may be executed by an image processing apparatus, and the apparatus may be implemented by hardware and/or software, and the method specifically includes the following steps:
and S110, acquiring the image to be processed, and performing down-sampling processing on the image to be processed to obtain a down-sampled image.
The image to be processed refers to an image which needs to be subjected to multi-resolution analysis in the image processing process, such as a template image and/or an image to be searched, the template image and the image to be searched are layered to a multi-scale image structure, and registration analysis is performed on the multi-scale image structure to realize image registration, so that the speed and the precision of image registration are improved; or the left viewpoint image and/or the right viewpoint image subjected to image fusion are/is respectively fused under multiple scales by layering the two images into a multi-scale image structure, so that the speed and the precision of image fusion are improved.
In this embodiment, the downsampling process is used to reduce the resolution of the image to be processed, and the downsampling process may be implemented by deleting pixel points in even rows and even columns of the image, or by changing all pixels in a 2 × 2 window of the image into an average value of all pixels in the window, where, if the size of the image to be processed is M × N, the image to be processed is downsampled by 2 times, that is, an image with (M/2) × (N/2) size is obtained, and the resolution of the downsampled image is 1/4 of the image to be processed. By performing downsampling on the image to be processed in an iteration mode, an image set with gradually reduced resolution can be obtained, namely the image set with the pyramid structure, wherein the bottom layer image of the pyramid is the image to be processed, namely the original image which is not subjected to downsampling, and each image on the upper layer is obtained by downsampling the previous layer image layer by layer.
For example, the down-sampling may adopt a gaussian pyramid down-sampling method, taking down-sampling the layer 1 image (to-be-processed image) to obtain the second layer image as an example, the specific process is as follows: (1) taking the image to be processed as the bottom layer L1Store in pyramid data structure, for L1Carrying out Gaussian filtering on the image; (2) interlaced alternate decimation L1Pixels of the image, resulting in a second layer L2And (4) an image. In particular, from L1To L2Satisfies the following formula:
wherein G is2(i, j) is a pyramid L2Image, G1(i, j) is a pyramid L1Images, i, j representing the rows and columns of pixels, w (m, n) being a Gaussian kernel function, m, n being windowsWidth and height, with a window size of 5 × 5; m, N is L1Rows and columns of the image. It is understood that the construction from the k-th layer to the (k + 1) -th layer can also be obtained based on the above formula, where k is the middle layer of the pyramid structure.
And S120, when the down-sampled image meets the first hierarchical condition, respectively determining the pixel codes of the current down-sampled image and the image before down-sampling, and determining the image distance between the current down-sampled image and the image before down-sampling based on the pixel codes.
In this embodiment, the first hierarchical condition is a first boundary condition for performing down-sampling processing, and is used to determine the size of an image to be down-sampled, and the image distance is determined by calculating the image distance only when the first boundary condition is satisfied. Illustratively, the first delamination condition is that the image size is larger than a preset size. Specifically, the preset size may be 8 × 8, if the size of the down-sampled image is larger than 8 × 8, determining pixel codes of the current down-sampled image and the image before down-sampling, and if the size of the image to be processed is smaller than 8 × 8, stopping the down-sampling processing. By judging the size of the image to be downsampled, the calculation of the image distance and the downsampling processing of the image only meeting the size condition are realized, the condition that the image information is seriously lost due to continuous downsampling of the image with lower resolution is avoided, and the layering precision is improved.
The current downsampled image is obtained through downsampling processing based on the image before downsampling, and in the pyramid structure, the current downsampled image is an image on the upper layer of the image before downsampling. The pixel coding represents the pixel information of the image, the image distance is used for analyzing the information correlation of the two images based on the image pixel information, the larger the image distance is, the larger the pixel difference between the current downsampled image and the image before downsampling is, the smaller the information correlation between the two images is, the less useful information in the current downsampled image is, and the value of continuous downsampling is lower; the smaller the image distance is, the smaller the pixel difference between the current downsampled image and the image before downsampling is, the larger the information correlation between the two images is, the more useful information in the current downsampled image is, and the value of continuous downsampling is higher. Specifically, the pixel distance may be a hamming distance, that is, the number of different corresponding bits between the pixel codes of the current downsampled image and the image before downsampling, and the hamming distance is calculated according to the following formula:
wherein x isk、ykFor the kth of the pixel encodings of the current down-sampled image and the pre-down-sampled image,
the modulo-2 operation is shown, and n is the number of codes in the pixel codes of the current down-sampled image and the image before down-sampling. Illustratively, if the pixel code of the current downsampled image is 00000100, the pixel code of the image before downsampling is 00000111, and the number of different corresponding bits between the two pixel codes is 2, the hamming distance is 2.
