Abstract: An image lighting method includes: determining a convolutional neural network corresponding to a lighting operation type of an initial image; obtaining local feature information and global feature information of initial image according to convolutional neural network; obtaining fusion feature information of initial image according to convolutional neural network based on local feature information and global feature information; obtaining a maximum pooling result map and a minimum pooling result map according to convolutional neural network based on a luminance component map of initial image; obtaining a bilateral grid matrix of initial image based on fusion feature information, maximum pooling result map, and minimum pooling result map; and performing affine transformation on initial image according to bilateral grid matrix to obtain a target image, the target image being an image obtained after lighting initial image according to lighting operation type.

Abstract: A face image processing method includes: performing face detection on an image to be processed, and obtaining at least one face region image included in the image to be processed and face attribute information in the at least one face region image; and for the at least one face region image, processing an image corresponding to a first region and/or an image corresponding to a second region in the face region image at least according to the face attribute information in the face region image, wherein the first region is a skin region, and the second region includes at least a non-skin region.

Abstract: A face image processing method includes: performing face detection on an image to be processed, and obtaining at least one face region image included in the image to be processed and face attribute information in the at least one face region image; and for the at least one face region image, processing an image corresponding to a first region and/or an image corresponding to a second region in the face region image at least according to the face attribute information in the face region image, wherein the first region is a skin region, and the second region includes at least a non-skin region.

Abstract: An image processing method includes: obtaining an original photographed image and original depth information of at least one pixel point in the original photographed image; determining blurring degree data of each of the at least one pixel point in the original photographed image according to blurring parameter data and the original depth information of the at least one pixel point; and performing blurring processing on the original photographed image according to the blurring degree data of the at least one pixel point to generate a post-processing result image.

Abstract: Image blurring methods and apparatuses, storage media, and electronic devices. The method can be carried out by the apparatuses, media and devices, and includes: generating a second image having second pixel points corresponding to first pixel points of a first image; determining, according to blurring desired data of the first pixel points, respective initial blurring weight values of the corresponding second pixel points; performing at least one update on at least one of the second pixel points, the update including: updating, based on a first pixel value of the first pixel point and a current blurring weight value of the second pixel point corresponding to the based first pixel point, a current pixel value and a current blurring weight value of at least one neighboring second pixel point of the based corresponding second pixel point; and obtaining a blurring result of the first image according to the updated second image.

Abstract: Image blurring methods and apparatuses, storage media, and electronic devices can include: obtaining a main image and a secondary image obtained by photographing the same object with a dual-lens camera; obtaining depth data and depth confidence degree data according to the main image and the secondary image, the depth data indicating depth values of corresponding pixel points in the main image and the secondary image, and the depth confidence degree data indicating confidence degrees of the depth values in the depth data; correcting at least one depth value in the depth data according to the depth confidence degree data; and blurring the main image according to corrected depth data.

Abstract: An image lighting method includes: determining a convolutional neural network corresponding to a lighting operation type of an initial image; obtaining local feature information and global feature information of initial image according to convolutional neural network; obtaining fusion feature information of initial image according to convolutional neural network based on local feature information and global feature information; obtaining a maximum pooling result map and a minimum pooling result map according to convolutional neural network based on a luminance component map of initial image; obtaining a bilateral grid matrix of initial image based on fusion feature information, maximum pooling result map, and minimum pooling result map; and performing affine transformation on initial image according to bilateral grid matrix to obtain a target image, the target image being an image obtained after lighting initial image according to lighting operation type.

Abstract: A face image processing method includes: performing face detection on an image to be processed, and obtaining at least one face region image included in the image to be processed and face attribute information in the at least one face region image; and for the at least one face region image, processing an image corresponding to a first region and/or an image corresponding to a second region in the face region image at least according to the face attribute information in the face region image, wherein the first region is a skin region, and the second region includes at least a non-skin region.

