Abstract: An image processing method includes obtaining a plurality of frames of raw images. After preprocessing such as image alignment, channel splitting or pixel rearrangement is performed on the obtained plurality of frames of raw images, detail restoration is performed on an image based on a deep learning network, and luminance enhancement and color enhancement are performed on an image output by the deep learning network. A plurality of types of processing related to detail restoration are integrated into a same deep learning network.

Abstract: A method and a terminal are provided by various embodiments of the present invention in which: M cameras are configured to continuously shoot a same scene for K times, where M?2, K?2, and each of the M cameras simultaneously collects one frame of image at each time of shooting; and a processor, connected to the M cameras and configured to perform registration on images obtained at the K times of shooting, and perform temporal and spatial filtering on the registered images. In the embodiments of the present invention. Because the multiple cameras simultaneously collect an image at each time of shooting, a time interval at which a particular quantity of images are collected by the terminal is shorter than that at which the particular quantity of images are collected by a single camera, and a probability of an occurrence of hand trembling or object motion within the time interval also correspondingly decreases.

Abstract: A method and a terminal are provided by various embodiments of the present invention in which: M cameras are configured to continuously shoot a same scene for K times, where M?2, K?2, and each of the M cameras simultaneously collects one frame of image at each time of shooting; and a processor, connected to the M cameras and configured to perform registration on images obtained at the K times of shooting, and perform temporal and spatial filtering on the registered images. In the embodiments of the present invention. Because the multiple cameras simultaneously collect an image at each time of shooting, a time interval at which a particular quantity of images are collected by the terminal is shorter than that at which the particular quantity of images are collected by a single camera, and a probability of an occurrence of hand trembling or object motion within the time interval also correspondingly decreases.

Abstract: An image blurring method and apparatus, and an electronic device, and pertains to the field of image processing. The method includes acquiring a first image and a second image that are captured by a same camera and have a same viewfinder coverage; acquiring depth information according to the first image and the second image; determining a background area of the first image according to the depth information; and performing blurring processing on content in the background area of the first image. In the present invention, solving a problem in the prior art that a user needs to open an image using software and manually selects a background area, and achieving an effect of improving a blurring effect and simplifying a user operation.

Abstract: An image blurring method and apparatus, and an electronic device, and pertains to the field of image processing. The method includes acquiring a first image and a second image that are captured by a same camera and have a same viewfinder coverage; acquiring depth information according to the first image and the second image; determining a background area of the first image according to the depth information; and performing blurring processing on content in the background area of the first image. In the present invention, solving a problem in the prior art that a user needs to open an image using software and manually selects a background area, and achieving an effect of improving a blurring effect and simplifying a user operation.