Optionally, determining the pixel codes of the current downsampled image and the image before downsampling includes: respectively determining the mean value of the pixels of the image for the current downsampled image or the image before downsampling; and comparing the pixel value of each pixel point in the current downsampled image or the image before downsampling with the corresponding image pixel mean value, and determining the code of each pixel point according to the comparison result to obtain the pixel code of the current downsampled image or the image before downsampling.
The pixel mean value is the pixel mean value of all pixel points of the image. Specifically, when the pixel value of the pixel point is greater than or equal to the image pixel mean value, a first code of the pixel point is generated; when the pixel value of the pixel point is smaller than the image pixel mean value, generating a second code of the pixel point; and forming the codes of all the pixel points into a coding matrix based on the positions of all the pixel points in the current downsampled image or the image before downsampling to obtain the pixel codes of the current downsampled image or the image before downsampling.
Optionally, the first code and the second code are 1 and 0, respectively, when the pixel value of the pixel is greater than or equal to the pixel mean value, the code of the pixel is 1, and when the pixel value of the pixel is less than the pixel mean value, the code of the pixel is 0. Illustratively, each pixel row of the image is traversed, wherein each row traverses each pixel of the image matrix G point by point from left to right, and if the ith row and the j column element G (i, j) >, a, the encoding value is recorded as 1; if the ith row and j column element G (i, j) < a, the encoded value is noted as 0. If the number of the pixel points in the current downsampled image or the image before downsampling is n, based on the position of each pixel point, the codes of the pixel points of each pixel point are placed into the code matrix line by line, and a 1 x n one-dimensional code matrix can be formed. Illustratively, the size of the current downsampled image or the image before downsampling is 2 × 2, the number of pixel points is 4, and the formed coding matrix is 1 × 4. The encoding of each pixel point forms a one-dimensional encoding matrix so as to compare corresponding bits between the one-dimensional encoding matrix of the down-sampled image and the one-dimensional encoding matrix of the image before down-sampling, thereby improving the calculation speed of the Hamming distance between the down-sampled image and the image before down-sampling.
Optionally, before determining the pixel encoding of the current downsampled image and the image before downsampling, the method further includes: respectively carrying out scaling processing on the current downsampled image and the image before downsampling to obtain the current downsampled image and the image before downsampling with the same size; accordingly, determining the pixel encoding of the current downsampled image and the image before downsampling comprises: and coding the current downsampled image with the same size and the image before downsampling to obtain the pixel code corresponding to each image. The current downsampled image and the image before downsampling are subjected to scaling processing, and the sizes of the current downsampled image and the image before downsampling are unified, so that pixel codes with the same number of bits are obtained, and the wrong image distance is avoided.
The Hamming distance is the number of corresponding bits of two codes with the same length, so that the number of bits of pixel codes of the current downsampled image and the image before downsampling is required to be consistent, the image is subjected to scaling processing before the image calculation region is coded, and the size of the image is fixed to be a uniform size, such as 8 x 8, so that the pixel codes of the current downsampled image and the image before downsampling, wherein the number of the corresponding bits of the two codes is consistent.
Illustratively, the complete process of calculating the image distance between the current down-sampled image and the image before down-sampling in this embodiment is as follows: (1) image zooming: scaling the current down-sampled image and the image before down-sampling to 8 × 8 pixels; (2) calculating the pixel mean value: calculating the average value of all elements in the matrix according to the 8 multiplied by 8 integer matrix G obtained in the last step, and enabling the value to be a; (3) acquiring pixel codes: traversing each pixel of matrix G row by row from left to right if the ith row and j column elements G (i, j)>If a, the code value is 1; if the ith row and j column element G (i, j)<a, recording the coding value as 0, thereby obtaining the region coding of the current downsampled image and the image before downsampling; (4) calculating the Hamming distance: computing a current downsampled image [ x ]1,x2,…,xk,…,xn]And the image before down-sampling [ y1,y2,…,yk,…,yn]D (x, y).
And S130, when the current down-sampling image and the image distance meet the second hierarchical condition, performing iterative down-sampling processing on the current down-sampling image.
