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: 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 apparatus for white balance editing, includes a memory storing instructions, and at least one processor configured to execute the instructions to obtain an input image having an original white balance that is corrected by image signal processing, and obtain, using a first neural network, an intermediate representation of the obtained input image, the intermediate representation having the original white balance that is not corrected by the image signal processing. The at least one processor is further configured to execute the instructions to obtain, using a second neural network, a first output image having a first white balance different than the original white balance, based on the obtained intermediate representation.

Abstract: A noise model is iteratively trained to simulate introduction of noise by a capture device, by use of a denoiser and a training data set of pairs of noisy signals. First and second noisy signals of each pair are independently sampled by the capture device from source information corresponding to the pair. Each iteration of training obtains first and second denoised signals from respective noisy signals, then optimizes at least one loss function which sums first and second terms to train both the noise model and the denoiser, where the first term is based on the first denoised signal and the second noisy signal, and the second term is based on the second denoised signal and the first noisy signal. By using noisy samples, the complexities of obtaining “clean” signals are avoided. By using “cross-sample” loss functions, convergence on undesired training results is avoided without complex regularization.

Abstract: An apparatus for white balance editing, includes a memory storing instructions, and at least one processor configured to execute the instructions to obtain an input image having an original white balance that is corrected by image signal processing, and obtain, using a first neural network, an intermediate representation of the obtained input image, the intermediate representation having the original white balance that is not corrected by the image signal processing. The at least one processor is further configured to execute the instructions to obtain, using a second neural network, a first output image having a first white balance different than the original white balance, based on the obtained intermediate representation.

Abstract: The present disclosure provides methods, apparatuses, systems, and computer-readable mediums for encoding color lookup tables (LUTs) by an apparatus. A method includes determining a first identifier corresponding to a first LUT of a plurality of LUTs, providing, to a trained machine learning model, the first identifier and an input lattice, obtaining, from the trained machine learning model, an output lattice corresponding to the first LUT, and performing color manipulation on at least one input image using the output lattice. Each LUT of the plurality of LUTs includes mappings from input color values to output color values. The trained machine learning model has been jointly trained on the plurality of LUTs and identification information corresponding to each LUT in the plurality of LUTs.

Abstract: A method includes collecting a first set of images of a scene with a sensor in accordance with a first condition; collecting a second set of images of the scene with the sensor in accordance with a second condition; collecting one or more noise sample sets based on the first set of images and the second set of images; generating a calibrated noise model based on the one or more noise sample sets; and generating a noisy image by applying the calibrated noise model to a noise free image.

Abstract: The present disclosure provides methods, apparatuses, systems, and computer-readable mediums for demosaicing images by an apparatus. A method includes obtaining, from a plurality of sensors of the apparatus, a plurality of mosaic images, concatenating each image of the plurality of mosaic images with encoded embeddings of the corresponding mosaic pattern of the sensor of the plurality of sensors that captured that image of the plurality of mosaic images, providing the concatenated plurality of mosaic images to a machine learning model, and acquiring, from the machine learning model, a plurality of demosaiced images corresponding to the plurality of mosaic images. Each sensor of the plurality of sensors has a corresponding mosaic pattern from among a plurality of mosaic patterns.

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 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 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 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: 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: 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 apparatus for white balance editing, includes a memory storing instructions, and at least one processor configured to execute the instructions to obtain an input image having an original white balance that is corrected by image signal processing, and obtain, using a first neural network, an intermediate representation of the obtained input image, the intermediate representation having the original white balance that is not corrected by the image signal processing. The at least one processor is further configured to execute the instructions to obtain, using a second neural network, a first output image having a first white balance different than the original white balance, based on the obtained intermediate representation.

Abstract: A method for generating an output image may include obtaining an input image having a first exposure value, generating a plurality of levels that are each respectively associated with a respective representation of the input image, based on the input image, generating the output image having a second exposure value, based on a deep neural network (DNN) including a set of sub-networks and the plurality of levels, and providing the output image.

Abstract: An apparatus for white balance editing, includes a memory storing instructions, and at least one processor configured to execute the instructions to obtain an input image having an original white balance that is corrected by image signal processing, and obtain, using a first neural network, an intermediate representation of the obtained input image, the intermediate representation having the original white balance that is not corrected by the image signal processing. The at least one processor is further configured to execute the instructions to obtain, using a second neural network, a first output image having a first white balance different than the original white balance, based on the obtained intermediate representation.

Abstract: An apparatus for white balance editing, includes a memory storing instructions, and at least one processor configured to execute the instructions to obtain an input image having an original white balance that is corrected by image signal processing, and obtain, using a first neural network, an intermediate representation of the obtained input image, the intermediate representation having the original white balance that is not corrected by the image signal processing. The at least one processor is further configured to execute the instructions to obtain, using a second neural network, a first output image having a first white balance different than the original white balance, based on the obtained intermediate representation.

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 noise model is iteratively trained to simulate introduction of noise by a capture device, by use of a denoiser and a training data set of pairs of noisy signals. First and second noisy signals of each pair are independently sampled by the capture device from source information corresponding to the pair. Each iteration of training obtains first and second denoised signals from respective noisy signals, then optimizes at least one loss function which sums first and second terms to train both the noise model and the denoiser, where the first term is based on the first denoised signal and the second noisy signal, and the second term is based on the second denoised signal and the first noisy signal. By using noisy samples, the complexities of obtaining “clean” signals are avoided. By using “cross-sample” loss functions, convergence on undesired training results is avoided without complex regularization.