Adobe, Inc.

Ke Wang

Elya Shechtman

Xin Lu

Zichuan Liu

An image processing system employs a parametric model for image harmonization of composite images. The parametric model employs a two-stage approach to harmonize an input composite image. At a first stage, a color curve prediction model predicts color curve parameters for the composite image. At a second stage, the composite image with the color curve parameters is input to a shadow map prediction model, which predicts a shadow map. The predicted color curve parameters and shadow map are applied to the composite image to provide a harmonized composite image. In some aspects, the color curve parameters and shadow map are predicted using a lower-resolution composite image and up-sampled to apply to a higher-resolution version of the composite image. The harmonized composite image can be output with the predicted color curve parameters and/or shadow map, which can be modified by a user to further enhance the harmonized composite image.

Parametric composite image harmonization

Methods, systems, and non-transitory computer readable storage media are disclosed for generating image mattes for detected objects in digital images without trimap segmentation via a multi-branch neural network. The disclosed system utilizes a first neural network branch of a generative neural network to extract a coarse semantic mask from a digital image. The disclosed system utilizes a second neural network branch of the generative neural network to extract a detail mask based on the coarse semantic mask. Additionally, the disclosed system utilizes a third neural network branch of the generative neural network to fuse the coarse semantic mask and the detail mask to generate an image matte. In one or more embodiments, the disclosed system also utilizes a refinement neural network to generate a final image matte by refining selected portions of the image matte generated by the generative neural network.

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Berkeley, CA, United States of America

Ke Wang