The current downsampled image is an image obtained by first or multiple downsampling of the image to be processed, and the second hierarchical condition is used for further judging whether the current downsampled image continues to be downsampled or not. If the current downsampled image meets the first layering condition and the current downsampled image and the image distance meet the second layering condition, the current downsampled image is subjected to downsampling to obtain a downsampled image of the current downsampled image, the downsampled image is used as the current downsampled image, and the steps of judging the conditions and downsampling are iterated repeatedly until the first layering condition or the second layering condition is not met.
Optionally, when any of the down-sampled images does not satisfy the first hierarchical condition and/or an image distance between any of the down-sampled images and an image before down-sampling of any of the down-sampled images does not satisfy the second hierarchical condition, stopping processing of any of the down-sampled images. By judging the current downsampled image based on the second layering condition, the layering is stopped when the useful information of the current downsampled image is less, and the layering is continued when the useful information of the current downsampled image is more, so that the self-adaptive layering based on the image information is realized, the reduction of the registration accuracy caused by excessive layering is avoided, and the registration speed caused by insufficient layering is lower, so that the registration accuracy and speed are improved.
Optionally, the second hierarchical condition includes that the current downsampled image is not the first downsampled image of the image to be processed, and the image distance is less than or equal to the preset distance.
As shown in tables 1 to 3, the hamming distances between the images of the layers after the down-sampling of the 6 different images to be processed are shown, wherein the image of the layer 1 is an original non-layered image to be processed, the images after the iterative down-sampling of the images to be processed are shown in the layers 2 to 5, the similarity is evaluated as the similarity between two adjacent layers of images, the effective information content contained in the image of the current layer is represented, and if the hamming distance between the image of the current layer and the image before the down-sampling is less than 5, the two images are very similar at this time, and the image of the current layer still contains a large amount of effective information content; if the Hamming distance is larger than 10, the similarity of the two images is low, and the information loss of the current layer image is serious.
As can be seen from Table 1, the Lth image of the first image to be processed2Layer to L4Hamming distance of layer less than 10 from L4Hamming distances from layer to topmost layer are all greater than 10, indicating the L < th > layer4The amount of image information after a layer is severely lost, in this case by L4The layer is the top layer and satisfies the cutoff condition, namely from L3Layer and L3Calculating the Hamming distance of the upper layer of the layer to be more than 10 for the first time, and stopping layering; l < th > of the second image to be processed2Layer to L4Hamming distance of layer greater than 10, L5The Hamming distance of the layer is less than 10, and L is used for the abnormal condition that the Hamming distances of the first layers are all more than 103The layer is the top layer and satisfies the cutoff condition, namely from L3Layer and L3And (5) calculating the Hamming distance of the upper layer of the layer to be more than 10 for the first time, and stopping layering. As can be seen from Table 2, the Lth image of the third image to be processed2Of a layerHamming distance greater than 10, L3Layer to L5The Hamming distance of the layer is less than 10, and for the abnormal condition that the Hamming distance of the first layered image is more than 10, but the Hamming distances of the subsequent layers are all less than 10, the first layered condition is taken as a cut-off condition; lth of fourth image to be processed2Hamming distance of layer is greater than 10, L3Hamming distance of layer less than 10, L4The layer is again greater than 10, this time at L4The layer is the top layer and satisfies the cutoff condition, namely from L3Layer and L3And (5) calculating the Hamming distance of the upper layer of the layer to be more than 10 for the first time, and stopping layering. As can be seen from Table 3, the fifth image to be processed is from the L-th image2Layer to L5The Hamming distance of each layer is less than or equal to 10, which indicates that the information content of each layer of image meets the requirement, and the first layering condition is taken as a cut-off condition; lth of sixth image to be processed2Hamming distance of layer is less than or equal to 10 from L3Hamming distances from layer to topmost layer are all greater than 10, indicating the L < th > layer3The amount of image information after a layer is severely lost, which is the case with L3The layer is the top layer and satisfies the cutoff condition, namely from L3Layer and L3And (5) calculating the Hamming distance of the upper layer of the layer to be more than 10 for the first time, and stopping layering.
