Adobe, Inc.

Betty Leong

I-Ming Pao

Alan Lee Erickson

Jianming Zhang

Sarah Kong

Hyunghwan Byun

Sarah Stuckey

Guotong Feng

Zhe Lin

Kalyan Krishna Sunkavalli

Bradee Rae Evans

Yukie Takahashi

Frederick Mandia

Meredith Payne Stotzner

Quynn Megan Le

The present disclosure provides systems and methods for image editing. Embodiments of the present disclosure provide an image editing system for perform image object replacement or image region replacement (e.g., an image editing system for replacing an object or region of an image with an object or region from another image). For example, the image editing system may replace a sky portion of an image with a more desirable sky portion from a different replacement image. The original image and the replacement image (e.g., the image including a desirable object or region) include layers of masks. A sky from the replacement image may replace the sky of the image to produce an aesthetically pleasing composite image.

Sky replacement

Systems and methods for image editing are described. Embodiments of the present disclosure provide an image editing system for performing image object replacement or image region replacement (e.g., an image editing system for replacing an object or region of an image with an object or region from another image). For example, the image editing system may replace a sky portion of an image with a more desirable sky portion from a different replacement image. According to some embodiments described herein, thumbnails, region location information, and image metadata from multiple preset images can be stored together and loaded for presentation and selection of a preset image for replacing a region of an image. Once a preset image (e.g., an image with a replacement sky) is selected, a high-resolution version of the image can be loaded and used to generate a composite image.

Sky replacement preset loading

The present disclosure relates to systems, method, and computer readable media that iteratively apply a neural network to a digital image at a reduced resolution to automatically identify pixels of salient objects portrayed within the digital image. For example, the disclosed systems can generate a reduced-resolution digital image from an input digital image and apply a neural network to identify a region corresponding to a salient object. The disclosed systems can then iteratively apply the neural network to additional reduced-resolution digital images (based on the identified region) to generate one or more reduced-resolution segmentation maps that roughly indicate pixels of the salient object. In addition, the systems described herein can perform post-processing based on the reduced-resolution segmentation map(s) and the input digital image to accurately determine pixels that correspond to the salient object.

Iteratively applying neural networks to automatically identify pixels of salient objects portrayed in digital images

In some embodiments, an image editing application stores, based on a first selection input, a selection state that identifies a first image portion of a target image as included in a preview image displayed in a mask-based editing interface of the image editing application. An edit to the preview image generated from the selected first image portion is applied in the mask-based editing interface. The image editing application also updates an edit state that tracks the edit applied to the preview image. The image editing application modifies, based on a second selection input received via the mask-based editing interface, the selection state to include a second image portion in the preview image. The edit state is maintained with the applied edit concurrently with modifying the selection state. The image editing application applies the edit to the modified preview image in the mask-based editing interface.

Editing dynamically selected portions of target images in a mask-based editing interface

Growing community of inventors

Los Altos, CA, United States of America

Betty Leong