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公开(公告)号：US11551390B2
公开(公告)日：2023-01-10
申请号：US16985927
申请日：2020-08-05
申请人： Adobe Inc.
摘要： The present disclosure relates to systems, methods, and non-transitory computer readable media for generating deterministic enhanced digital images based on parallel determinations of pixel group offsets arranged in pixel waves. For example, the disclosed systems can utilize a parallel wave analysis to propagate through pixel groups in a pixel wave of a target region within a digital image to determine matching patch offsets for the pixel groups. The disclosed systems can further utilize the matching patch offsets to generate a deterministic enhanced digital image by filling or replacing pixels of the target region with matching pixels indicated by the matching patch offsets.
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公开(公告)号：US20220398712A1
公开(公告)日：2022-12-15
申请号：US17820649
申请日：2022-08-18
申请人： Adobe Inc.
摘要： The present disclosure relates to systems, methods, and non-transitory computer readable media for generating modified digital images by utilizing a patch match algorithm to generate nearest neighbor fields for a second digital image based on a nearest neighbor field associated with a first digital image. For example, the disclosed systems can identify a nearest neighbor field associated with a first digital image of a first resolution. Based on the nearest neighbor field of the first digital image, the disclosed systems can utilize a patch match algorithm to generate a nearest neighbor field for a second digital image of a second resolution larger than the first resolution. The disclosed systems can further generate a modified digital image by filling a target region of the second digital image utilizing the generated nearest neighbor field.
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43.发明申请公开(公告)号：US20220292650A1
公开(公告)日：2022-09-15
申请号：US17202019
申请日：2021-03-15
申请人： Adobe Inc.
发明人： Sohrab Amirghodsi , Lingzhi Zhang , Zhe Lin , Connelly Barnes , Elya Shechtman
摘要： The present disclosure relates to systems, methods, and non-transitory computer readable media for accurately, efficiently, and flexibly generating modified digital images utilizing a guided inpainting approach that implements a patch match model informed by a deep visual guide. In particular, the disclosed systems can utilize a visual guide algorithm to automatically generate guidance maps to help identify replacement pixels for inpainting regions of digital images utilizing a patch match model. For example, the disclosed systems can generate guidance maps in the form of structure maps, depth maps, or segmentation maps that respectively indicate the structure, depth, or segmentation of different portions of digital images. Additionally, the disclosed systems can implement a patch match model to identify replacement pixels for filling regions of digital images according to the structure, depth, and/or segmentation of the digital images.
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公开(公告)号：US20220172331A1
公开(公告)日：2022-06-02
申请号：US17651435
申请日：2022-02-17
申请人： Adobe Inc.
摘要： Techniques are disclosed for filling or otherwise replacing a target region of a primary image with a corresponding region of an auxiliary image. The filling or replacing can be done with an overlay (no subtractive process need be run on the primary image). Because the primary and auxiliary images may not be aligned, both geometric and photometric transformations are applied to the primary and/or auxiliary images. For instance, a geometric transformation of the auxiliary image is performed, to better align features of the auxiliary image with corresponding features of the primary image. Also, a photometric transformation of the auxiliary image is performed, to better match color of one or more pixels of the auxiliary image with color of corresponding one or more pixels of the primary image. The corresponding region of the transformed auxiliary image is then copied and overlaid on the target region of the primary image.
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公开(公告)号：US20210357684A1
公开(公告)日：2021-11-18
申请号：US15930539
申请日：2020-05-13
申请人： Adobe Inc.
发明人： Sohrab Amirghodsi , Zhe Lin , Yilin Wang , Tianshu Yu , Connelly Barnes , Elya Shechtman
摘要： A panoptic labeling system includes a modified panoptic labeling neural network (“modified PLNN”) that is trained to generate labels for pixels in an input image. The panoptic labeling system generates modified training images by combining training images with mask instances from annotated images. The modified PLNN determines a set of labels representing categories of objects depicted in the modified training images. The modified PLNN also determines a subset of the labels representing categories of objects depicted in the input image. For each mask pixel in a modified training image, the modified PLNN calculates a probability indicating whether the mask pixel has the same label as an object pixel. The modified PLNN generates a mask label for each mask pixel, based on the probability. The panoptic labeling system provides the mask label to, for example, a digital graphics editing system that uses the labels to complete an infill operation.
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公开(公告)号：US20200372619A1
公开(公告)日：2020-11-26
申请号：US16420782
申请日：2019-05-23
申请人： ADOBE INC.