TABLE 1
TABLE 2
TABLE 3
In summary, L is not limited2If the Hamming distance of the layer pyramid image is less than 10, the L-th distance is selected2Determining the number of layers with the Hamming distance larger than 10 after the first layer as a cut-off condition, namely determining the downsampled images with the Hamming distance larger than 10 except the first downsampled image as top layer images; if no case occurs in which the Hamming distance is greater than 10, the topmost image is determined based on the first hierarchical condition. It can be understood that, according to the second hierarchical condition, the image distance of the downsampled image except the first downsampled image of the image to be processed is determined, the corresponding layer of the downsampled image whose image distance is greater than the preset distance is used as a cut-off layer, and the downsampling process is stopped, that is, no matter what value the image distance of the first downsampled image of the image to be processed is, the image distance of the downsampled image after the first downsampled image is only determined. In the embodiment, the self-adaptive layering of the images to be layered is realized by judging the image distance after the second down-sampling, so that the information content of each layer of images meets the requirement.
And S140, forming an image set with a pyramid structure based on the image to be processed and at least one down-sampled image obtained by down-sampling processing.
The image to be processed is an original image which is not subjected to down-sampling processing, and one image to be processed can be subjected to continuous down-sampling processing to obtain at least one down-sampled image. The pyramid structure is an image structure with gradually reduced resolution, the bottom layer is an image to be processed, and the images of other layers are obtained layer by layer through iterative down-sampling of the images of the bottom layer. As shown in fig. 2 and fig. 3, the image sets of the template image and the downsampled pyramid structure of the image to be searched are respectively.
The adaptive hierarchical pyramid structure obtained in this embodiment may be applied to image registration, as shown in table 4, which is a comparison between the accuracy of image registration in the adaptive hierarchical pyramid and the manual hierarchical pyramid. As can be seen from the figure, the number of the pyramid self-adaptive layering layers is 3, and the matching result is accurate at the moment. If the number of layers is manually set to be 3, the same result can be obtained, and if the number of layers is manually set to be 4, the matching result is subjected to error, but the matching speed is relatively improved. Therefore, pyramid adaptive layering can achieve the same matching effect while ensuring accuracy.
TABLE 4
Table 5 shows that the effect of the pyramid adaptive layering method provided in this embodiment is compared with that of an unused pyramid adaptive layering method in image registration, and as can be seen from the table, under the condition that the same registration accuracy is ensured, matching speed can be effectively increased by pyramid adaptive layering, and speed level is increased, so that this embodiment has a good application advantage in image registration.
TABLE 5
Table 6 shows the result of FAST feature point calculation for the pyramid image of "lena. jpg", and as can be seen from table 6, the FAST feature point method is applied to detection of the pyramid image to find: (1) manually appointing a threshold value t, wherein the influence of the threshold value on the number of the characteristic points is large, and the optimal value cannot be accurately judged; (2) when the number of the feature points is reduced quickly during scale change and the fluctuation between layers is large, the problem that the number of the matching points between layers is difficult to meet the requirement that 25% -30% of the information quantity of the fuzzy image at the first layer of the image pyramid mentioned in the self-adaptive scale-changing feature point extraction method is used as the threshold value of the number of the matching points is solved, the reliability of the result can be ensured, and the rationality of the matching time can be ensured; (3) almost no feature points exist in the smooth area of the image, and the method can be invalid if the input image has more smooth areas. Therefore, the FAST feature point method cannot be well applied to the task of pyramid adaptive layering, and the scheme of the embodiment can be applied to various images without being limited by image features.
TABLE 6
According to the technical scheme of the embodiment, the image to be processed is acquired and subjected to down-sampling processing to obtain a down-sampled image; when the down-sampled image meets a first layering condition, respectively determining pixel codes of the current down-sampled image and an image before down-sampling, and determining an image distance between the current down-sampled image and the image before down-sampling based on the pixel codes; performing iterative down-sampling processing on the current down-sampled image when the current down-sampled image and the image distance meet a second hierarchical condition; the image set of the pyramid structure is formed based on the image to be processed and at least one downsampled image obtained through downsampling processing, self-adaptive layering of the image is achieved, the image set is suitable for various images and is not limited by image characteristics, so that the self-adaptive layered pyramid structure can be suitable for the fields of image registration and the like, the speed and the precision of image registration are improved, meanwhile, the number of layers of the pyramid is selected manually, and operation is simplified.
Example two
Fig. 4 is a schematic flowchart of an image processing method according to a second embodiment of the present invention, and in this embodiment, on the basis of the foregoing embodiment, when the image to be processed is a template image to be registered, a registration step with the template image and the image to be searched is added. Wherein explanations of the same or corresponding terms as those of the above embodiments are omitted.
