Patent Publication Number: US-11663819-B2

Title: Image processing method, apparatus, and device, and storage medium

Description:
CROSS-REFERENCE TO RELATED APPLICATION(S) 
     This application is a bypass continuation application of International Application No. PCT/CN2020/094482, filed on Jun. 5, 2020, which claims priority to Chinese Patent Application No. 201910514603.6, filed with the China National Intellectual Property Administration on Jun. 14, 2019, the disclosures of which are incorporated by reference in their entireties. 
    
    
     FIELD 
     The disclosure relates to the field of image processing, and specifically, to an image processing method, apparatus, and device, and a storage medium. 
     BACKGROUND 
     A computing device may process pixel information in an image to obtain semantic information contained in the image. For example, image classification processing may be performed on the image to determine whether content contained in the image belongs to a preset category. In another example, image segmentation processing may be performed on the image to recognize a specific region in the image. In still another example, object detection processing may be performed on the image to recognize a specific target object included in the image, and a category to which the target object belongs may be further outputted. By using the foregoing various image processing methods, the computing device may extract various semantic information in the image. 
     SUMMARY 
     An objective of the disclosure is to provide an image processing method, apparatus, and device, and a storage medium. 
     According to an aspect of the disclosure, an image processing method is provided, performed by a computing device, the method including: receiving an input image; determining a first image feature of a first size of the input image, the first image feature having at least two channels; determining a first weight adjustment parameter corresponding to the first image feature, and performing weight adjustment on each channel in the first image feature by using the first weight adjustment parameter, to obtain an adjusted first image feature, the first weight adjustment parameter including at least two parameter components, and each parameter component being used for adjusting a pixel of a channel corresponding to each parameter component; downsampling the adjusted first image feature to obtain a second image feature having a second size, the second size being smaller than the first size; combining the first image feature and the second image feature to obtain a combined image feature; and determining an image processing result according to the combined image feature. 
     According to another aspect of the disclosure, an image processing apparatus is further provided, including: a receiving unit, configured to receive an input image; a feature determining unit, configured to determine a first image feature of a first size of the input image, the first image feature having at least two channels; an adjustment unit, configured to determine a first weight adjustment parameter corresponding to the first image feature, and perform weight adjustment on each channel in the first image feature by using the first weight adjustment parameter, to obtain an adjusted first image feature, the first weight adjustment parameter including at least two parameter components, and each parameter component being used for adjusting a pixel of a channel corresponding to each parameter component; a downsampling unit, configured to downsample the adjusted first image feature to obtain a second image feature having a second size, the second size being smaller than the first size; a combining unit, configured to combine the first image feature and the second image feature to obtain a combined image feature; and a result determining unit, configured to determine an image processing result according to the combined image feature. 
     According to still another aspect of the disclosure, an image processing device is further provided, including a memory and a processor, the memory storing instructions, the instructions, when executed by the processor, causing the processor to perform the foregoing method. 
     According to still another aspect of the disclosure, a non-transitory computer-readable storage medium is further provided, storing instructions, the instructions, when executed by a processor, causing the processor to perform the foregoing method. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       To describe the technical solutions in example embodiments of the disclosure more clearly, the following briefly describes accompanying drawings for describing the example embodiments. The accompanying drawings in the following description show merely some embodiments of the disclosure, and a person of ordinary skill in the art may still derive other drawings from these accompanying drawings without creative efforts. The following accompanying drawings are not deliberately drawn to scale according to the actual size, and a focus is demonstrating the main idea of the disclosure. 
         FIG.  1    is an example scenario diagram of an image processing system according to the disclosure. 
         FIG.  2    is a schematic flowchart of an image processing method according to an embodiment of the disclosure. 
         FIG.  3 A  shows a schematic process of performing weight adjustment on a first image feature according to an embodiment of the disclosure. 
         FIG.  3 B  shows another schematic process of performing weight adjustment on a first image feature according to an embodiment of the disclosure. 
         FIG.  4 A  shows an example process of downsampling according to an embodiment of the disclosure. 
         FIG.  4 B  shows an example process of upsampling according to an embodiment of the disclosure. 
         FIG.  5 A  shows an example process of an image processing process according to an embodiment of the disclosure. 
         FIG.  5 B  shows another example process of an image processing process according to an embodiment of the disclosure. 
         FIG.  6 A  shows an example method for determining an image segmentation result according to an embodiment of the disclosure. 
         FIG.  6 B  shows an example method for determining an image classification result according to an embodiment of the disclosure. 
         FIG.  6 C  shows an example method for determining an object detection result according to an embodiment of the disclosure. 
         FIG.  7    is a schematic block diagram of an image processing apparatus according to an embodiment of the disclosure. 
         FIG.  8    shows an architecture of a computing device according to an embodiment of the disclosure. 
         FIG.  9    shows a schematic use scenario according to an embodiment of the disclosure. 
     
    
    
