Abstract: 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 are 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.

Abstract: Systems and methods for synthesizing images with increased high-frequency detail are described. Embodiments are configured to identify an input image including a noise level and encode the input image to obtain image features. A diffusion model reduces a resolution of the image features at an intermediate stage of the model using a wavelet transform to obtain reduced image features at a reduced resolution, and generates an output image based on the reduced image features using the diffusion model. In some cases, the output image comprises a version of the input image that has a reduced noise level compared to the noise level of the input image.

Abstract: A plurality of pixel-based sampling points are identified within an image, wherein sampling points of a pixel are distributed within the pixel. For individual sampling points of individual pixels, a corresponding radiance vector is estimated. A radiance vector includes one or more radiance values characterizing light received at a sampling point. A first machine learning module generates, for each pixel, a corresponding intermediate radiance feature vector, based on the radiance vectors associated with the sampling points within that pixel. A second machine learning module generates, for each pixel, a corresponding final radiance feature vector, based on an intermediate radiance feature vector for that pixel, and one or more other intermediate radiance feature vectors for one or more other pixels neighboring that pixel. One or more kernels are generated, based on the final radiance feature vectors, and applied to corresponding pixels of the image, to generate a lower noise image.

Abstract: Generating vector representations of visual objects is leveraged in a digital medium environment. For instance, a raster-based visual input object is encoded into a global latent code and individual path latent codes for visual components of the raster visual object are extracted from the global latent code. The path latent codes are decoded and used to generate vector representations of the original raster versions of the visual components. The vector representations are rasterized and composited to generate an output object that simulates a visual appearance of the input object.

Abstract: A plurality of pixel-based sampling points are identified within an image, wherein sampling points of a pixel are distributed within the pixel. For individual sampling points of individual pixels, a corresponding radiance vector is estimated. A radiance vector includes one or more radiance values characterizing light received at a sampling point. A first machine learning module generates, for each pixel, a corresponding intermediate radiance feature vector, based on the radiance vectors associated with the sampling points within that pixel. A second machine learning module generates, for each pixel, a corresponding final radiance feature vector, based on an intermediate radiance feature vector for that pixel, and one or more other intermediate radiance feature vectors for one or more other pixels neighboring that pixel. One or more kernels are generated, based on the final radiance feature vectors, and applied to corresponding pixels of the image, to generate a lower noise image.