Abstract: A method for processing image data, including obtaining a raw input image that is captured using a first image sensor under an input illumination condition; obtaining, using a transform estimator, a color transform that maps a characteristic of the first image sensor to a characteristic of a second image sensor, based on the input illumination condition; and generating a raw output image having the characteristic of the second image sensor based on the raw input image and the color transform.

Abstract: A method for processing image data may include: obtaining a raw input image that is captured under an input illumination; obtaining a target illumination from a user input; obtaining an intermediate image having colors captured under a reference illumination, from the raw input image, based on a first color transform that maps the input illumination to the reference illumination in an illumination dataset of raw sensor images that are captured under a plurality of different illuminations; and obtaining an output image having colors captured under the target illumination, from the intermediate image, based on a second color transform that maps the reference illumination in the illumination dataset to the target illumination.

Abstract: An electronic device may be provided to identify fake pixels from an image that is processed via an image processor that uses artificial intelligence (AI) technology and/or an AI camera module to perform image authentication. The electronic device may be configured to: obtain an input image; obtain a processed image of the input image via AI-based model; generate authentication metadata that indicates fake pixels that have been generated by the AI-based model; store the authentication metadata in association with the processed image in the at least one memory; and output the processed image with an indication of the fake pixels.

Abstract: Electronic devices, methods, and non-transitory program storage devices for leveraging machine learning to perform improved image fusion and/or noise reduction are disclosed. When a capture request is received, a neural network may be used to perform fusion and denoising operations on a first set of captured input images. According to some embodiments, the neural network's architecture comprises: a first plurality of network layers configured to compute optical flow information between the first set of input images; a second plurality of network layers configured to perform, at least in part, the fusion and denoising operations on the first set of input images; and a third plurality of skip connections between layers of the second plurality of network layers, wherein parameters for each skip connection of the third plurality of skip connections are warped based on at least part of the optical flow information computed by the first plurality of network layers.

Abstract: A method for processing image data may include: obtaining a first image and a second image that capture a same scene in different views, from a first camera and a second camera, respectively; spatially aligning the first image with the second image; obtaining a color transformation matrix that maps the first image to the second image based on color values of the first image and the second image; obtaining an estimated illuminant color from an output of a neural network by inputting the color transformation matrix to the neural network, wherein the neural network is trained based on a pair of reference images of a same reference scene with a color rendition chart that are captured by different cameras having different spectral sensitivities; and performing a white balance correction on the first image based on the estimated illuminant color to output a corrected first image.

Abstract: A method of generating a low-light image is provided. The method includes receiving a raw image, removing an amount of first illumination from the raw image, applying a low exposure condition to the raw image having the amount of first illumination removed, and applying an amount of low-light illumination to the raw image having the applied low exposure condition.

Abstract: A method is provided. The method includes receiving an input image, extracting at least one feature from the input image, determining at least one local tone curve for a portion of the input image based on the extracted at least one feature, the portion of the input image being less than an overall area of the input image, and generating a toned image based on the at least one local tone curve.

Abstract: A method for generating an image that includes at least one of a vignette effect or a grain effect corresponding to an input image may include obtaining the input image including at least one of the vignette effect or the grain effect; identifying at least one of a vignette parameter or a grain parameter of the input image; obtaining at least one of a vignette filter based on the vignette parameter or a grain layer based on the grain parameter; and generating the image that includes at least one of the vignette effect or the grain effect by applying at least one of the vignette filter or the grain layer to the image.

Abstract: A method for generating an image that includes at least one of a vignette effect or a grain effect corresponding to an input image may include obtaining the input image including at least one of the vignette effect or the grain effect; identifying at least one of a vignette parameter or a grain parameter of the input image; obtaining at least one of a vignette filter based on the vignette parameter or a grain layer based on the grain parameter; and generating the image that includes at least one of the vignette effect or the grain effect by applying at least one of the vignette filter or the grain layer to the image.

Abstract: A system and method of processing of a captured image to facilitate post-processing modification. The method includes: receiving the captured image; down-sampling the captured image to generate a down-sampled image; passing the captured image through an image processing pipeline to generate an initial output image, the image processing pipeline including performing one or more image processing operations on the passed image based on a set of parameters; iteratively passing, in one or more iterations, the down-sampled image through the image processing pipeline to generate an output down-sampled image in each iteration, each iteration using at least one variation to the set of parameters used by the image processing pipeline. Down-sampling can be used to generate a down-sampled initial output image such that a mapping is determined between the down-sampled initial output image and the respective output down-sampled image. The down-sampled images or the mapping can be stored as metadata.

Abstract: A method for processing image data may include: obtaining a first image and a second image that capture a same scene in different views, from a first camera and a second camera, respectively; spatially aligning the first image with the second image; obtaining a color transformation matrix that maps the first image to the second image based on color values of the first image and the second image; obtaining an estimated illuminant color from an output of a neural network by inputting the color transformation matrix to the neural network, wherein the neural network is trained based on a pair of reference images of a same reference scene and a color rendition chart that are captured by different cameras having different spectral sensitivities; and performing a white balance correction on the first image based on the estimated illuminant color to output a corrected first image.