Referring to fig. 4, the image processing method provided in this embodiment includes:
and S410, acquiring a template image, and performing down-sampling processing on the template image to obtain a down-sampled image.
The template image and the image to be searched are images acquired by the same object under different conditions, for example, a plurality of images shot under different conditions such as different acquisition devices, acquisition time, shooting distance or shooting angle of view, and the template image can be mapped onto the image to be searched, so that the template image and the image to be searched correspond to each other at points of the same position in space one by one, wherein the template image is a local image searched in a corresponding area of the image to be searched.
And S420, when the down-sampled image meets the first layering condition, respectively determining the pixel codes of the current down-sampled image and the image before down-sampling, and determining the image distance between the current down-sampled image and the image before down-sampling based on the pixel codes.
And S430, when the current down-sampling image and the image distance meet the second hierarchical condition, performing iterative down-sampling processing on the current down-sampling image.
And S440, forming an image set with a pyramid structure based on the template image and at least one down-sampled image obtained by down-sampling processing.
S450, determining the number of layers in the image set of the pyramid structure, and performing layering processing on the image to be searched which is registered with the template image based on the number of layers to obtain the image set of the pyramid structure of the image to be searched.
The number of layers of the image set of the pyramid structure of the template image is used for designating the number of layers of the image to be searched, so that the pyramid structure of the image to be searched and the number of layers of the pyramid structure of the template image are kept consistent.
And S460, performing rotation processing on each image in the image set corresponding to the template image to obtain at least one image to be registered corresponding to each pyramid layer.
The image to be registered refers to an image obtained by correspondingly rotating each layer of image in the image set of the template image, and each layer of image in the image set can generate a plurality of rotated images according to different rotation angles. Optionally, after performing rotation processing on each image in the image set corresponding to the template image, performing filling processing on the rotated image, and accordingly, taking the image after the filling processing as the image to be registered.
And S470, performing correlation registration on the at least one image to be registered with the same layer number and the corresponding image in the image set of the pyramid structure of the image to be searched layer by layer based on the pyramid structure to obtain a target registration area matched with the template image in the image to be searched.
Specifically, a registration angle range of the current layer number is obtained, and at least one target registration image for matching is determined based on the image to be registered of the registration angle range in the current layer number, where the target registration image is an image subjected to correlation registration with the image to be searched in the current layer number. If the current layer number is the top layer in the pyramid structure, the registration angle range is full-range angle registration, namely all the rotation images of the top layer are used as target registration images and are registered with top layer images in a target image set one by one; if the current layer number is not the top layer of the pyramid structure, that is, the bottom layer or the middle layer, the registration angle range is determined by the registration angle obtained by the previous layer and a preset range, specifically, the registration angle range is ± the registration angle of the previous layer (preset range/2), exemplarily, if the current layer is the 4 th layer, the registration angle obtained by the 3 rd layer is 90 °, the preset range is 10 °, the registration angle range of the 3 rd layer is 85 ° to 95 °, and an image in an interval of 85 ° to 95 ° is selected from the to-be-registered images of the 4 th layer as a target registration image.
After the target registration images are determined, respectively determining correlation coefficients of at least one target registration image and corresponding images in an image set of the images to be searched, determining a registration angle and a registration coordinate corresponding to the target registration image with the largest correlation coefficient, and judging whether the current layer number is the bottom layer of a pyramid structure, if not, iteratively executing the operation of mapping the registration angle range downwards, if yes, determining a target registration area matched with the template image based on the registration coordinate and the registration angle obtained by bottom layer registration, and as shown in fig. 5, framing out the displayed area as the target registration area in the images to be searched.
Illustratively, the complete steps of processing and registering the template image are as follows, as shown in fig. 6: (1) inputting a template image; (2) taking the input template image as L1Storing the layers into a pyramid structure; (3) to Lk(k 1, 2.. said.) layer image downsampling to obtain Lk+1A layer image; (4) judgment of Lk+1Whether the layer image meets the size boundary or not is judged, if yes, the step (5) is executed, and if not, the step (8) is executed; (ii) a (5) Obtaining LkLayer and Lk+1Image coding of the layers, calculating their hamming distances; (6) judging whether the requirements are metIf the Hamming distance boundary is the boundary, executing the step (7), otherwise, executing the step (8); (7) mixing L withk+1Storing the layer image into a pyramid structure, k + +, and executing the step (3); (8) finishing the down sampling, and recording the number of layers k +1, namely the number of layers of the self-adaptive layering; (9) performing down-sampling on the image to be searched according to k + 1; (10) the template image is matched on the image to be searched from coarse to fine, i.e. from top to bottom.
