Abstract: 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.

Abstract: 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.

Abstract: 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, real-time color harmonization based on the visible sky region may be used to produce more natural colorization. In some examples, horizon-aware sky alignment and placement with advanced padding may also be used. For example, the horizons of the original image and the replacement image may be automatically detected and aligned, and color harmonization may be performed based on the aligned images.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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, real-time color harmonization based on the visible sky region may be used to produce more natural colorization. In some examples, horizon-aware sky alignment and placement with advanced padding may also be used. For example, the horizons of the original image and the replacement image may be automatically detected and aligned, and color harmonization may be performed based on the aligned images.

Abstract: Methods and systems are provided for presenting and using multiple masks based on a segmented image in editing the image. In particular, multiple masks can be presented to a user using a graphical user interface for easy selection and utilization in the editing of an image. The graphical user interface can include a display configured to display an image, a mask zone configured to display segmentations of the image using masks, and an edit zone configured to display edits to the image. Upon receiving segmentation for the image, the masks can be displayed in the mask zone where the masks are based on a selected segmentation detail level.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Methods and systems are provided for generating enhanced image. A neural network system is trained where the training includes training a first neural network that generates enhanced images conditioned on content of an image undergoing enhancement and training a second neural network that designates realism of the enhanced images generated by the first neural network. The neural network system is trained by determine loss and accordingly adjusting the appropriate neural network(s). The trained neural network system is used to generate an enhanced aesthetic image from a selected image where the output enhanced aesthetic image has increased aesthetics when compared to the selected image.

Abstract: Methods and systems are provided for generating enhanced image. A neural network system is trained where the training includes training a first neural network that generates enhanced images conditioned on content of an image undergoing enhancement and training a second neural network that designates realism of the enhanced images generated by the first neural network. The neural network system is trained by determine loss and accordingly adjusting the appropriate neural network(s). The trained neural network system is used to generate an enhanced aesthetic image from a selected image where the output enhanced aesthetic image has increased aesthetics when compared to the selected image.

Abstract: Methods and systems are provided for presenting and using multiple masks based on a segmented image in editing the image. In particular, multiple masks can be presented to a user using a graphical user interface for easy selection and utilization in the editing of an image. The graphical user interface can include a display configured to display an image, a mask zone configured to display segmentations of the image using masks, and an edit zone configured to display edits to the image. Upon receiving segmentation for the image, the masks can be displayed in the mask zone where the masks are based on a selected segmentation detail level.

Abstract: Systems and techniques for automatic object replacement in an image include receiving an original image and a preferred image. The original image is automatically segmented into an original image foreground region and an original image object region. The preferred image is automatically segmented into a preferred image foreground region and a preferred image object region. A composite image is automatically composed by replacing the original image object region with the preferred image object region such that the composite image includes the original image foreground region and the preferred image object region. An attribute of the composite image is automatically adjusted.

Abstract: Embodiments of the present invention are directed at providing a font similarity system. In one embodiment, a new font is detected on a computing device. In response to the detection of the new font, a pre-computed font list is checked to determine whether the new font is included therein. The pre-computed font list including feature representations, generated independently of the computing device, for corresponding fonts. In response to a determination that the new font is absent from the pre-computed font list, a feature representation for the new font is generated. The generated feature representation capable of being utilized for a similarity analysis of the new font. The feature representation is then stored in a supplemental font list to enable identification of one or more fonts installed on the computing device that are similar to the new font. Other embodiments may be described and/or claimed.

Abstract: Methods and systems are provided for presenting and using multiple masks based on a segmented image in editing the image. In particular, multiple masks can be presented to a user using a graphical user interface for easy selection and utilization in the editing of an image. The graphical user interface can include a display configured to display an image, a mask zone configured to display segmentations of the image using masks, and an edit zone configured to display edits to the image. Upon receiving segmentation for the image, the masks can be displayed in the mask zone where the masks are based on a selected segmentation detail level.

Abstract: Systems and techniques for identification of fonts include receiving a selection of an area of an image including text, where the selection is received from within an application. The selected area of the image is input to a font matching module within the application. The font matching module identifies one or more fonts similar to the text in the selected area using a convolutional neural network. The one or more fonts similar to the text are displayed within the application and the selection and use of the one or more fonts is enabled within the application.

Abstract: A digital medium environment is described to automatically generate a trimap and segment a digital image, independent of any user intervention. An image processing system receives an image and a low-resolution mask for the image, which provides a probability map indicating a likelihood that a pixel in the image mask corresponds to a foreground object in the image. The image processing system analyzes the image to identify content in the image's foreground and background portions, and adaptively generates a trimap for the image based on differences between the identified foreground and background content. By identifying content of the image prior to generating the trimap, the techniques described herein can be applied to a wide range of images, such as images where foreground content is visually similar to background content, and vice versa. Thus, the image processing system can automatically generate trimaps for images having diverse visual characteristics.

Abstract: Systems and techniques for automatic object replacement in an image include receiving an original image and a preferred image. The original image is automatically segmented into an original image foreground region and an original image object region. The preferred image is automatically segmented into a preferred image foreground region and a preferred image object region. A composite image is automatically composed by replacing the original image object region with the preferred image object region such that the composite image includes the original image foreground region and the preferred image object region. An attribute of the composite image is automatically adjusted.