发明人： Sohrab Amirghodsi , Elya Shechtman , Derek Novo
摘要： Embodiments of the present invention provide systems, methods, and computer storage media for automatically synthesizing a content-aware sampling region for a hole-filling algorithm such as content-aware fill. Given a source image and a hole (or other target region to fill), a sampling region can be synthesized by identifying a band of pixels surrounding the hole, clustering these pixels based on one or more characteristics (e.g., color, x/y coordinates, depth, focus, etc.), passing each of the resulting clusters as foreground pixels to a segmentation algorithm, and unioning the resulting pixels to form the sampling region. The sampling region can be stored in a constraint mask and passed to a hole-filling algorithm such as content-aware fill to synthesize a fill for the hole (or other target region) from patches sampled from the synthesized sampling region.
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公开(公告)号：US20240362757A1
公开(公告)日：2024-10-31
申请号：US18307546
申请日：2023-04-26
申请人： Adobe Inc.
发明人： Sohrab Amirghodsi , Lingzhi Zhang , Connelly Barnes , Elya Shechtman , Yuqian Zhou , Zhe Lin
CPC分类号： G06T5/77 , G06T5/30 , G06T5/50 , G06T7/11 , G06T2207/20076 , G06T2207/20081 , G06T2207/20084
摘要： The present disclosure relates to systems, non-transitory computer-readable media, and methods for inpainting digital images utilizing mask-robust machine-learning models. In particular, in one or more embodiments, the disclosed systems obtain an initial mask for an object depicted in a digital image. Additionally, in some embodiments, the disclosed systems generate, utilizing a mask-robust inpainting machine-learning model, an inpainted image from the digital image and the initial mask. Moreover, in some implementations, the disclosed systems generate a relaxed mask that expands the initial mask. Furthermore, in some embodiments, the disclosed systems generate a modified image by compositing the inpainted image and the digital image utilizing the relaxed mask.
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公开(公告)号：US20230360180A1
公开(公告)日：2023-11-09
申请号：US17661985
申请日：2022-05-04
申请人： Adobe Inc.
发明人： Haitian Zheng , Zhe Lin , Jingwan Lu , Scott Cohen , Elya Shechtman , Connelly Barnes , Jianming Zhang , Ning Xu , Sohrab Amirghodsi
CPC分类号： G06T5/005 , G06T3/4046 , G06V10/40 , G06T2207/20084
摘要： The present disclosure relates to systems, methods, and non-transitory computer readable media that generate inpainted digital images utilizing a cascaded modulation inpainting neural network. For example, the disclosed systems utilize a cascaded modulation inpainting neural network that includes cascaded modulation decoder layers. For example, in one or more decoder layers, the disclosed systems start with global code modulation that captures the global-range image structures followed by an additional modulation that refines the global predictions. Accordingly, in one or more implementations, the image inpainting system provides a mechanism to correct distorted local details. Furthermore, in one or more implementations, the image inpainting system leverages fast Fourier convolutions block within different resolution layers of the encoder architecture to expand the receptive field of the encoder and to allow the network encoder to better capture global structure.
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公开(公告)号：US20230214967A1
公开(公告)日：2023-07-06
申请号：US18089218
申请日：2022-12-27
申请人： Adobe Inc.
CPC分类号： G06T5/005 , G06T5/50 , G06T3/0093 , G06N3/08 , G06T2207/10024 , G06T2207/20224 , G06T2207/20084 , G06T2207/20081 , G06T2207/20221
摘要： Various disclosed embodiments are directed to inpainting one or more portions of a target image based on merging (or selecting) one or more portions of a warped image with (or from) one or more portions of an inpainting candidate (e.g., via a learning model). This, among other functionality described herein, resolves the inaccuracies of existing image inpainting technologies.
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公开(公告)号：US20220292654A1
公开(公告)日：2022-09-15
申请号：US17200338
申请日：2021-03-12
申请人： Adobe Inc.
发明人： He Zhang , Yifan Jiang , Yilin Wang , Jianming Zhang , Kalyan Sunkavalli , Sarah Kong , Su Chen , Sohrab Amirghodsi , Zhe Lin
摘要： The present disclosure relates to systems, methods, and non-transitory computer readable media for accurately, efficiently, and flexibly generating harmonized digital images utilizing a self-supervised image harmonization neural network. In particular, the disclosed systems can implement, and learn parameters for, a self-supervised image harmonization neural network to extract content from one digital image (disentangled from its appearance) and appearance from another from another digital image (disentangled from its content). For example, the disclosed systems can utilize a dual data augmentation method to generate diverse triplets for parameter learning (including input digital images, reference digital images, and pseudo ground truth digital images), via cropping a digital image with perturbations using three-dimensional color lookup tables (“LUTs”). Additionally, the disclosed systems can utilize the self-supervised image harmonization neural network to generate harmonized digital images that depict content from one digital image having the appearance of another digital image.
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