     DETAILED DESCRIPTION 
     To make the objectives, technical solutions, and advantages of the embodiments of the disclosure more comprehensible, the following clearly and completely describes the technical solutions in the embodiments of the disclosure with reference to the accompanying drawings in the embodiments of the disclosure. The described embodiments are a part rather than all of the embodiments of the disclosure. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments of the disclosure without creative efforts shall fall within the protection scope of the disclosure. 
     Unless otherwise defined, the technical terms or scientific terms used herein are to have general meanings understood by a person of ordinary skill in the field of the disclosure. The “first”, the “second”, and similar terms used in the disclosure do not indicate any order, quantity or significance, but are used to only distinguish different components. Similarly, “include”, “including”, or similar terms mean that elements or items appearing before the term cover elements or items listed after the term and their equivalents, but do not exclude other elements or items. A similar term such as “connect” or “connection” is not limited to a physical or mechanical connection, but may include an electrical connection, whether direct or indirect. “Up”, “down”, “left”, “right”, and the like are merely used for indicating relative positional relationships. When absolute positions of described objects change, the relative positional relationships may correspondingly change. 
       FIG.  1    is an example scenario diagram of an image processing system according to the disclosure. As shown in  FIG.  1   , an image processing system  100  may include a user terminal  110 , a network  120 , a server  130 , and a database  140 . 
     The user terminal  110  may be, for example, a computer  110 - 1  and a mobile phone  110 - 2  shown in  FIG.  1   . It would be understood that the user terminal  110  may be any other type of electronic device that may perform data processing, and may include, but not limited to, a desktop computer, a notebook computer, a tablet computer, a smartphone, a smart home device, a wearable device, an in-vehicle electronic device, and a monitoring device. The user terminal may alternatively be any equipment provided with an electronic device, for example, a vehicle or a robot. 
     The user terminal  110  provided according to the disclosure may be used for receiving an image, and implementing image processing by using a method provided in the disclosure. For example, the user terminal  110  may capture an image to be processed through an image capture device (for example, a camera or a video camera) disposed on the user terminal  110 . In another example, the user terminal  110  may alternatively receive an image to be processed from an image capture device disposed independently. In still another example, the user terminal  110  may alternatively receive an image to be processed from a server through a network. An image described herein may be an individual image or a frame in a video. 
     In some embodiments, an image processing method provided in the disclosure may be performed by using a processing unit of the user terminal  110 . In some implementations, the user terminal  110  may perform the image processing method by using an application program built in the user terminal  110 . In some other implementations, the user terminal  110  may perform the image processing method by calling an application program stored outside the user terminal  110 . 
     In some other embodiments, the user terminal  110  sends a received image to the server  130  through the network  120 , and the server  130  performs the image processing method. In some implementations, the server  130  may perform the image processing method by using an application program built in the server. In some other implementations, the server  130  may perform the image processing method by calling an application program stored outside the server. 
     The network  120  may be a single network or a combination of at least two different networks. For example, the network  120  may include, but not limited to, one of or a combination of more than one of a local area network, a wide area network, a public network, a private network, and the like. 
     The server  130  may be an individual server or a server cluster, and servers in the cluster are connected by a wired or wireless network. The server cluster may be centralized, for example, a data center, or distributed. The server  130  may be local or remote. 
     The database  140  may generally refer to a device having a storage function. The database  140  is mainly used for storing various data used, generated, and outputted during operating of the user terminal  110  and the server  130 . The database  140  may be local or remote. The database  140  may include various memories, for example, a random access memory (RAM) and a read-only memory (ROM). The storage devices mentioned above are only some listed examples, and storage devices that may be used by the system are not limited thereto. 
     The database  140  may connect to or communicate with the server  130  or a part thereof through the network  120 , or directly connect to or communicate with the server  130 , or a combination of the foregoing two manners is used. 
     In some embodiments, the database  140  may be an independent device. In some other embodiments, the database  140  may be alternatively integrated in at least one of the user terminal  110  and the server  130 . For example, the database  140  may be disposed on the user terminal  110  or may be disposed on the server  130 . In another example, the database  140  may alternatively be distributed, a part thereof is disposed on the user terminal  110 , and another part is disposed on the server  130 . 
     The system provided in  FIG.  1    may be used to implement image processing on an image, such as image classification, image segmentation, and target detection. 
     A process of an image processing method provided in the disclosure is described below in detail. 
       FIG.  2    is a schematic flowchart of an image processing method according to an embodiment of the disclosure. The method shown in  FIG.  2    may be performed by a computing device shown in  FIG.  8   . 
     In operation S 202 , an input image may be received. In some embodiments, an image capture device may be used to capture the input image. In some other embodiments, the input image may be alternatively read from a storage device that stores pictures. 
     The input image described herein may contain various image information. For example, the input image may be an image and/or video in a driving process acquired by an in-vehicle image capture device. In another example, the input image may be a surveillance image and/or surveillance video acquired by a monitoring device. In still another example, the input image may be an image generated by a medical instrument by using CT, MRI, ultrasound, X-ray, electrocardiogram, electroencephalogram, optical photography, or the like. 
     In some embodiments, the input image may be black and white, or color. For example, when the input image is a black and white image, the input image may have a single channel. In another example, when the input image is a color image, the input image may have at least two channels (such as R, G, and B). 
     In operation S 204 , a first image feature of a first size of the input image may be determined, the first image feature having at least two channels. 
     The first image feature may have the same size as that of the input image, or may have a size different from that of the input image. 
     In some embodiments, the first image feature may be determined by performing convolution processing on the input image. A specific form of performing convolution processing on the input image is not limited in the disclosure herein. For example, modules of a VGG series network, a Resnet series network and/or an Inception series network may be used to perform convolution processing on the input image at least once to obtain the first image feature. In the disclosure, a Resnet module is used as an example to explain the principle of the disclosure. However, it would be understood that any one of the foregoing network modules or any other network module capable of extracting image features may be used to replace the Resnet module in the disclosure. 
     In operation S 206 , a first weight adjustment parameter used for the first image feature may be determined, and weight adjustment is performed on each channel in the first image feature by using the first weight adjustment parameter, to obtain an adjusted first image feature, the first weight adjustment parameter including at least two parameter components, and each parameter component being used for adjusting a pixel of a channel corresponding to each parameter component. 
       FIG.  3 A  shows a schematic process of performing weight adjustment on a first image feature according to an embodiment of the disclosure. 
     As shown in  FIG.  3 A , the first image feature may be a tensor U with a size of H×W and a quantity of channels of C. H and W may be sizes with pixel quantities as units, and C is a positive integer. A squeeze operation (F sq ) may be performed on the first image feature U to convert the tensor with the size of H×W and the quantity of channels of C into an output vector z with a size of 1×1×C. The output vector z includes C elements of z 1 , z 2 , . . . , and z c . A k th  element z k  in the output vector z may be used to represent a global feature parameter of a k th  channel of the first image feature. For each channel in the at least two channels in the first image feature, the F sq  operation may be used to determine a global feature parameter of each channel. In some embodiments, the F sq  operation may be implemented as global average pooling, and the F sq  operation may be implemented by using the following formula: 
     
       
         
           
             
               
                 
                   
                     
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                               j 
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                             H 
                           
