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: 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: In one embodiment, a method includes generating, from an accessed image, one or more image patches, and for each image patch: (1) accessing a set of point-spread functions (PSFs), wherein each PSF in the set of PSFs corresponds to one of a plurality of points in the image patch; (2) generating a set of deconvolved image patches by deconvolving the image patch with each PSF from the set of PSFs for that patch; (3) determining, for each of one or more portions of the image patch, a set of weights, wherein each weight in the set of weights is associated with one of the deconvolved image patches; and (4) generating a restored image patch by interpolating the set of deconvolved image patches based on the set of weights.

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: 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: 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.