Abstract: Disclosed are a method and apparatus for compressing a topology recognition model, an electronic device, and a medium. The method includes: pruning a to-be-compressed model to obtain a pruned model, and training the pruned model to obtain a trained pruned model; quantifying a bit quantity of a weight parameter of each neural network layer in the trained pruned model from a first bit quantity to a second bit quantity to obtain a highly-quantified pruned model, and training the highly-quantified pruned model to obtain a trained highly-quantified pruned model; using the trained highly-quantified pruned model as the to-be-compressed model, continuously pruning and quantifying the to-be-compressed model until a compressed topology recognition model is obtained, and deploying the compressed topology recognition model on an Internet of Things in Power Systems (IOTIPS) terminal, to reduce a size of the topology recognition model while ensuring the topology recognition model meets an accuracy requirement.

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 stereoscopic image acquisition method includes acquiring a first three-dimensional model corresponding to a two-dimensional image according to the two-dimensional image and a depth image; acquiring a target area and an area to be repaired corresponding to the first three-dimensional model; obtaining a second three-dimensional model by repairing a pixel in the area to be repaired according to a color value and a depth value of a pixel in the target area; and acquiring a stereoscopic image to be displayed according to a preset viewing angle change path and the second three-dimensional model.

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 stereoscopic image acquisition method includesacquiring a first three-dimensional model corresponding to a two-dimensional image according to the two-dimensional image and a depth image; acquiring a target area and an area to be repaired corresponding to the first three-dimensional model; obtaining a second three-dimensional model by repairing a pixel in the area to be repaired according to a color value and a depth value of a pixel in the target area; and acquiring a stereoscopic image to be displayed according to a preset viewing angle change path and the second three-dimensional model.

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.