Tencent Technology (shenzhen) Company Limited

Tencent Technology Company Limited

Xin Tao

Jiaya Jia

Xiaoyong Shen

Yuwing Tai

Yi Tong Wang

Jia Ya Jia

Yu Wing Tai

Xiao Yong Shen

Hongyun Gao

Hong Yun Gao

Ruixing Wang

This application discloses an image processing method, device, and apparatus, and a storage medium. The method is performed by a computing device, the method including: receiving an input image; determining a context feature of the input image; determining a first feature set and a second feature set according to the context feature and based on a size of a target image and a location of the input image in the target image; adjusting the second feature set according to a first feature statistic of the first feature set, to obtain an adjusted second feature set; and generating the target image based on the adjusted second feature set and the first feature set.

Image processing method and apparatus, device, and storage medium

Embodiments of the disclosure disclose a method, an apparatus, a computer device, and a storage medium for inpainting an image. In an embodiment, an image inpainting method includes: determining, from a target image, a first region to be inpainted and a second region that is not to be inpainted; performing feature extraction on the second region based on different receptive fields and spatial resolutions, to obtain feature information of a plurality of scales; generating a texture of the first region based on the feature information of the plurality of scales; and filling the first region in the target image with the generated texture, to obtain an inpainted image.

Image inpainting method and apparatus, computer device, and storage medium

Embodiments of this disclosure include an image enhancement method and apparatus. The image enhancement may include obtaining an original image and performing synthesis processing on features of the original image to obtain a first illumination map corresponding to the original image. A resolution of the first illumination map may be lower than a resolution of the original image. The image enhancement may further include obtaining, based on the first illumination map, a mapping relationship between an image to an illumination map and performing mapping processing on the original image based on the mapping relationship to obtain a second illumination map. A resolution of the second illumination map may be equal to the resolution of the original image. The image enhancement may further include performing image enhancement processing on the original image according to the second illumination map to obtain a target image.

Image enhancement method and apparatus, and storage medium

Embodiments of this application disclose a training method using image processing model for processing blurry images. The method includes obtaining a sample pair comprising a clear image and a corresponding blurry image; the sharpness of the clear image being greater than a preset threshold, the sharpness of the blurry image being less than the preset threshold; activating the image processing model to perform sharpness restoration on the blurry image to obtain a restored image; and updating network parameters of a first network and network parameters of a second network in the image processing model according to the restored image and the clear image to obtain a trained image processing model; the network parameters of the first network and the network parameters of the second network meeting a selective sharing condition indicating whether the network parameters between the first network and the second network are shared or independent.

Training method for image processing model, image processing method, network device, and storage medium

An image processing method and device, storage medium and electronic device for deblurring an image. The method includes obtaining an image processing instruction including an instruction to deblur a target blurred image; obtaining a target model by training an original model based on a plurality of sample images of different scales, one of the plurality of sample images being a blurred image composed of a plurality of clear images, and the obtained target model being used for deblurring the blurred image to obtain a clear image; based on the image processing instruction, using the target model to deblur the target blurred image to obtain a target clear image; and outputting the target clear image.

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Shenzhen, China

Xin Tao