Abstract: A method, electronic device and storage medium for processing an image using depth-of-field information is disclosed. The method includes obtaining a weight matrix and a weight image based on depth-of-field data of an input image; obtaining a first horizontal summed area table corresponding to the weight matrix and a second horizontal summed area table corresponding to the weight image by performing horizontal summing operation on the weight matrix and the weight image; obtaining a first weighted blurring image corresponding to the weight matrix based on the first horizontal summed area table, and obtaining a second weighted blurring image corresponding to the weight image based on the second horizontal summed area table; and obtaining a pixel value of the pixel in a target image based on the first and second weighted blurring images.

Abstract: In an example of the disclosed techniques, a method comprising computing, by a computing system, a distribution of symbol errors in a forward error correction (FEC) frame transmitted in an optical signal. The method further includes, in response to determining that the distribution of symbol errors indicates a burst error that satisfies a threshold, adjusting, in an optical transceiver module, one or more of a pre-cursor setting, a post-cursor setting, or a signal amplitude for the optical signal.

Abstract: A method for processing an image and a mobile terminal. The method includes: obtaining a lens effect instruction, and determining a target blurring algorithm from a plurality of preset blurring algorithms based on the lens effect instruction, in which the blurring algorithms are algorithms for simulating optical lens effects; and obtaining a target image by blurring an image to be processed based on the target blurring algorithm.

Abstract: A method, electronic device and storage medium for processing an image using depth-of-field information is disclosed. The method includes obtaining a weight matrix and a weight image based on depth-of-field data of an input image; obtaining a first horizontal summed area table corresponding to the weight matrix and a second horizontal summed area table corresponding to the weight image by performing horizontal summing operation on the weight matrix and the weight image; obtaining a first weighted blurring image corresponding to the weight matrix based on the first horizontal summed area table, and obtaining a second weighted blurring image corresponding to the weight image based on the second horizontal summed area table; and obtaining a pixel value of the pixel in a target image based on the first and second weighted blurring images.

Abstract: A method and an apparatus for generating a face rotation image are provided. The method includes: performing pose encoding on an obtained face image based on two or more landmarks in the face image, to obtain pose encoded images; obtaining a plurality of training images each including a face from a training data set, wherein presented rotation angles of the faces included in the plurality of training images are the same; performing pose encoding on a target face image based on two or more landmarks in the target face image in the foregoing similar manner, to obtain pose encoded images, wherein the target face image is obtained based on the plurality of training images; generating a to-be-input signal based on the face image and the foregoing two types of pose encoded images; and inputting the to-be-input signal into an face rotation image generative model to obtain a face rotation image.

Abstract: This application provides a method and an apparatus for generating a face rotation image. The method includes: performing pose encoding on an obtained face image based on two or more landmarks in the face image, to obtain pose encoded images; obtaining a plurality of training images each including a face from a training data set, where presented rotation angles of the faces included in the plurality of training images are the same; performing pose encoding on a target face image based on two or more landmarks in the target face image in the foregoing similar manner, to obtain pose encoded images, where the target face image is obtained based on the plurality of training images; generating a to-be-input signal based on the face image and the foregoing two types of pose encoded images; and inputting the to-be-input signal into an face rotation image generative model to obtain a face rotation image.