Adobe, Inc.

Mitsubishi Electric Research Laboratories, Inc.

Mitsubishi Electric Corporation

Eric Chan

Sylvain Philippe Paris

Jen-Chan Jeff Chien

Frederic Durand

Vladimir Leonid Bychkovsky

Ronald N Perry

Mark C Hamburg

Hailin Jin

Simon Chen

Joshua Michael Bury

Methods and apparatus for retargeting and prioritized interpolation of lens profiles. A lens profile file may include a set of lens sub-profiles. The camera body and/or settings described in the file may not exactly match that of camera body and/or settings used to capture a target image. A sub-profile processing module may perform a prioritized sub-profile sorting and interpolation method to generate an interpolated sub-profile that may be applied to the target image to correct aberrations including, but not limited to, geometric distortion, lateral chromatic aberration, and vignette. Thus, models generated for a reference camera at a variety of settings may be applied to a target image captured with the same type of lens but with a different camera and/or with different settings that are not exactly modeled in the lens profile file.

Methods and apparatus for retargeting and prioritized interpolation of lens profiles

Techniques of editing images using a digital image editing tool involve defining a local adjustment that includes, as separate masks, a spatial mask and a range mask. The spatial mask specifies a region of the image to be adjusted and the range mask specifies ranges of colors and tones to be adjusted independent of the region specified by the spatial mask. In applying these masks, when a user changes a setting in the digital image editing tool, the digital image editing tool weights the effect of the setting according to the values of the spatial mask and the range mask for each pixel of the image.

Local color range selection

Image compensation value computation techniques are described. In one or more implementations, an image key value is calculated, by a computing device, for image data based on values of pixels of the image data. A tuning value is computed by the computing device using the image key value. The tuning value is configured to adjust how the image data is to be measured to compute an image compensation value. The image compensation value is then computed by the computing device such that a statistic computed in accordance with the tuning value approaches a target value. The image compensation value is applied by the computing device to adjust the image data.

Image compensation value computation

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.

Methods and apparatus for tone mapping high dynamic range images

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.

Methods and apparatus for performing tone mapping on high dynamic range images

Techniques are disclosed relating to automatically adjusting images. In one embodiment, an image may be automatically adjusted based on a regression model trained with a database of raw and adjusted images. In one embodiment, an image may be automatically adjusted based on a model trained by both a database of raw and adjusted images and a small set of images adjusted by a different user. In one embodiment, an image may be automatically adjusted based on a model trained by a database of raw and adjusted images and predicted differences between a user's adjustment to a small set of images and a predicted adjustment based on the database of raw and adjusted images.

Image adjustment

A method aligns a character to a sampling grid of an image, where an outline of the character is specified by input pen commands. Points and contours of the input pen commands are determined. An orientation of each contour is determined. A first directed acyclic graph (DAG) is constructed indicating a hierarchical relationship of related contours. Radicals are determined using the first DAG. Simple segments of the contours are determined and merged independently for each radical. Segment pairs and their hinted coordinates are determined. The segment pairs are sorted and a second DAG is constructed for the sorted segment pairs. Collisions between the segment pairs are resolved using the second DAG. The segments pairs, x-free points, and y-free points are fitted to the sampling grid independently for each radical and a result of the fitting is stored in output pen commands.

Method for improving character outlines using multiple alignment zones

A method aligns a character to a sampling grid of an image. An original uniform width path that traces a skeleton of the character is specified by input pen commands having original coordinates. The width of the original path is rounded to a nearest integer to produce a hinted width. The original path is traversed using the input pen commands to identify segments of the path. The segments are stored in an array. Hinted coordinates are determined for each segment in the array. The array is sorted, and a directed acyclic graph (DAG) is constructed. Collisions between the segments are resolved using the sorted array, the DAG, and the width of the hinted path. The segments are fitted to the sampling grid, and the result is stored in output pen commands, thereby aligning the skeleton of the character to the sampling grid.

Method for improving uniform width character strokes using multiple alignment zones

Methods and apparatus for reducing or removing noise in digital images. Image noise reduction methods are described that may be applied to grayscale and color images, for example RGB images. An image noise reduction method may, before applying a noise filtering technique, transform the image values from linear space to flat noise space in which the noise is independent of the signal. An edge-preserving noise filtering technique may then be applied to the image in flat noise space. After noise filtering is applied, the image is transformed from flat noise space back to linear space. For color images, the flat noise space may be converted from linear color space to luminance-chrominance space before applying the noise filtering technique so that different filters can be applied to luminance and color channels. After applying the noise filtering technique, the image is converted back to linear color space.

Growing community of inventors

Belmont, MA, United States of America

Eric Chan