Abstract:
A system and method for a blur brush performing adaptive bilateral filtering is disclosed. The method may include receiving user input selecting an area of an image to be filtered, such as by pointing to the image area using the blur brush. The selected image may comprise an edge and a plurality of pixels. The method may operate to the blur brush identifying the edge in the selected image area. The method may operate to apply a filter tool (e.g., a bilateral filter) to the selected image area, while preserving the edge. The methods may be implemented by program instructions executing in parallel on CPU(s) or GPUs.
Abstract:
A system and method for a blur brush performing adaptive bilateral filtering is disclosed. The method may include receiving user input selecting an area of an image to be filtered, such as by pointing to the image area using the blur brush. The selected image may comprise an edge and a plurality of pixels. The method may operate to the blur brush identifying the edge in the selected image area. The method may operate to apply a filter tool (e.g., a bilateral filter) to the selected image area, while preserving the edge. The methods may be implemented by program instructions executing in parallel on CPU(s) or GPUs.
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.
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:
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. 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:
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:
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.
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:
A system and method for performing integral histogram convolution for filtering image data is disclosed. The method may include applying a filter window to a first portion of an image, wherein the filter window includes an interior region and a border region. The method may include generating a plurality of histograms for the pixels in the filter window. The method may include generating spatial weight coefficients for the pixels in the border of the filter window. The method may include generating a plurality of color weight coefficients for the pixels in the filter window. The method may include performing a filtering operation on the pixels in the filter window by applying a respective spatial weight coefficient and a respective color weight coefficient to the values in the plurality of histograms for each respective pixel in the filter window. The methods may be implemented by program instructions executing in parallel on CPU(s) or GPUs.