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.

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: An apparatus for customizing camera parameters is provided. The apparatus includes: an image signal processor; and a processor configured to execute instructions to: generate a first edited image based on a first original image, the first original image including first original image data and first metadata indicating a first pattern of processed RGB colors; identify a first mapping function by analyzing the first pattern with respect to a fixed pattern of unprocessed-RGB color space samples; generate first unprocessed image data based on the first mapping function and the first original image; identify image signal processor adjustment parameters based on the first unprocessed image data and the first edited image; and image signal processor parameters of the image signal processor based on the image signal processor adjustment parameters.

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: Computer-based systems, methods and computer program products for colorimetric mapping are provided. In one embodiment of the method, the method includes obtaining a digital reference image, including: illuminating a known reference source with an arbitrary illumination; and determining an observed illumination by receiving light signals from the reference source at an image sensor; determining a color-balance matrix by minimizing an error between colorimetric values of the digital reference image and color corrected pixel values; applying the color-balance matrix to the digital reference image to produce a color-corrected image; determining a color-space transformation matrix that transforms a color space of the image sensor to a perceptual color space; generating a transformed image by applying the color-space transformation matrix to the color corrected image; and outputting the transformed image.

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.

Abstract: An apparatus for customizing camera parameters is provided. The apparatus includes: an image signal processor; and a processor configured to execute instructions to: generate a first edited image based on a first original image, the first original image including first original image data and first metadata indicating a first pattern of processed RGB colors; identify a first mapping function by analyzing the first pattern with respect to a fixed pattern of unprocessed-RGB color space samples; generate first unprocessed image data based on the first mapping function and the first original image; identify image signal processor adjustment parameters based on the first unprocessed image data and the first edited image; and image signal processor parameters of the image signal processor based on the image signal processor adjustment parameters.

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: Computer-based systems, methods and computer program products for colorimetric mapping are provided. In one embodiment of the method, the method includes obtaining a digital reference image, including: illuminating a known reference source with an arbitrary illumination; and determining an observed illumination by receiving light signals from the reference source at an image sensor; determining a color-balance matrix by minimizing an error between colorimetric values of the digital reference image and color corrected pixel values; applying the color-balance matrix to the digital reference image to produce a color-corrected image; determining a color-space transformation matrix that transforms a color space of the image sensor to a perceptual color space; generating a transformed image by applying the color-space transformation matrix to the color corrected image; and outputting the transformed image.

Abstract: The present invention provides telecommunications transport methods and systems for the transparent mapping/demapping of client data signals without the insertion/deletion of idle characters for client data signal rate adaptation. These methods and systems include mapping an incoming client data signal to and demapping an outgoing client data signal from a transport frame comprising: a first segment that is dedicated to client data; a second segment that is dedicated to fixed stuff, wherein the fixed stuff comprises, for example, network management information; a third segment that is dedicated to justification data for supporting a client data signal rate adaptation function; and a fourth segment that is dedicated to justification control information for indicating whether the third segment is used for client data or fixed stuff, wherein the justification control information is redundant for error protection purposes.