Abstract: A healing component that heals foreground pixels with background pixels is provided. In some embodiments, the healing component is programmed or otherwise configured to respond to a single healing request by identifying a plurality of regions within a selected area and healing each region of the plurality of regions independently of other regions.

Abstract: Systems and methods are disclosed for segregating target individuals represented in a probe digital image from background pixels in the probe digital image. In particular, in one or more embodiments, the disclosed systems and methods train a neural network based on two or more of training position channels, training shape input channels, training color channels, or training object data. Moreover, in one or more embodiments, the disclosed systems and methods utilize the trained neural network to select a target individual in a probe digital image. Specifically, in one or more embodiments, the disclosed systems and methods generate position channels, training shape input channels, and color channels corresponding the probe digital image, and utilize the generated channels in conjunction with the trained neural network to select the target individual.

Abstract: A healing component that heals foreground pixels with background pixels is provided. In some embodiments, the healing component is programmed or otherwise configured to respond to a single healing request by identifying a plurality of regions within a selected area and healing each region of the plurality of regions independently of other regions.

Abstract: Techniques are disclosed for automatically transferring a style of at least two reference images to an input image. The resulting transformation of the input image matches the visual styles of the reference images without changing the identity of the subject of the input image. Each image is decomposed into levels of detail with corresponding energy levels and a residual. A style transfer operation is performed at each energy level and residual using the reference image that most closely matches the input image at each energy level. The transformations of each level of detail and the residual of the input image are aggregated to generate an output image having the styles of the reference images. In some cases, the transformations are performed on the foreground of the input image, and the background can be transformed by an amount that is proportional to the aggregated transformations of the foreground.

Abstract: Embodiments described herein are directed to methods and systems for facilitating control of smoothness of transitions between images. In embodiments, a difference of color values of pixels between a foreground image and the background image are identified along a boundary associated with a location at which to paste the foreground image relative to the background image. Thereafter, recursive down sampling of a region of pixels within the boundary by a sampling factor is performed to produce a plurality of down sampled images having color difference indicators associated with each pixel of the down sampled images. Such color difference indicators indicate whether a difference of color value exists for the corresponding pixel. To effectuate a seamless transition, the color difference indicators are normalized in association with each recursively down sampled image.

Abstract: A device for creating a digital image is described. The device that may receive two or more input images. The input images may have a common viewpoint with unique lighting configurations. The device may utilize basis light functions and modifiers to create a final composite image. The device may include a computing device configured to provide user a graphical user interface enabling a user to create a final composite image. Devices described herein may be useful for computational light workflows in photography.

Abstract: In example embodiments, systems and methods for learning and using user preferences for image adjustments are presented. In example embodiments, a new image is received. A correction parameter based on previously stored user adjustments for similar images is determined. A user style that is an adjusted version of the new image is generated by applying the correction parameter. The user style is provided on a user interface. A user adjustment is received. Based on determining that a user sample image is within a predetermined threshold of closeness to the new image, data corresponding to the user sample image is replaced with new adjustment data for the new image in a database of user sample images used to generate the correction parameter. Based on determining that no user sample images are within the predetermined threshold of closeness, new adjustment data is appended to the database used to generate the correction parameter.

Abstract: In example embodiments, systems and methods for learning and using user preferences for image adjustments are presented. In example embodiments, a new image is received. A correction parameter based on previously stored user adjustments for similar images is determined. A user style that is an adjusted version of the new image is generated by applying the correction parameter. The user style is provided on a user interface. A user adjustment is received. Based on determining that a user sample image is within a predetermined threshold of closeness to the new image, data corresponding to the user sample image is replaced with new adjustment data for the new image in a database of user sample images used to generate the correction parameter. Based on determining that no user sample images are within the predetermined threshold of closeness, new adjustment data is appended to the database used to generate the correction parameter.

Abstract: In various example embodiments, a system and method for using machine learning to define user controls for image adjustment is provided. In example embodiments, a new image to be adjusted is received. A weight is applied to reference images of a reference dataset based on a comparison of content of the new image to the reference image of the reference dataset. A plurality of basis styles is generated by applying weighted averages of adjustment parameters corresponding to the weighted reference images to the new image. Each of the plurality of basis styles comprises a version of the new image with an adjustment of at least one image control based on the weighted averages of the adjustment parameters of the reference dataset. The plurality of basis styles is provided to a user interface of a display device.

Abstract: Techniques are disclosed relating to lens modeling. In one embodiment, a lens model may be generated based on reference images of a pre-determined, known geometric pattern. The lens model may represent a spatially variant blur pattern across the image field of the lens used to capture the reference images. In one embodiment, the lens model may include Gaussian approximations of the blur that may minimize the difference between a location within a reference image and a corresponding location of a pre-determined, known geometric pattern. In one embodiment, the generated lens model may be applied to deblur a new image.