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                               u 
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     where W represents a quantity of pixels of the first image feature in a first direction (for example, a row direction), H represents a quantity of pixels of the first image feature in a second direction (for example, a column direction), and u k  (i, j) is a value of an element in an i th  row and j th  column on the k th  channel. z k  represents the k th  element of the output vector z. 
     As can be seen, the F sq  operation is implemented by using the formula (1) to obtain the distribution of elements of the image feature on each channel, that is, the global information of the image feature on each channel. 
     Then, the global feature parameter of each channel may be used to determine a parameter component used for adjusting a pixel of a channel corresponding to the parameter component. 
     For example, an excitation operation (F ex ) may be performed on the output vector z. According to the F ex  operation, the first weight adjustment parameter used for each of the at least two channels may be determined by using the global feature parameter. In some embodiments, the F ex  operation may include first multiplying a matrix W1 with a dimension of C/r×C by z to implement an operation of a fully connected layer. C is a quantity of elements of the output vector z, and r is a scaling parameter. In an implementation, r may take a value of 16. Then, an output of the operation of the fully connected layer may be caused to pass through a ReLU (rectified linear unit) layer. Further, an output of the ReLU layer may be multiplied by a matrix W2 with a dimension of C×C/r (which is equivalent to an operation of a fully connected layer), and finally the output processed by the matrix W2 is caused to pass through a sigmoid function to obtain an output s. 
     The output s obtained in the foregoing manner may represent a weight of a feature map of each channel in the tensor U of the first image feature. The output s has C elements of s 1 , s 2 , . . . , and s c , and a value of a k th  element of the output s represents a parameter component of the k th  channel in the first image feature. 
     By using the output s, weight adjustment may be performed on the first image feature U. For example, for the k th  channel in the first image feature U, values of all elements in the k th  channel may be multiplied by the value of the k th  element in the output s to obtain an adjusted feature of the k th  channel. By performing the foregoing weight adjustment on each channel in the first image feature U, the adjusted first image feature may be obtained. 
       FIG.  3 B  shows another schematic process of performing weight adjustment on a first image feature according to an embodiment of the disclosure. When the Resnet module is used to process the input image to determine the image feature, the weight adjustment process shown in  FIG.  3 A  may be combined with the Resnet module. 
     As shown in  FIG.  3 B , a residual module may be used to process the first image feature U, where the first image feature U may be a tensor with a size of H×W and a quantity of channels of C. H and W may be sizes with pixel quantities as units, and C is a positive integer. 
     Then, the f sq  operation (the global pooling layer in  FIG.  3 B ) and the F ex  operation (the fully connected layer implemented by using the matrix W1, the ReLU layer, the fully connected layer implemented by the matrix W2, and the sigmoid layer that are included in  FIG.  3 B ) may be performed on the first image feature U processed by the residual module, to determine the first weight adjustment parameter used for at least two channels of the first image feature U. 
     As shown in  FIG.  3 B , by using the residual module to process the first image feature U, a residual δ of the first image feature U may be obtained. Further, the residual δ may be scaled by using the first weight adjustment parameter outputted by the sigmoid layer, to obtain an adjusted residual δ′. The adjusted residual δ′ is added to the inputted first image feature U to obtain a result Ũ after the addition. By equating Ũ with the inputted first image feature U, the module shown in  FIG.  3 B  may implement the function of the Resnet module. Therefore, by using the structure shown in  FIG.  3 B , the weight adjustment process may be added to the Resnet module. 
     Referring back to  FIG.  2   , in operation S 208 , the adjusted first image feature may be downsampled to obtain a second image feature having a second size, the second size being smaller than the first size. The second image feature has at least two channels, and a quantity of channels of the second image feature is greater than a quantity of channels of the first image feature. 
     For M×N first pixels in the adjusted first image feature, a first pixel vector corresponding to the M×N first pixels is determined, the first pixel vector including elements of the M×N first pixels in the at least two channels, M and N being positive integers, and a product of M and N being a positive integer greater than 1. Then, the first pixel vector may be mapped by using a full-rank first matrix to obtain the mapped first pixel vector. Further, a second pixel in the second image feature may be determined according to the mapped first pixel vector. A quantity of channels of the second image feature may be determined according to a quantity of elements of the mapped first pixel vector. For example, the quantity of channels of the second image feature may be equal to the quantity of elements of the mapped first pixel vector. 
       FIG.  4 A  shows an example process of downsampling according to an embodiment of the disclosure. In  FIG.  4 A , the principle of the disclosure is described by using an example in which the adjusted first image feature is a tensor with a size of H×W and a quantity of channels of C. H and W may be sizes with pixel quantities as units, and C is a positive integer. 
     As shown in  FIG.  4 A , a process of 2× downsampling is described by using four pixels in a 2×2 arrangement in the adjusted first image feature as an example. For a region  1  in  FIG.  4 A , by determining values of elements of the 2×2 pixels on the C channels, the 2×2×C elements may be arranged into a one-dimensional vector with a size of 1×1×4C, that is, a pixel vector used for the 2×2 pixels. Then, a first matrix with a size of 4C×4C may be used to map the pixel vector of the 2×2 pixels, to obtain a new mapped pixel vector with a size of 1×4C. Then, by using the mapped pixel vector with the size of 1×4C, a pixel with an element of 4C channels may be obtained, that is, a pixel in the image feature obtained after downsampling. 
     According to the process shown in  FIG.  4 A , after sequentially processing other pixels in the adjusted first image feature (for example, each pixel in a region  2 , a region  3 , and a region  4  shown in  FIG.  4 A ), a tensor with a size of H/2×W/2 and a quantity of channels of 4C may be obtained as a second image feature obtained by downsampling the adjusted first image feature. 
     As can be seen, the parameter matrix used in the downsampling process provided in the disclosure is a square matrix with a size of N×N (in the example of  FIG.  4 A , N=4C), so that the mapping used in the downsampling process is a full-rank transformation, thereby transferring the image feature without loss of information. In the example shown in  FIG.  4 A , if a rank of the parameter matrix is less than 4C, loss of image information is caused in the mapping process. On the contrary, if the rank of the parameter matrix is greater than 4C, a quantity of parameters in the mapping process increases, but an amount of information in the image does not increase. Therefore, the full-rank matrix is used to implement the parameter transformation in the downsampling process, to transfer the image information with a small quantity of parameters without losing information. 
     Although  FIG.  4 A  only uses 2× downsampling as an example to explain the principle of the disclosure, it would be understood that a person skilled in the art may choose the ratio of the downsampling as appropriate. For example, 3×3 pixels, 1×2 pixels, 2×1 pixels, or any other at least two pixels may be determined in the adjusted first image feature, and at least two pixels in the adjusted first image feature are mapped to one pixel in the second image feature by using the method shown in  FIG.  4 A , to implement the downsampling process. Without departing from the principle of the disclosure, a person skilled in the art may arbitrarily determine the ratio of downsampling. 
     Referring back to  FIG.  2   , in operation S 210 , the first image feature and the second image feature may be combined to obtain a combined image feature. 
     In some embodiments, operation S 210  may further include: determining a third weight adjustment parameter used for the adjusted first image feature, and performing weight adjustment on each channel in the adjusted first image feature by using the third weight adjustment parameter, to obtain the further adjusted first image feature, the third weight adjustment parameter including at least two parameter components, and each parameter component being used for adjusting a pixel of a channel corresponding to each parameter component. The weight adjustment method shown in  FIG.  3 A  and  FIG.  3 B  may be used to perform weight adjustment on the adjusted first image feature. Then, the further adjusted first image feature may be downsampled to obtain a third image feature having the second size. The second image feature and the third image feature are combined to obtain a fourth image feature having the second size. The weight adjustment method shown in  FIG.  3 A  and  FIG.  3 B  may be used to perform weight adjustment on the fourth image feature. For example, a fourth weight adjustment parameter used for the fourth image feature may be determined, and weight adjustment is performed on each channel in the fourth image feature by using the fourth weight adjustment parameter, to obtain an adjusted fourth image feature, the fourth weight adjustment parameter including at least two parameter components, and each parameter component being used for adjusting a pixel of a channel corresponding to each parameter component. Further, the adjusted fourth image feature may be downsampled to obtain a fifth image feature having a third size. 
     In this case, the first image feature, the second image feature, and the fifth image feature may be combined to obtain the combined image feature. 
     As can be seen, by using the result outputted in operation S 208 , image features of different scales representing the input image information may be determined. By combining the foregoing image features of different scales in various manners, different image processing effects may be achieved. 
     For example, the foregoing image features of different scales may be used to implement image segmentation processing on the input image, thereby dividing the image into several specific regions with different properties. For example, a medical image is used as an example, the image segmentation method may be used to distinguish regions of different properties in the input image (for example, normal regions and diseased regions). 
     In another example, the foregoing image features of different scales may be used to implement image classification processing on the input image. The image classification method may be used to determine whether the image belongs to a specific preset category. 
     In still another example, the foregoing image features of different scales may be used to implement object detection on a specific target in the input image. For example, the object detection method may be used to determine whether the image contains a specific target object and a position of the target object in the image. 
     In an image segmentation algorithm, the second image feature may be upsampled to obtain the upsampled second image feature. Then, the first image feature and the upsampled second image feature may be concatenated in a channel direction to obtain the combined image feature. 
     In an implementation, the process of upsampling the second image feature may include performing weight adjustment on the second image feature, and upsampling the adjusted second image feature. For example, the process shown in  FIG.  3 A  and  FIG.  3 B  may be used to determine a second weight adjustment parameter used for the second image feature, and perform weight adjustment on each channel in the second image feature by using the second weight adjustment parameter, to obtain an adjusted second image feature, the second weight adjustment parameter including at least two parameter components, and each parameter component being used for adjusting a pixel of a channel corresponding to each parameter component. Then, the adjusted second image feature may be upsampled to obtain the upsampled second image feature, the upsampled second image feature having the first size. 
     In some embodiments, for a third pixel in the adjusted second image feature, a second pixel vector corresponding to the third pixel is determined, the second pixel vector including elements of the third pixel in the at least two channels. Then, the second pixel vector may be mapped by using a full-rank second matrix to obtain the mapped second pixel vector. Further, the mapped second pixel vector may be rearranged to determine at least two fourth pixels in the upsampled second image feature. 
       FIG.  4 B  shows an example process of upsampling according to an embodiment of the disclosure. In  FIG.  4 B , the principle of the disclosure is described by using an example in which the adjusted second image feature is a tensor with a size of H×W and a quantity of channels of C. H and W may be sizes with pixel quantities as units, and C is a positive integer. 