According to the technical scheme of the embodiment, the pyramid self-adaptive layering method is applied to the two images participating in image registration, so that layer-by-layer registration from top to bottom is realized, the speed and the precision of image registration are improved, and the pyramid self-adaptive layering method is also suitable for registration of images with more smooth areas.
EXAMPLE III
Fig. 7 is a schematic structural diagram of an image processing apparatus according to a third embodiment of the present invention, which is applicable to a situation where images need to be pyramid-adaptively layered and layered image sets are used for image registration or image fusion, where the apparatus specifically includes: a to-be-processed image down-sampling module 710, an image distance calculation module 720, a current image down-sampling module 730, and an image set forming module 740.
The to-be-processed image down-sampling module 710 is configured to acquire an image to be processed, and perform down-sampling processing on the image to be processed to obtain a down-sampled image;
an image distance calculating module 720, configured to determine pixel codes of the current downsampled image and the image before downsampling respectively when the downsampled image meets the first hierarchical condition, and determine an image distance between the current downsampled image and the image before downsampling based on the pixel codes;
the current image down-sampling module 730 is configured to perform iterative down-sampling processing on the current down-sampled image when the current down-sampled image and the image distance satisfy a second hierarchical condition;
and an image set forming module 740, configured to form an image set with a pyramid structure based on the image to be processed and the at least one downsampled image obtained through the downsampling processing.
In the embodiment, the image to be processed is obtained through the image to be processed downsampling module, and downsampling processing is performed on the image to be processed to obtain a downsampled image; the image-based distance calculation module is used for respectively determining the pixel codes of the current downsampled image and the image before downsampling when the downsampled image meets a first hierarchical condition, and determining the image distance between the current downsampled image and the image before downsampling based on the pixel codes; performing iterative down-sampling processing on the current down-sampled image based on the fact that the current image down-sampling module meets a second hierarchical condition when the current down-sampled image and the image distance meet the second hierarchical condition; the image set forming module forms an image set of a pyramid structure based on the image to be processed and at least one downsampled image obtained through downsampling processing, self-adaptive layering of the image is achieved, the image set forming module is suitable for various images and is not limited by image characteristics, so that the self-adaptive layered pyramid structure can be suitable for the fields of image registration and the like, the speed and the precision of image registration are improved, meanwhile, the number of layers of a pyramid is selected manually, and operation is simplified.
Optionally, the current image downsampling module 730 is further configured to stop processing of any downsampled image when any downsampled image does not satisfy the first hierarchical condition and/or an image distance between any downsampled image and an image before any downsampling of any downsampled image does not satisfy the second hierarchical condition.
Optionally, the first hierarchical condition is that the image size is larger than a preset size, the second hierarchical condition includes that the current downsampled image is not the first downsampled image of the image to be processed, and the image distance is smaller than or equal to the preset distance.
Optionally, the image distance calculating module 720 includes:
the pixel mean value calculating unit is used for respectively determining the pixel mean value of the image for the current downsampled image or the image before downsampling;
and the pixel code calculating unit is used for comparing the pixel value of each pixel point in the current downsampled image or the image before downsampling with the corresponding image pixel mean value, determining the code of each pixel point according to the comparison result, and obtaining the pixel code of the current downsampled image or the image before downsampling.
Optionally, the pixel code calculating unit is specifically configured to generate a first code of the pixel point when the pixel value of the pixel point is greater than or equal to the image pixel mean value; when the pixel value of the pixel point is smaller than the image pixel mean value, generating a second code of the pixel point; and forming the codes of all the pixel points into a coding matrix based on the positions of all the pixel points in the current downsampled image or the image before downsampling to obtain the pixel codes of the current downsampled image or the image before downsampling.
Optionally, the image distance calculating module 720 further includes:
the scaling processing unit is used for respectively scaling the current downsampled image and the image before downsampling before determining the pixel codes of the current downsampled image and the image before downsampling to obtain the current downsampled image and the image before downsampling with the same size;
correspondingly, the pixel mean value calculating unit and the pixel coding calculating unit are also used for coding the current downsampled image with the same size and the image before downsampling to obtain the pixel coding corresponding to each image.
