Abstract: Multiple images are continuously captured based on a photographing instruction. Multiple processed images without ghosts are obtained based on the multiple images. A fused image is obtained by inputting the multiple processed images without ghosts into an image fusion model. Noise of the fused image is lower than noise of any one of the multiple images, and a resolution of the fused image is higher than a resolution of any one of the multiple images. A target image is obtained and output by performing image enhancement processing on the fused image.

Abstract: An image processing method and apparatus, a model training method and apparatus, and a storage medium. The image processing method includes: acquiring a to-be-processed image; and inputting the to-be-processed image into a chromatic aberration-removing model to obtain a target image output by the chromatic aberration-removing model, where the chromatic aberration-removing model is obtained by a training data set through training, the training data set includes multiple groups of sample data, and each group of sample data includes a chromatic-aberration-free image and a chromatic-aberration image corresponding to the chromatic-aberration-free image.

Abstract: Multiple images are continuously captured based on a photographing instruction. Multiple processed images without ghosts are obtained based on the multiple images. A fused image is obtained by inputting the multiple processed images without ghosts into an image fusion model. Noise of the fused image is lower than noise of any one of the multiple images, and a resolution of the fused image is higher than a resolution of any one of the multiple images. A target image is obtained and output by performing image enhancement processing on the fused image.

Abstract: A method, device and computer-readable medium are provided for adjusting video playing progress in the field of video processing. The method for adjusting video playing progress includes: acquiring a keyword inputted by a user; determining at least one matching keyframe corresponding to the keyword in a target video, which includes at least one keyframe; displaying the at least one matching keyframe; and adjusting displaying progress of the target video to target displaying progress, when a selection operation on a keyframe of the at least one matching keyframe is detected, where the target displaying progress corresponds to the keyframe as indicated by the selection operation.

Abstract: A method, device and computer-readable medium are provided for adjusting video playing progress in the field of video processing. The method for adjusting video playing progress includes: acquiring a keyword inputted by a user; determining at least one matching keyframe corresponding to the keyword in a target video, which includes at least one keyframe; displaying the at least one matching keyframe; and adjusting displaying progress of the target video to target displaying progress, when a selection operation on a keyframe of the at least one matching keyframe is detected, where the target displaying progress corresponds to the keyframe as indicated by the selection operation.

Abstract: A method and system for classifying tissue endomicroscopy images are disclosed. Local feature descriptors are extracted from an endomicroscopy image. Each of the local feature descriptors is encoded using a learnt discriminative dictionary. The learnt discriminative dictionary includes class-specific sub-dictionaries and penalizes correlation between bases of sub-dictionaries associated with different classes. Tissue in the endomicroscopy image is classified using a trained machine learning based classifier based on the coded local feature descriptors encoded using a learnt discriminative dictionary.

Abstract: A method for performing cellular classification includes using a convolution sparse coding process to generate a plurality of feature maps based on a set of input images and a plurality of biologically-specific filters. A feature pooling operation is applied on each of the plurality of feature maps to yield a plurality of image representations. Each image representation is classified as one of a plurality of cell types.

Abstract: A method for performing cellular classification includes extracting a plurality of local feature descriptors from a set of input images and applying a coding process to covert each of the plurality of local feature descriptors into a multi-dimensional code. A feature pooling operation is applied on each of the plurality of local feature descriptors to yield a plurality of image representations and each image representation is classified as one of a plurality of cell types.