     As shown in  FIG.  4 B , a process of 2× upsampling is described by using a pixel in the adjusted second image feature as an example. For the pixel in the adjusted second image feature, values of elements of the pixel on the C channels may be determined. The C elements may be arranged into a one-dimensional vector with a size of 1×1×C, that is, a pixel vector of the pixel. Then, a second matrix with a size of C×C may be used to map the pixel vector of the pixel, to obtain a new mapped pixel vector with a size of 1×C. Then, by rearranging the mapped pixel vector with the size of 1×C, 2×2 pixels with an element of C/4 channels may be obtained, that is, 2×2 pixels in the image feature obtained after upsampling. 
     According to the process shown in  FIG.  4 B , after sequentially processing pixels in the adjusted second image feature, a tensor with a size of 2H×2W and a quantity of channels of C/4 may be obtained as an upsampled second image feature obtained by upsampling the adjusted second image feature. 
     As can be seen, the parameter matrix used in the upsampling process provided in the disclosure is a square matrix with a size of N×N (in the example of  FIG.  4 B , N=C), so that the mapping used in the upsampling process is a full-rank transformation, thereby transferring the image feature without loss of information. In the example shown in  FIG.  4 B , if a rank of the parameter matrix is less than 4C, loss of image information is caused in the mapping process. On the contrary, if the rank of the parameter matrix is greater than 4C, a quantity of parameters in the mapping process increases, but an amount of information in the image does not increase. Therefore, the full-rank matrix is used to implement the parameter transformation in the upsampling process, to transfer the image information with a small quantity of parameters without losing information. 
     Although  FIG.  4 B  only uses 2× upsampling as an example to explain the principle of the disclosure, it would be understood that a person skilled in the art may choose the ratio of the upsampling as appropriate. For example, one pixel in the adjusted second image feature may be mapped to 3×3 pixels, 1×2 pixels, 2×1 pixels, or any other at least two pixels in the upsampled second image feature to implement the upsampling process. Without departing from the principle of the disclosure, a person skilled in the art may arbitrarily determine the ratio of upsampling. 
     In an image classification algorithm, the combined image feature may be determined through the following operations. In some embodiments, a third weight adjustment parameter used for the adjusted first image feature may be determined, and weight adjustment is performed on each channel in the adjusted first image feature by using the third weight adjustment parameter, to obtain the further adjusted first image feature, the third weight adjustment parameter including at least two parameter components, and each parameter component being used for adjusting a pixel of a channel corresponding to each parameter component. Then, the further adjusted first image feature may be downsampled to obtain a third image feature having the second size. Further, an addition operation may be performed on the second image feature and the third image feature to determine the combined image feature. 
     In an implementation, the weight adjustment process shown in  FIG.  3 A  and  FIG.  3 B  may be used to adjust the first image feature, and the process shown in  FIG.  4 A  may be used to downsample the further adjusted first image feature. Details are not repeated herein. 
     In an object detection algorithm, the combined image feature may be determined through the following operations. In some embodiments, a third weight adjustment parameter used for the adjusted first image feature may be determined, and weight adjustment is performed on each channel in the adjusted first image feature by using the third weight adjustment parameter, to obtain the further adjusted first image feature, the third weight adjustment parameter including at least two parameter components, and each parameter component being used for adjusting a pixel of a channel corresponding to each parameter component. Then, the further adjusted first image feature may be downsampled to obtain a third image feature having the second size. Further, the second image feature and the third image feature may be concatenated in a channel direction to determine the combined image feature. 
     In an implementation, the weight adjustment process shown in  FIG.  3 A  and  FIG.  3 B  may be used to adjust the first image feature, and the process shown in  FIG.  4 A  may be used to downsample the further adjusted first image feature. Details are not repeated herein. 
     Referring back to  FIG.  2   , in operation S 212 , an image processing result may be determined according to the combined image feature. 
     As described above, for different image processing methods, operation S 210  may combine image features of image information with different scales in different manners. Therefore, by using the image feature outputted in operation S 210 , various image processing results may be obtained in different processing manners. 
     An example is used in which image segmentation is performed on the input image. Operation S 212  may include performing convolution processing on the combined image feature to determine an image segmentation result used for the input image. 
     An example is used in which image classification is performed on the input image. Operation S 212  may include performing convolution processing, global pooling, and full connection on the combined image feature to determine an image classification result used for the input image. 
     An example is used in which object detection is performed on the input image. Operation S 212  may include performing convolution processing, full connection, and rearrangement on the combined image feature to determine an object detection result used for the input image. 
     By using the image processing method provided in the disclosure, the first weight adjustment parameter used for the first image feature is determined according to the elements in the at least two channels of the first image feature, and weight adjustment is performed on the at least two channels in the first image feature by using the first weight adjustment parameter to obtain an adjusted first image feature, so that finer channel features may be obtained, and a better image processing result may be further obtained. Further, by combining the first image feature and the second image feature of different sizes, picture information of different scales may be exchanged to obtain comprehensive image information, thereby obtaining a better image processing result. Furthermore, full-rank matrices are used to transform pixel information, to transfer the image information with a small quantity of parameters without losing information. 
     In the image processing method provided in  FIG.  2   , only the first size, the second size, and the third size are used as an example to explain the principle of the disclosure. However, it would be understood that a person skilled in the art may further downsample the image feature of the third size according to an actual condition, to obtain an image feature of a smaller size, and further combine image information of different scales to generate a final image processing result. 
       FIG.  5 A  shows an example process of an image processing process according to an embodiment of the disclosure. By using a two-layer structure shown in  FIG.  5 A , information of image features of two different sizes may be fused for subsequent image processing. 
     In  FIG.  5 A , black color is used to indicate an input image  501 , white color is used to indicate an image feature processed by a convolution processing module (for example, a Resnet module), gray color is used to indicate an image feature processed by weight adjustment, and a downward arrow indicates downsampling on the image features. For example, the methods shown in  FIG.  3 A  and  FIG.  3 B  may be used to perform weight adjustment on the image features. The method shown in  FIG.  4 A  may be used to downsample the image features. 
     As shown in  FIG.  5 A , for the input image  501  of a first size, the input image  501  may be processed by using, for example, the Resnet module to obtain an image feature  502 . Then, weight adjustment may be performed on the image feature  502  to obtain an image feature  503 . 
     As shown in  FIG.  5 A , the image feature  503  may be downsampled by using, for example, the method shown in  FIG.  4 A  to obtain an image feature  504  of a second size. 
     Then, at least one time of convolution processing and weight adjustment may be performed on each of the image feature  503  of the first size and the image feature  504  of the second size, to obtain an image feature  505  of the first size and an image feature  506  of the second size. 
     Therefore, by using the process shown in  FIG.  5 A , at least the image feature  505  of the first size and the image feature  506  of the second size may be outputted for a subsequent image processing process (for example, operation S 210  and operation S 212  described in  FIG.  2   ). 
       FIG.  5 B  shows an exemplary process of an image processing process according to an embodiment of this application. By using a three-layer structure shown in  FIG.  5 B , information of image features of three different sizes may be fused for subsequent image processing. 
     In  FIG.  5 B , black color is used to indicate an input image  501 , white color is used to indicate represents an image feature processed by a convolution processing module (for example, a Resnet module), gray color is used to indicate an image feature processed by weight adjustment, an upward arrow indicates upsampling on the image features, and a downward arrow indicates downsampling on the image features. For example, the methods shown in  FIG.  3 A  and  FIG.  3 B  may be used to perform weight adjustment on the image features. The method shown in  FIG.  4 A  may be used to downsample the image features, and the method shown in  FIG.  4 B  may be used to upsample the image features. 
     As shown in  FIG.  5 B , for the input image  501  of a first size, the input image  501  may be processed by using, for example, the Resnet module to obtain an image feature  502 . Then, weight adjustment may be performed on the image feature  502  to obtain an image feature  503 . 
     As shown in  FIG.  5 B , the image feature  503  may be downsampled by using, for example, the method shown in  FIG.  4 A  to obtain an image feature  504  of a second size. 
     Then, at least one time of convolution processing and weight adjustment may be performed on each of the image feature  503  of the first size and the image feature  504  of the second size, to obtain an image feature  505  of the first size and an image feature  506  of the second size. 
     Further, the image feature  506  of the second size may be upsampled, and the upsampled image feature  506  of the first size and the image feature  505  of the first size may be added to obtain an image feature  507  of the first size. In addition, downsampling and weight adjustment may be performed on the image feature  505  of the first size, and the downsampled image feature  505  and the image feature  506  may be added to obtain an image feature  508  of the second size. 
     Further, the image feature  508  of the second size obtained after the weight adjustment may be downsampled to obtain an image feature  509  of a third size. 
     Then, at least one time of convolution processing and weight adjustment may be performed on the image feature  507  of the first size, the image feature  508  of the second size, and the image feature  509  of the third size, to obtain an image feature  510  of the first size, an image feature  511  of the second size, and an image feature  512  of the third size. 
     Then, the image feature  510  of the first size, the image feature  511  of the second size after one time of upsampling, and the image feature  512  of the third size after two times of upsampling may be added to obtain an image feature  513  of the first size. The image feature  510  of the first size after one time of downsampling, the image feature  511  of the second size, and the image feature  512  of the third size after one time of upsampling may be added to obtain an image feature  514  of the second size. The image feature  510  of the first size after two times of downsampling, the image feature  511  of the second size after one time of downsampling, and the image feature  512  of the third size may be added to obtain an image feature  515  of the third size. 
     Then, at least one time of convolution processing and weight adjustment may be performed on the image feature  513  of the first size, the image feature  514  of the second size, and the image feature  515  of the third size, to respectively obtain an output image feature  516  of the first size, an output image feature  517  of the second size, and an output image feature  518  of the third size. 
     Therefore, by using the process shown in  FIG.  5 B , at least the image feature  516  of the first size, the image feature  517  of the second size, and the image feature  518  of the third size may be outputted for a subsequent image processing process (for example, operation S 210  and operation S 212  described in  FIG.  2   ). 
     An example of a quantity of channels of each module in a neural network shown in  FIG.  5 B  is given in Table 1. It would be understood that, without departing from the principles disclosed in the disclosure, a person skilled in the art may adjust, according to a condition, a quantity of channels used by each module. 
     