Optionally, on the basis of the image processing apparatus, an image registration module is further included, and the image registration module includes:
the image to be searched layering unit is used for determining the layer number in the image set of the pyramid structure when the image to be processed is the template image to be registered, and performing layering processing on the image to be searched which is registered with the template image based on the layer number to obtain the image set of the pyramid structure of the image to be searched;
the rotating unit is used for rotating each image in the image set corresponding to the template image to obtain at least one image to be registered corresponding to each pyramid layer;
and the registration unit is used for performing correlation registration on the at least one image to be registered with the same layer number and the corresponding image in the image set of the pyramid structure of the image to be searched layer by layer based on the pyramid structure to obtain a target registration area matched with the template image in the image to be searched.
The image processing device provided by the embodiment of the invention can execute the image processing method provided by any embodiment of the invention, and has corresponding functional modules and beneficial effects of the execution method.
It should be noted that, the units and modules included in the system are merely divided according to functional logic, but are not limited to the above division as long as the corresponding functions can be realized; in addition, specific names of the functional units are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the embodiment of the invention.
Example four
Fig. 8 is a schematic structural diagram of an electronic device according to a fourth embodiment of the present invention. FIG. 8 illustrates a block diagram of an exemplary electronic device 80 suitable for use in implementing embodiments of the present invention. The electronic device 80 shown in fig. 8 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiment of the present invention.
As shown in fig. 8, the electronic device 80 is in the form of a general purpose computing device. The components of the electronic device 80 may include, but are not limited to: one or more processors or processing units 801, a system memory 802, and a bus 803 that couples various system components including the system memory 802 and the processing unit 801.
The electronic device 80 typically includes a variety of computer system readable media. Such media may be any available media that is accessible by electronic device 80 and includes both volatile and nonvolatile media, removable and non-removable media.
The system memory 802 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM)804 and/or cache memory 805. The electronic device 80 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 806 may be used to read from and write to non-removable, nonvolatile magnetic media (not shown in FIG. 8, and commonly referred to as a "hard drive"). Although not shown in FIG. 8, a magnetic disk drive for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, nonvolatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to the bus 803 by one or more data media interfaces. Memory 802 may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the invention.
A program/utility 808 having a set (at least one) of program modules 807 may be stored, for instance, in memory 802, such program modules 807 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which examples or some combination thereof may include an implementation of a network environment. Program modules 807 generally perform the functions and/or methodologies of embodiments of the present invention as described herein.
The electronic device 80 may also communicate with one or more external devices 809 (e.g., keyboard, pointing device, display 810, etc.), with one or more devices that enable a user to interact with the electronic device 80, and/or with any devices (e.g., network card, modem, etc.) that enable the electronic device 80 to communicate with one or more other computing devices. Such communication may occur via an input/output (I/O) interface 811. Also, the electronic device 80 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network such as the Internet) via the network adapter 812. As shown, the network adapter 812 communicates with the other modules of the electronic device 80 over the bus 803. It should be appreciated that although not shown in FIG. 8, other hardware and/or software modules may be used in conjunction with electronic device 80, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
The processing unit 801 executes various functional applications and data processing by running a program stored in the system memory 802, for example, implementing steps of an image processing method provided by the present embodiment, the method including:
acquiring an image to be processed, and performing down-sampling processing on the image to be processed to obtain a down-sampled image;
when the down-sampled image meets a first hierarchical condition, respectively determining pixel codes of a current down-sampled image and an image before down-sampling, and determining an image distance between the current down-sampled image and the image before down-sampling based on the pixel codes;
when the current down-sampled image and the image distance meet the second hierarchical condition, performing iterative down-sampling processing on the current down-sampled image;
and forming an image set with a pyramid structure based on the image to be processed and at least one down-sampled image obtained by down-sampling processing.
Of course, those skilled in the art can understand that the processor can also implement the technical solution of the image processing method provided by any embodiment of the present invention.