Abstract: Image blurring methods and apparatuses, storage media, and electronic devices can include: obtaining a main image and a secondary image obtained by photographing the same object with a dual-lens camera; obtaining depth data and depth confidence degree data according to the main image and the secondary image, the depth data indicating depth values of corresponding pixel points in the main image and the secondary image, and the depth confidence degree data indicating confidence degrees of the depth values in the depth data; correcting at least one depth value in the depth data according to the depth confidence degree data; and blurring the main image according to corrected depth data.

Abstract: An image processing method includes: obtaining an original photographed image and original depth information of at least one pixel point in the original photographed image; determining blurring degree data of each of the at least one pixel point in the original photographed image according to blurring parameter data and the original depth information of the at least one pixel point; and performing blurring processing on the original photographed image according to the blurring degree data of the at least one pixel point to generate a post-processing result image.

Abstract: Image blurring methods and apparatuses, storage media, and electronic devices. The method can be carried out by the apparatuses, media and devices, and includes: generating a second image having second pixel points corresponding to first pixel points of a first image; determining, according to blurring desired data of the first pixel points, respective initial blurring weight values of the corresponding second pixel points; performing at least one update on at least one of the second pixel points, the update including: updating, based on a first pixel value of the first pixel point and a current blurring weight value of the second pixel point corresponding to the based first pixel point, a current pixel value and a current blurring weight value of at least one neighboring second pixel point of the based corresponding second pixel point; and obtaining a blurring result of the first image according to the updated second image.

Abstract: It is provided according to the present application an image processing method applied to an electronic device equipped with a binocular camera. The image processing method includes: calculating a motion direction of a first image and a motion direction of a second image, wherein the first image and second image are captured by two cameras of the binocular camera respectively; performing deblurring on the first image and the second image; and combining the images which are already deblurred to obtain a definite three-dimensional perspective image. According to the image processing method, a blurry image generated during imaging is deblurred, the blurring caused by movement or the like may be weakened, the definition of the deblurred image is better than the definition of the blurred image, thereby leading to a better imaging result.

Abstract: An information processing method is described that includes obtaining a first operation that is used to select a foreground image from a first picture; determining the foreground image in the first picture based on the first operation; obtaining status information of the foreground image; obtaining a second operation that is used to place the foreground image into a second picture; determining the second picture which serves as a background based on the second operation; obtaining status information of the second picture; determining a target size occupied by the foreground image in the second picture based on the status information of the foreground image and the second picture; scaling the foreground image into the target size; and displaying the foreground image in the second picture with the target size. An electronic device is also described.

Abstract: An image processing method is applied to an electronic device having a binocular camera that includes a first camera and a second camera. The method includes acquiring at least one first image taken by the first camera of the binocular camera and at least one second image taken by the second camera of the binocular camera; acquiring depth images in scenes of the at least one first image and the at least one second image; differentiating, based on the depth images, foregrounds and backgrounds in the scenes of the at least one first image and the at least one second image; and matching and stitching the foregrounds of the at least one first image and the at least one second image, and matching and stitching the backgrounds of the at least one first image and the at least one second image, so as to obtain a stitched third image.

Abstract: An information processing method is described that includes obtaining a first operation that is used to select a foreground image from a first picture; determining the foreground image in the first picture based on the first operation; obtaining status information of the foreground image; obtaining a second operation that is used to place the foreground image into a second picture; determining the second picture which serves as a background based on the second operation; obtaining status information of the second picture; determining a target size occupied by the foreground image in the second picture based on the status information of the foreground image and the second picture; scaling the foreground image into the target size; and displaying the foreground image in the second picture with the target size. An electronic device is also described.

Abstract: It is provided according to the present application an image processing method applied to an electronic device equipped with a binocular camera. The image processing method includes: calculating a motion direction of a first image and a motion direction of a second image, wherein the first image and second image are captured by two cameras of the binocular camera respectively; performing deblurring on the first image and the second image; and combining the images which are already deblurred to obtain a definite three-dimensional perspective image. According to the image processing method, a blurry image generated during imaging is deblurred, the blurring caused by movement or the like may be weakened, the definition of the deblurred image is better than the definition of the blurred image, thereby leading to a better imaging result.