       
         
           
               
               
               
             
               
                   
                 TABLE 1 
               
               
                   
                   
               
               
                   
                   
                 Quantity of 
               
               
                   
                 Module number 
                 channels 
               
               
                   
                   
               
             
            
               
                   
               
            
           
           
               
               
               
            
               
                   
                 Res1_1 
                 16 
               
               
                   
                 SE1_1 
                 16 
               
               
                   
                 Res1_2 
                 16 
               
               
                   
                 Res2_1 
                 64 
               
               
                   
                 Res1_3 
                 32 
               
               
                   
                 Res2_2 
                 128 
               
               
                   
                 SE1_2 
                 32 
               
               
                   
                 SE2_1 
                 128 
               
               
                   
                 Res1_4 
                 32 
               
               
                   
                 SE2_2 
                 128 
               
               
                   
                 Res1_5 
                 32 
               
               
                   
                 Res2_3 
                 128 
               
               
                   
                 Res3_1 
                 512 
               
               
                   
                 SE1_3 
                 64 
               
               
                   
                 SE2_3 
                 256 
               
               
                   
                 SE3_1 
                 1024 
               
               
                   
                 Res1_6 
                 64 
               
               
                   
                 Res2_4 
                 256 
               
               
                   
                 Res3_2 
                 1024 
               
               
                   
                 SE1_4 
                 64 
               
               
                   
                 SE2_4 
                 256 
               
               
                   
                 SE3_2 
                 1024 
               
               
                   
                   
               
            
           
         
       
     
       FIG.  6 A  shows an example method for determining an image segmentation result according to an embodiment of the disclosure. 
     As shown in  FIG.  6 A , an output image feature  610  of the first size, an output image feature  620  of the second size, and an output image feature  630  of the third size may be obtained by using the network structure shown in  FIG.  5 B . The output image feature  620  of the second size may be upsampled to obtain the upsampled output image feature  620  of the first size, and the output image feature  630  of the third size may be upsampled to obtain the upsampled output image feature  630  of the first size. Then, the output image feature  610  of the first size, the upsampled output image feature  620 , and the upsampled output image feature  630  may be concatenated in a channel direction to obtain a combined image feature  640 . Further, a convolution layer may be used to perform convolution processing on the combined image feature  640 , and the combined image feature after the convolution processing may be further processed by a sigmoid layer to obtain a final image segmentation result. 
     Table 2 shows quantities of channels of modules of a neural network used during image segmentation processing to determine the segmentation result. The image feature  610  of the first size has 64 channels. The image feature  620  of the second size has 256 channels. The image feature  620  is upsampled to the first size by using the process shown in  FIG.  4 B , so that the upsampled image feature  620  has 256/4=64 channels. Similarly, the image feature  630  of the third size is upsampled to the first size, so that the upsampled image feature  630  also has 256/4=64 channels. Therefore, a concatenating layer has 64+64+64=192 channels. 
     
       
         
           
               
               
               
             
               
                   
                 TABLE 2 
               
               
                   
                   
               
               
                   
                   
                 Quantity of 
               
               
                   
                 Module number 
                 channels 
               
               
                   
                   
               
             
            
               
                   
               
            
           
           
               
               
               
            
               
                   
                 SE1_4 
                 64 
               
               
                   
                 SE2_4 
                 256 
               
               
                   
                 SE3_2 
                 1024 
               
               
                   
                 Concat 
                 192 
               
               
                   
                   
               
            
           
         
       
