Abstract: A method may include acquiring a first image that is obtained by adding noise to a second image, the second image comprising a target restoration region. The method may include performing at least one first denoising process on the first image using a first artificial intelligence (AI) network to obtain a first denoising result. The method may include restoring the target restoration region based on the first denoising result using a second AI network to obtain a restored image.

Abstract: According to an embodiment of the disclosure, a method performed by an electronic apparatus may include extracting at least one key frame and at least one non-key frame from a video. According to an embodiment of the disclosure, a method performed by an electronic apparatus may include inpainting the at least one key frame based on at least one mask corresponding to the at least one key frame. According to an embodiment of the disclosure, a method performed by an electronic apparatus may include inpainting the at least one non-key frame based on the at least one inpainted key frame.

Abstract: A method performed by an electronic device, includes acquiring a first image comprising at least a first region and a second region and a target object to be moved in the first image from the second region to the first region; and performing target object removal processing on the first image using a first artificial intelligence (AI) network based on guidance information related to at least one of first region and the second region, wherein the second region is a region of the target object in the first image in which the target object is located prior to the removal processing.

Abstract: A method performed by an electronic apparatus, includes: determining a local frame sequence from a video; obtaining a feature map sequence of the local frame sequence by encoding the local frame sequence; determining a feature flow sequence of the local frame sequence based on the feature map sequence of the local frame sequence and a mask image sequence regarding an object to be removed, the mask image sequence being corresponding to the feature map sequence; obtaining an updated feature map sequence of the local frame sequence, by performing, based on the feature flow sequence, feature fusion between adjacent feature maps in the feature map sequence; and obtaining a processed local frame sequence, by decoding the updated feature map sequence of the local frame sequence.

Abstract: Methods and apparatuses for performing image processing using artificial intelligence are provided. In an embodiment, an image processing method includes acquiring a target image based on an editing operation on an original image, performing a filling processing operation on the target image to obtain a first filled image including a first target region, identifying a target patch based on the first filled image, calculating a similarity value between the target patch and at least one patch related to the first filled image using a first artificial intelligence (AI) model, determining a target residual patch corresponding to the target patch based on the similarity value and generating a processing result image based on the target residual patch.

Abstract: An image processing apparatus including: at least one memory; and at least one processor coupled to the at least one memory and configured to implement: an image acquisition module configured to acquire an input image including an object region; a mask image generation module configured to generate a mask image based on the input image; and an image inpainting module configured to extract a fusion feature map corresponding to the input image using an encoding network according to the input image and the mask image, and to inpaint the object region in the input image using a decoding network based on the fusion feature map, to obtain an inpainting result.

Abstract: An image processing apparatus including: at least one memory; and at least one processor coupled to the at least one memory and configured to implement: an image acquisition module configured to acquire an input image including an object region; a mask image generation module configured to generate a mask image based on the input image; and an image inpainting module configured to extract a fusion feature map corresponding to the input image using an encoding network according to the input image and the mask image, and to inpaint the object region in the input image using a decoding network based on the fusion feature map, to obtain an inpainting result