公开(公告)号：US20240394834A1

公开(公告)日：2024-11-28

申请号：US18323233

申请日：2023-05-24

Applicant: Adobe Inc.

Inventor： Zhihao Xia ,  Michael Gharbi ,  Jiawen Chen ,  Goutam Bhat

IPC: G06T3/40 ,  G06T3/00 ,  G06T5/00 ,  G06T7/20 ,  G06V10/56

Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that implements self-supervised training of an image burst model, trained exclusively on low-resolution images. For example, the disclosed system accesses an image burst that includes a plurality of images. The disclosed system generates a high-resolution image estimation from a first subset of images of the plurality of images. Further, the disclosed system generates a set of low-resolution images by modifying the high-resolution image estimation based on parameters of one or more images from the plurality of images. Moreover, the disclosed system determines a measure of loss by comparing the set of low-resolution images with a second subset of images from the plurality of images and updates the image burst model with the determined measure of loss.

公开(公告)号：US20240202989A1

公开(公告)日：2024-06-20

申请号：US18067878

申请日：2022-12-19

Applicant: Adobe Inc.

Inventor： Ethan Tseng ,  Zhihao Xia ,  Yifei Fan ,  Xuaner Zhang ,  Peter Merrill ,  Lars Jebe ,  Jiawen Chen

IPC: G06T11/00 ,  G06T11/60 ,  G06V10/77 ,  G06V10/774 ,  G06V10/82 ,  G06V20/40

CPC classification number: G06T11/001 ,  G06T11/60 ,  G06V10/7715 ,  G06V10/774 ,  G06V10/82 ,  G06V20/46 ,  G06V2201/10

Abstract: Digital content stylization techniques are described that leverage a neural photofinisher to generate stylized digital images. In one example, the neural photofinisher is implemented as part of a stylization system to train a neural network to perform digital image style transfer operations using reference digital content as training data. The training includes calculating a style loss term that identifies a particular visual style of the reference digital content. Once trained, the stylization system receives a digital image and generates a feature map of a scene depicted by the digital image. Based on the feature map as well as the style loss, the stylization system determines visual parameter values to apply to the digital image to incorporate a visual appearance of the particular visual style. The stylization system generates the stylized digital image by applying the visual parameter values to the digital image automatically and without user intervention.

公开(公告)号：US20220156588A1

公开(公告)日：2022-05-19

申请号：US17590995

申请日：2022-02-02

Applicant: Adobe Inc.

Inventor： Federico Perazzi ,  Zhihao Xia ,  Michael Gharbi ,  Kalyan Sunkavalli

IPC: G06N3/08 ,  G06T15/50 ,  G06T5/00 ,  G06N20/10

Abstract: Certain embodiments involve techniques for efficiently estimating denoising kernels for generating denoised images. For instance, a neural network receives a noisy reference image to denoise. The neural network uses a kernel dictionary of base kernels and generates a coefficient vector for each pixel in the reference image such that the coefficient vector includes a coefficient value for each base kernel in the kernel dictionary, where the base kernels are combined to generate a denoising kernel and each coefficient value indicates a contribution of a given base kernel to a denoising kernel. The neural network calculates the denoising kernel for a given pixel by applying the coefficient vector for that pixel to the kernel dictionary. The neural network applies each denoising kernel to the respective pixel to generate a denoised output image.

公开(公告)号：US11281970B2

公开(公告)日：2022-03-22

申请号：US16686978

申请日：2019-11-18

Applicant: Adobe Inc.

Inventor： Federico Perazzi ,  Zhihao Xia ,  Michael Gharbi ,  Kalyan Sunkavalli

IPC: G06K9/62 ,  G06N3/08 ,  G06T15/50 ,  G06T5/00 ,  G06N20/10

Abstract: Certain embodiments involve techniques for efficiently estimating denoising kernels for generating denoised images. For instance, a neural network receives a noisy reference image to denoise. The neural network uses a kernel dictionary of base kernels and generates a coefficient vector for each pixel in the reference image such that the coefficient vector includes a coefficient value for each base kernel in the kernel dictionary, where the base kernels are combined to generate a denoising kernel and each coefficient value indicates a contribution of a given base kernel to a denoising kernel. The neural network calculates the denoising kernel for a given pixel by applying the coefficient vector for that pixel to the kernel dictionary. The neural network applies each denoising kernel to the respective pixel to generate a denoised output image.

公开(公告)号：US11783184B2

公开(公告)日：2023-10-10

申请号：US17590995

申请日：2022-02-02

Applicant: Adobe Inc.

Inventor： Federico Perazzi ,  Zhihao Xia ,  Michael Gharbi ,  Kalyan Sunkavalli

IPC: G06N3/08 ,  G06T15/50 ,  G06T5/00 ,  G06N20/10

CPC classification number: G06N3/08 ,  G06N20/10 ,  G06T5/002 ,  G06T15/50

Abstract: Certain embodiments involve techniques for efficiently estimating denoising kernels for generating denoised images. For instance, a neural network receives a noisy reference image to denoise. The neural network uses a kernel dictionary of base kernels and generates a coefficient vector for each pixel in the reference image such that the coefficient vector includes a coefficient value for each base kernel in the kernel dictionary, where the base kernels are combined to generate a denoising kernel and each coefficient value indicates a contribution of a given base kernel to a denoising kernel. The neural network calculates the denoising kernel for a given pixel by applying the coefficient vector for that pixel to the kernel dictionary. The neural network applies each denoising kernel to the respective pixel to generate a denoised output image.

公开(公告)号：US20210150333A1

公开(公告)日：2021-05-20

申请号：US16686978

申请日：2019-11-18

Applicant: Adobe Inc.

Inventor： Federico Perazzi ,  Zhihao Xia ,  Michael Gharbi ,  Kalyan Sunkavalli

IPC: G06N3/08 ,  G06N20/10 ,  G06T5/00 ,  G06T15/50

Abstract: Certain embodiments involve techniques for efficiently estimating denoising kernels for generating denoised images. For instance, a neural network receives a noisy reference image to denoise. The neural network uses a kernel dictionary of base kernels and generates a coefficient vector for each pixel in the reference image such that the coefficient vector includes a coefficient value for each base kernel in the kernel dictionary, where the base kernels are combined to generate a denoising kernel and each coefficient value indicates a contribution of a given base kernel to a denoising kernel. The neural network calculates the denoising kernel for a given pixel by applying the coefficient vector for that pixel to the kernel dictionary. The neural network applies each denoising kernel to the respective pixel to generate a denoised output image.