EXAMPLE five
Fifth, an embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps of the image processing method provided in any embodiment of the present invention, where the method includes:
acquiring an image to be processed, and performing down-sampling processing on the image to be processed to obtain a down-sampled image;
when the down-sampled image meets a first hierarchical condition, respectively determining pixel codes of a current down-sampled image and an image before down-sampling, and determining an image distance between the current down-sampled image and the image before down-sampling based on the pixel codes;
when the current down-sampled image and the image distance meet the second hierarchical condition, performing iterative down-sampling processing on the current down-sampled image;
and forming an image set with a pyramid structure based on the image to be processed and at least one down-sampled image obtained by down-sampling processing.
Computer storage media for embodiments of the invention may employ any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for embodiments of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (10)
1. An image processing method, comprising:
acquiring an image to be processed, and performing down-sampling processing on the image to be processed to obtain a down-sampled image;
when the down-sampled image meets a first hierarchical condition, respectively determining pixel codes of a current down-sampled image and an image before down-sampling, and determining an image distance between the current down-sampled image and the image before down-sampling based on the pixel codes;
when the current downsampling image and the image distance meet a second hierarchical condition, performing iterative downsampling processing on the current downsampling image;
and forming an image set with a pyramid structure based on the image to be processed and at least one down-sampled image obtained by down-sampling processing.
2. The method of claim 1, further comprising:
and stopping the processing of any down-sampled image when the image distance between any down-sampled image and the image between any down-sampled image and any down-sampled image does not meet the first layering condition and/or the second layering condition.
3. The method according to claim 1 or 2, wherein the first hierarchical condition is that an image size is larger than a preset size, the second hierarchical condition includes that the current downsampled image is not a first downsampled image of the image to be processed, and the image distance is smaller than or equal to a preset distance.
4. The method of claim 1, wherein determining the pixel encodings of the current downsampled image and the pre-downsampled image comprises:
respectively determining the average value of the pixels of the image for the current downsampled image or the image before downsampling;
and comparing the pixel value of each pixel point in the current downsampled image or the image before downsampling with the corresponding image pixel mean value, and determining the code of each pixel point according to the comparison result to obtain the pixel code of the current downsampled image or the image before downsampling.
5. The method of claim 4, wherein determining the coding of each pixel according to the comparison result to obtain the pixel coding of the current downsampled image or the image before downsampling comprises:
when the pixel value of the pixel point is larger than or equal to the image pixel mean value, generating a first code of the pixel point;
when the pixel value of the pixel point is smaller than the image pixel mean value, generating a second code of the pixel point;
and forming the codes of all the pixel points into a coding matrix based on the positions of all the pixel points in the current downsampled image or the image before downsampling to obtain the pixel codes of the current downsampled image or the image before downsampling.
6. The method of claim 4, further comprising, prior to determining the pixel encodings of the current downsampled image and the pre-downsampled image:
respectively carrying out scaling processing on the current downsampling image and the image before downsampling to obtain the current downsampling image and the image before downsampling with the same size;
correspondingly, the determining the pixel codes of the current downsampled image and the image before downsampling comprises the following steps:
and coding the current downsampled image with the same size and the image before downsampling to obtain the pixel code corresponding to each image.
7. The method according to claim 1, wherein the image to be processed is a template image to be registered;
the method further comprises the following steps:
determining the number of layers in the image set of the pyramid structure, and performing layering processing on the image to be searched which is registered with the template image based on the number of layers to obtain the image set of the pyramid structure of the image to be searched;
performing rotation processing on each image in the image set corresponding to the template image to obtain at least one image to be registered corresponding to each pyramid layer;
and performing correlation registration on at least one image to be registered with the same layer number and corresponding images in the image set of the pyramid structure of the image to be searched layer by layer based on the pyramid structure to obtain a target registration area matched with the template image in the image to be searched.
8. An image processing apparatus characterized by comprising:
the image to be processed down-sampling module is used for acquiring an image to be processed and performing down-sampling processing on the image to be processed to obtain a down-sampled image;
the image distance calculation module is used for respectively determining pixel codes of a current downsampled image and an image before downsampling when the downsampled image meets a first hierarchical condition, and determining an image distance between the current downsampled image and the image before downsampling based on the pixel codes;
the current image down-sampling module is used for performing iterative down-sampling processing on the current down-sampled image when the current down-sampled image and the image distance meet a second hierarchical condition;
and the image set forming module is used for forming an image set with a pyramid structure based on the image to be processed and at least one down-sampled image obtained by down-sampling processing.
9. An electronic device, characterized in that the electronic device comprises:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the image processing method as claimed in claims 1-7.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the image processing method as claimed in claims 1 to 7.
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