     
     In some embodiments, an output image during image segmentation processing has the same size as that of an input image, and a pixel value of each pixel in the output image represents a probability that the pixel belongs to a predetermined region. In an implementation, the probability that the pixel belongs to the predetermined region may be represented as a probability between 0 and 1. When the probability that the pixel belongs to the predetermined region is greater than a preset threshold (for example, 0.5), it may be considered that the pixel belongs to the predetermined region. Correspondingly, when the probability that the pixel belongs to the predetermined region is less than a preset threshold (for example, 0.5), it may be considered that the pixel does not belong to the predetermined region. 
     In some embodiments, during the image segmentation processing, an Adam-based gradient descent method may be used to update parameters of a network provided in the disclosure. An initial learning rate may be preset to 0.05, and a betas parameter in Adam may be preset to (0.95, 0.9995). A training image in a preset training set may be processed by using the network provided in the disclosure to obtain a training image segmentation result, and a result of processing the training image may be used to determine a loss function of an image segmentation processing task. 
     In an implementation, a dice index may be used as the loss function of the image segmentation processing task. The dice index may be used to indicate an overlapping degree between an image segmentation result obtained through processing of the neural network and a real image segmentation result. The real image segmentation result may be obtained by manually segmenting a training image in the training set. In some embodiments, the following formula may be used to calculate the dice index: 
     
       
         
           
             Dice 
             = 
             
               2 
               ⁢ 
               
                 
                   
                     V 
                     seg 
                   
                   ⋂ 
                   
                     V 
                     gt 
                   
                 
                 
                   
                     V 
                     seg 
                   
                   + 
                   
                     V 
                     gt 
                   
                 
               
             
           
         
       
     
     where V seg  represents the image segmentation result obtained through processing of the neural network, and V gt  represents the real image segmentation result. 
     By minimizing the loss function, an error gradient may be calculated, and a gradient of the network may be updated through backward propagation to complete the training of the neural network. 
       FIG.  6 B  shows an example method for determining an image classification result according to an embodiment of the disclosure. 
     As shown in  FIG.  6 B , an output image feature  610  of the first size, an output image feature  620  of the second size, and an output image feature  630  of the third size may be obtained by using the network structure shown in  FIG.  5 B . 
     In some embodiments, the output image feature  610  of the first size may be downsampled to obtain the downsampled output image feature  610  of the second size. Then, the output image feature  620  of the second size and the downsampled output image feature  610  may be added to obtain an image feature  660 . For example, pixel values of corresponding pixels in the output image feature  620  of the second size and the downsampled output image feature  610  may be added to obtain the image feature  660  in which image information of the output image feature  610  and that of the output image feature  620  are fused. Further, the image feature  660  may be downsampled to obtain the downsampled image feature  660  of the third size. Then, the output image feature  630  of the third size and the downsampled image feature  660  may be added to obtain a combined image feature  670 . Further, convolution processing may be performed on the combined image feature  670  to obtain an image feature  680 , and the image feature  680  is further processed by using a global pooling layer and a fully connected layer, to obtain a final image classification result. 
     In some embodiments, the image classification result may be represented as a vector of 1×K, where K represents a preset quantity of categories. A value of each element in the image classification result represents a probability that the image belongs to a corresponding category. 
     Table 3 shows quantities of channels of modules of a neural network used during image classification processing to determine the classification result. 
     
       
         
           
               
               
               
             
               
                   
                 TABLE 3 
               
               
                   
                   
               
               
                   
                   
                 Quantity of 
               
               
                   
                 Module number 
                 channels 
               
               
                   
                   
               
             
            
               
                   
               
            
           
           
               
               
               
            
               
                   
                 SE1_4 
                 64 
               
               
                   
                 SE2_4 
                 256 
               
               
                   
                 SE3_2 
                 1024 
               
               
                   
                 SE2_5 
                 256 
               
               
                   
                 SE3_3 
                 1024 
               
               
                   
                 Convolution layer 
                 4096 
               
               
                   
                 Pooling layer + fully 
                 K 
               
               
                   
                 connected layer 
               
               
                   
                   
               
            
           
         
       
     
     In some embodiments, during the image classification processing, an Adam-based gradient descent method may be used to update parameters of a network provided in the disclosure. An initial learning rate may be preset to 0.05, and a betas parameter in Adam may be preset to (0.95, 0.9995). A training image in a preset training set may be processed by using the network provided in the disclosure to obtain a training image classification result, and a result of processing the training image may be used to determine a loss function of an image classification processing task. 
     In an implementation, a weighted cross entropy function may be used as the loss function of the image classification processing task. By minimizing the loss function, an error gradient may be calculated, and a gradient of the network may be updated through backward propagation to complete the training of the neural network. 
       FIG.  6 C  shows an example method for determining an object detection result according to an embodiment of the disclosure. 
     As shown in  FIG.  6 C , an output image feature  610  of the first size, an output image feature  620  of the second size, and an output image feature  630  of the third size may be obtained by using the network structure shown in  FIG.  5 B . The output image feature  610  of the first size may be downsampled to obtain the downsampled output image feature  610  of the third size, and the output image feature  620  of the second size may be downsampled to obtain the downsampled output image feature  620  of the third size. Then, the downsampled output image feature  610  of the third size, the downsampled output image feature  620 , and the output image feature  630  may be concatenated in a channel direction to obtain a combined image feature  690 . Further, an output of an object detection result may be obtained by performing convolution processing, processing of the fully connected layer, and rearrangement on the combined image feature  690 . 
     In some embodiments, the output of the object detection result may be represented as a position and a size of a detection frame in an output image and a category of an object included in the detection frame. 
     Table 4 shows quantities of channels of modules of a neural network used during object detection processing on the image to determine the detection result. 
     
       
         
           
               
               
               
             
               
                   
                 TABLE 4 
               
               
                   
                   
               
               
                   
                   
                 Quantity of 
               
               
                   
                 Module number 
                 channels 
               
               
                   
                   
               
             
            
               
                   
               
            
           
           
               
               
               
            
               
                   
                 SE1_4 
                 64 
               
               
                   
                 SE2_4 
                 256 
               
               
                   
                 SE3_2 
                 1024 
               
               
                   
                 Convolution layer 
                 4096 
               
               
                   
                 Pooling layer + fully 
                 S × S × (B × 5 × C) 
               
               
                   
                 connected layer 
               
               
                   
                   
               
            
           
         
       