Abstract: Methods, apparatus, and computer-readable storage media for tone mapping High Dynamic Range (HDR) images. The HDR image is separated into luminance and color. Luminance is processed according to the parameters to obtain a base layer and a detail layer. The base layer is compressed into a lower dynamic range and the detail layer is adjusted according to the parameters. The compressed base layer, the detail layer, and the color component may be output as separate layers, and various image processing tools and techniques may be applied to the component layers separately to modify the layer(s). One or more tone-mapped images may be generated by merging the modified layers. Thus, each layer of the tone-mapped image may be processed separately using various image processing tools or techniques to modify the output of the tone mapping technique in a wide variety of ways.

Abstract: In various example embodiments, a system and method for using machine learning to define user controls for image adjustment is provided. In example embodiments, a new image to be adjusted is received. A weight is applied to reference images of a reference dataset based on a comparison of content of the new image to the reference image of the reference dataset. A plurality of basis styles is generated by applying weighted averages of adjustment parameters corresponding to the weighted reference images to the new image. Each of the plurality of basis styles comprises a version of the new image with an adjustment of at least one image control based on the weighted averages of the adjustment parameters of the reference dataset. The plurality of basis styles is provided to a user interface of a display device.

Abstract: One exemplary embodiment involves receiving an image with a set of undefined pixels and a set of defined pixels and recursively modifying the image to generate a seamless composition comprising only defined pixels. Disclosed are embodiments for recursively modifying the image by recursively down sampling the image by a factor to generate a plurality of down sampled images until the down sampled image generated at each recursive down sampling lacks undefined pixels and then recursively up sampling each one of the down sampled images by the factor to generate an up sampled image from the respective down sampled image. Additionally, at each recursive up sampling instance, pasting the next recursively occurring down sampled image on the up sampled image to generate the next recursively occurring image for up sampling.

Abstract: A device for creating a digital image is described. The device that may receive two or more input images. The input images may have a common viewpoint with unique lighting configurations. The device may utilize basis light functions and modifiers to create a final composite image. The device may include a computing device configured to provide user a graphical user interface enabling a user to create a final composite image. Devices described herein may be useful for computational light workflows in photography.

Abstract: Techniques are disclosed relating to modifying an automatically predicted adjustment. In one embodiment, the automatically predicted adjustment may be adjusted, for example, based on a rule. The automatically predicted adjustment may be based on a machine learning prediction. A new image may be globally adjusted based on the modified automatically predicted adjustment.

Abstract: In example embodiments, systems and methods for learning and using user preferences for image adjustments are presented. In example embodiments, a new image is received. A correction parameter based on previously stored user adjustments for similar images is determined. A user style that is an adjusted version of the new image is generated by applying the correction parameter. The user style is provided on a user interface. A user adjustment is received. Based on determining that a user sample image is within a predetermined threshold of closeness to the new image, data corresponding to the user sample image is replaced with new adjustment data for the new image in a database of user sample images used to generate the correction parameter. Based on determining that no user sample images are within the predetermined threshold of closeness, new adjustment data is appended to the database used to generate the correction parameter.

Abstract: In various example embodiments, a system and method for providing a visual example-based user interface for adjusting images is provided. In example embodiments, a new image to be adjusted is received. A plurality of basis styles is generated by applying adjustment parameters to the new image. Each of the plurality of basis styles comprises an adjusted version of the new image with an adjustment of at least one image control. A user interface is provided that positions a version of the new image in a center portion and positions the plurality of basis styles on the user interface based on the adjustment parameters applied to the new image. A control mechanism is provided over the version of the new image whereby movement of the control mechanism to a new position on the user interface causes the version of the new image to adjust accordingly.

Abstract: In various example embodiments, a system and method for using machine learning to define user controls for image adjustment is provided. In example embodiments, a new image to be adjusted is received. A weight is applied to reference images of a reference dataset based on a comparison of content of the new image to the reference image of the reference dataset. A plurality of basis styles is generated by applying weighted averages of adjustment parameters corresponding to the weighted reference images to the new image. Each of the plurality of basis styles comprises a version of the new image with an adjustment of at least one image control based on the weighted averages of the adjustment parameters of the reference dataset. The plurality of basis styles is provided to a user interface of a display device.

Abstract: Techniques are disclosed relating to generating generic labels, translating generic labels to image pipeline-specific labels, and automatically adjusting images. In one embodiment, generic labels may be generated. Generic algorithm parameters may be generated based on training a regression algorithm with the generic labels. The generic labels may be translated to pipeline-specific labels, which may be usable to automatically adjust an image.

Abstract: Methods, apparatus, and computer-readable storage media for tone mapping High Dynamic Range (HDR) images. An input HDR image is separated into luminance and color. Luminance is processed to obtain a base layer and a detail layer. The base layer is compressed according to a non-linear remapping function to reduce the dynamic range, and the detail layer is adjusted. The layers are combined to generate output luminance, and the output luminance and color are combined to generate an output image. A base layer compression technique may be used that analyzes the details and compresses the base layer accordingly to provide space at the top of the intensity scale where the details are displayed to thus generate output images that are visually better than images generated using conventional techniques. User interface elements may be provided via which a user may control one or more parameters of the tone mapping method.