     
     where B is a quantity of detection frames, C is a quantity of detection categories, and S is a quantity of detection intervals in this detection task. 
     The training image in the training data set is processed by using the neural network provided in the disclosure to obtain the training object detection result, and the real object detection result of the training image may be obtained by using manual annotation. The parameter adjustment on the neural network may be completed by obtaining, through comparison, a loss between the training object detection result and the real object detection result. In some embodiments, a mean square error may be used to represent a loss of the detection region, and the cross entropy function may represent a loss of a classification result of an object included in the detection region. 
       FIG.  7    is a schematic block diagram of an image processing apparatus according to an embodiment of the disclosure. As shown in  FIG.  7   , an image processing apparatus  700  may include a receiving unit  710 , a feature determining unit  720 , an adjustment unit  730 , a downsampling unit  740 , a combining unit  750 , and a result determining unit  760 . 
     The receiving unit  710  may be configured to receive an input image. In some embodiments, an image capture device may be used to capture the input image. In some other embodiments, the input image may be alternatively read from a storage device that stores pictures. 
     The input image described herein may contain various image information. For example, the input image may be an image and/or video in a driving process acquired by an in-vehicle image capture device. In another example, the input image may be a surveillance image and/or surveillance video acquired by a monitoring device. In still another example, the input image may be an image generated by a medical instrument by using CT, Mill, ultrasound, X-ray, electrocardiogram, electroencephalogram, optical photography, or the like. 
     In some embodiments, the input image may be black and white, or color. For example, when the input image is a black and white image, the input image may have a single channel. In another example, when the input image is a color image, the input image may have at least two channels (such as R, G, and B). 
     The feature determining unit  720  may be configured to determine a first image feature of a first size of the input image, the first image feature having at least two channels. 
     The first image feature may have the same size as that of the input image, or may have a size different from that of the input image. 
     In some embodiments, the first image feature may be determined by performing convolution processing on the input image. A specific form of performing convolution processing on the input image is not limited in the disclosure herein. For example, modules of a VGG series network, a Resnet series network and/or an Inception series network may be used to perform convolution processing on the input image at least once to obtain the first image feature. In the disclosure, a Resnet module is used as an example to explain the principle of the disclosure. However, it would be understood that any one of the foregoing network modules or any other network module capable of extracting image features may be used to replace the Resnet module in the disclosure. 
     The adjustment unit  730  may be configured to determine a first weight adjustment parameter to be used for the first image feature, and perform weight adjustment on each channel in the first image feature by using the first weight adjustment parameter, to obtain an adjusted first image feature, the first weight adjustment parameter including at least two parameter components, and each parameter component being used for adjusting a pixel of a channel corresponding to each parameter component. For example, the methods shown in  FIG.  3 A  and  FIG.  3 B  may be used to perform weight adjustment on the image features. 
     The downsampling unit  740  may be configured to downsample the adjusted first image feature to obtain a second image feature having a second size, the second size being smaller than the first size. The second image feature has at least two channels, and a quantity of channels of the second image feature is greater than a quantity of channels of the first image feature. 
     For example, the downsampling unit  740  may downsample the adjusted first image feature by using the method shown in  FIG.  4 A . 
     The combining unit  750  may be configured to combine the first image feature and the second image feature to obtain a combined image feature. 
     In some embodiments, the adjustment unit  730  is further configured to determine, for each channel in the at least two channels in the first image feature, a global feature parameter of each channel; and determine, by using the global feature parameter of each channel, a parameter component used for adjusting a pixel of each channel in the at least two channels. 
     In some embodiments, the downsampling unit  740  is further configured to determine, for M×N first pixels in the adjusted first image feature, a first pixel vector corresponding to the M×N first pixels, the first pixel vector including elements of the M×N first pixels in the at least two channels, M and N being positive integers, and a product of M and N being a positive integer greater than 1; map the first pixel vector by using a full-rank first matrix to obtain the mapped first pixel vector; and determine a second pixel in the second image feature according to the mapped first pixel vector. 
     In some embodiments, the combining unit  750  is further configured to upsample the second image feature to obtain the upsampled second image feature; and concatenate the first image feature and the upsampled second image feature in a channel direction to obtain the combined image feature. 
     In some embodiments, the combining unit  750  is further configured to determine a second weight adjustment parameter to be used for the second image feature, and perform weight adjustment on each channel in the second image feature by using the second weight adjustment parameter, to obtain an adjusted second image feature, the second weight adjustment parameter including at least two parameter components, and each parameter component being used for adjusting a pixel of a channel corresponding to each parameter component; and upsample the adjusted second image feature to obtain the upsampled second image feature, the upsampled second image feature having the first size. 
     In some embodiments, the combining unit  750  is further configured to determine, for a third pixel in the adjusted second image feature, a second pixel vector corresponding to the third pixel, the second pixel vector including elements of the third pixel in the at least two channels; map the second pixel vector by using a full-rank second matrix to obtain the mapped second pixel vector; and rearrange the mapped second pixel vector to determine at least two fourth pixels in the upsampled second image feature. 
     In some embodiments, the combining unit  750  may be further configured to determine a third weight adjustment parameter to be used for the adjusted first image feature, and perform weight adjustment on each channel in the adjusted first image feature by using the third weight adjustment parameter, to obtain the further adjusted first image feature, the third weight adjustment parameter including at least two parameter components, and each parameter component being used for adjusting a pixel of a channel corresponding to each parameter component. The weight adjustment method shown in  FIG.  3 A  and  FIG.  3 B  may be used to perform weight adjustment on the adjusted first image feature. Then, the further adjusted first image feature may be downsampled to obtain a third image feature having the second size. The second image feature and the third image feature are combined to obtain a fourth image feature having the second size. The weight adjustment method shown in  FIG.  3 A  and  FIG.  3 B  may be used to perform weight adjustment on the fourth image feature. For example, a fourth weight adjustment parameter to be used for the fourth image feature may be determined, and weight adjustment is performed on each channel in the fourth image feature by using the fourth weight adjustment parameter, to obtain an adjusted fourth image feature, the fourth weight adjustment parameter including at least two parameter components, and each parameter component being used for adjusting a pixel of a channel corresponding to each parameter component. Further, the adjusted fourth image feature may be downsampled to obtain a fifth image feature having a third size. In this case, the first image feature, the second image feature, and the fifth image feature may be combined to obtain the combined image feature. 
     An image segmentation algorithm is used as an example. In some embodiments, the second image feature may be upsampled to obtain the upsampled second image feature. Then, the first image feature and the upsampled second image feature may be concatenated in a channel direction to obtain the combined image feature. 
     In an implementation, the process of upsampling the second image feature may include performing weight adjustment on the second image feature, and upsampling the adjusted second image feature. For example, the processes shown in  FIG.  3 A  and  FIG.  3 B  may be used to determine the second weight adjustment parameter to be used for the second image feature, and the process shown in  FIG.  4 B  may be used to upsample the adjusted second image feature. 
     An image classification algorithm is used as an example, and the combined image feature may be determined through the following operations. In some embodiments, a third weight adjustment parameter to be used for the adjusted first image feature may be determined, and weight adjustment is performed on each channel in the adjusted first image feature by using the third weight adjustment parameter, to obtain the further adjusted first image feature, the third weight adjustment parameter including at least two parameter components, and each parameter component being used for adjusting a pixel of a channel corresponding to each parameter component. Then, the further adjusted first image feature may be downsampled to obtain a third image feature having the second size. Further, an addition operation may be performed on the second image feature and the third image feature to determine the combined image feature. 
     In an implementation, the weight adjustment process shown in  FIG.  3 A  and  FIG.  3 B  may be used to adjust the first image feature, and the process shown in  FIG.  4 A  may be used to downsample the further adjusted first image feature. Details are not repeated herein. 
     An example is used in which object detection is performed on the image, and the combined image feature may be determined through the following operations. In some embodiments, a third weight adjustment parameter to be used for the adjusted first image feature may be determined, and weight adjustment is performed on each channel in the adjusted first image feature by using the third weight adjustment parameter, to obtain the further adjusted first image feature, the third weight adjustment parameter including third adjustment parameter components respectively used for adjusting each channel in the adjusted first image feature. Then, the further adjusted first image feature may be downsampled to obtain a third image feature having the second size. Further, the second image feature and the third image feature may be concatenated in a channel direction to determine the combined image feature. 
     In an implementation, the weight adjustment process shown in  FIG.  3 A  and  FIG.  3 B  may be used to adjust the first image feature, and the process shown in  FIG.  4 A  may be used to downsample the further adjusted first image feature. Details are not repeated herein. 
     The result determining unit  760  may be configured to determine an image processing result according to the combined image feature. 
     As described above, for different image processing methods, the combining unit  750  may combine image features of image information with different scales in different manners. Therefore, by using the image feature outputted by the combining unit  750 , various image processing results may be obtained in different processing manners. 
     In an example embodiment, image segmentation is performed on the input image. The result determining unit  760  may be configured to perform convolution processing on the combined image feature to determine an image segmentation result used for the input image. 
     In an example embodiment, image classification is performed on the input image. The result determining unit  760  may be configured to perform convolution processing, global pooling, and full connection on the combined image feature to determine an image classification result used for the input image. 
     In an example embodiment, object detection is performed on the input image. The result determining unit  760  may be configured to perform convolution processing, full connection, and rearrangement on the combined image feature to determine an object detection result used for the input image. 
     By using the image processing apparatus provided in the disclosure, the first weight adjustment parameter to be used for the first image feature is determined according to the elements in the at least two channels of the first image feature, and weight adjustment is performed on the at least two channels in the first image feature by using the first weight adjustment parameter to obtain an adjusted first image feature, so that finer channel features may be obtained, and a better image processing result may be further obtained. Further, by combining the first image feature and the second image feature of different sizes, picture information of different scales may be exchanged to obtain comprehensive image information, thereby obtaining a better image processing result. Furthermore, full-rank matrices are used to transform pixel information, to transfer the image information with a small quantity of parameters without losing information. 
     In addition, the method or apparatus according to the embodiments of the disclosure may alternatively be implemented by using an architecture of a computing device shown in  FIG.  8   .  FIG.  8    shows an architecture of the computing device. As shown in  FIG.  8   , the computing device  800  may include a bus  810 , one or at least two CPUs  820 , a read-only memory (ROM)  830 , a random access memory (RAM)  840 , a communication port  850  connected to a network, an input/output component  860 , a hard disk  870 , and the like. A storage device, for example, the ROM  830  or the hard disk  870 , in the computing device  800  may store various data or files used in processing and/or communication in the method for detecting a target in a video provided in the disclosure and program instructions executed by the CPU. The computing device  800  may further include a user interface  880 . The architecture shown in  FIG.  8    is only an example, and in other embodiments, one or at least two components in the computing device shown in  FIG.  8    may be omitted or additional components not shown in  FIG.  8    may be added. 
       FIG.  9    shows a schematic use scenario according to an embodiment of the disclosure. 
     In some embodiments, the method and apparatus provided in the disclosure may be used for image processing of a medical image. The medical image described herein may be, for example, a medical image captured through a method such as CT, MRI, ultrasound, X-ray, or nuclide imaging (for example, SPECT or PET), or may be an image displaying physiological information of a human body, for example, an electrocardiogram, an electroencephalogram, or an optical photograph. The foregoing medical images are important means and reference factors for assisting in clinical diagnosis. According to an appearance and a shape embodied in a medical image, the inherent heterogeneity of different diseases may be further reflected. By performing image segmentation processing on a medical image, a position of a lesion in the image may be obtained through segmentation. By performing image classification processing on a medical image, a category of a lesion in the image may be determined. For example, diseases such as tumors, tuberculosis, cysts, and cardiovascular diseases may be diagnosed according to a medical image. 
     As shown in  FIG.  9   , a front end A may be used to receive medical image data to be processed by a user. For example, the front end A may use various medical imaging devices (such as a CT, MRI, ultrasound, and X-ray instrument) that acquire medical image data. Further, the front end A may be used to preprocess the image data to be processed, including but not limited to data augmentation such as translation, rotation, and symmetrization, and organ selection algorithms such as segmentation. It would be understood that the front end A may alternatively send the acquired image data to another electronic device for preprocessing the image data. 
     In some embodiments, the preprocessed medical image data may be sent to a back end by using the front end A or another electronic device that preprocesses the image. The back end herein may be implemented as an image processing apparatus provided in the disclosure. By using the back-end image processing apparatus, image segmentation, image classification, and/or object detection processing of medical images may be implemented. Then, the image processing apparatus may send a result of segmentation, classification, or object detection to a front end B. 
     The front end B described herein may be the same as or different from the front end A. In some embodiments, the front end B may include an output device. For example, the output device may be a display screen or a speaker, so that a processing result of the image processing apparatus may be outputted in a visual or auditory manner. It would be understood that the output device may alternatively be any other type of device. 
     In some examples, when a data acquisition device includes a display screen, the back-end image processing apparatus may send the image processing result back to the front end A to display the image processing result to the user. In some other examples, the front end B may be an electronic device (such as a mobile phone, or a computer) as a client. The client may include an output device to display the image processing result to the user. In addition, the client may further include a processing unit, and may perform further subsequent processing on a result outputted by the image processing apparatus. 
     The embodiments of the disclosure may alternatively be implemented as a computer-readable storage medium. The computer-readable storage medium according to the embodiments of the disclosure stores a computer-readable instruction. The computer-readable instruction, when executed by a processor, may perform the method according to the embodiments of the disclosure described with reference to the foregoing accompanying drawings. The computer-readable storage medium includes, but is not limited to, a volatile memory and/or a non-volatile memory. For example, the volatile memory may include a RAM and/or a high-speed cache. For example, the non-volatile memory may include a ROM, a hard disk, and a flash memory. 
     By using the image processing method, apparatus, and device, the storage medium, and any related technology provided in the disclosure, the first weight adjustment parameter to be used for the first image feature is determined according to the elements in the at least two channels of the first image feature, and weight adjustment is performed on the at least two channels in the first image feature by using the first weight adjustment parameter to obtain an adjusted first image feature, so that finer channel features may be obtained, and a better image processing result may be further obtained. Further, by combining the first image feature and the second image feature of different sizes, picture information of different scales may be exchanged to obtain comprehensive image information, thereby obtaining a better image processing result. Furthermore, full-rank matrices are used to transform pixel information, to transfer the image information with a small quantity of parameters without losing information. 
     A person skilled in the art would understand that, content disclosed in the disclosure may have various variations and improvements. For example, the devices or components described above may be implemented by using hardware, or may be implemented by using software, firmware, or a combination of some of or all of the software, the firmware, and the hardware. 
     In addition, as shown in the disclosure and the claims, words such as “a/an”, “one”, “one kind”, and/or “the” do not refer specifically to singular forms and may also include plural forms, unless the context expressly indicates an exception. In general, terms “comprise” and “include” merely indicate including clearly identified operations and elements. The operations and elements do not constitute an exclusive list. A method or a device may also include other operations or elements. 
     In addition, although the disclosure makes various references to some units in the system according to the embodiments of the disclosure, any quantity of different units may be used and run on a client and/or a server. The units are only illustrative, and different aspects of the system and method may use different units. 
     In addition, flowcharts are used in the disclosure for illustrating operations performed by the system according to the embodiments of the disclosure. It is to be understood that, the foregoing or following operations are not necessarily strictly performed according to an order. On the contrary, the operations may be performed in a reverse order or simultaneously or any other given order. Meanwhile, other operations may be added to the processes. Alternatively, one or more operations may be deleted from the processes. 
     Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the disclosure belongs. It is further to be understood that, the terms such as those defined in commonly used dictionaries are to be interpreted as having meanings that are consistent with the meanings in the context of the related art, and are not to be interpreted in an idealized or extremely formalized sense, unless expressively so defined herein. 
     The above is description of the disclosure, and is not to be considered as a limitation to the disclosure. Although several example embodiments of the disclosure are described, a person skilled in the art would easily understand that, various changes may be made to the example embodiments without departing from novel teaching and advantages of the disclosure. Therefore, the changes are intended to be included within the scope of the disclosure as defined by the claims. It is to be understood that, the above is description of the disclosure, and is not to be considered to be limited by the disclosed specific embodiments, and modifications to the disclosed embodiments and other embodiments fall within the scope of the appended claims and their equivalents.