An image capture device obtains first image data from a front facing camera on a front face of the device including a display. The first image data represents a subject within a field of view of the image capture device. Facial recognition is performed in response to the first image data from the front facing camera. The front image data is used for determining whether any subject is a priority subject, of whom at least one priority subject image-indicative data is accessible by the image capture device. A region of interest is selected, corresponding to the subject determined to be a priority subject. Automatic focus, automatic exposure, or automatic white balance is performed using the selected region of interest. Second image data from the front facing camera are captured based on the automatic focus, automatic exposure, or automatic white balance using the selected region of interest.

An input signal for each of three primary color components is converted into a luminance signal and a color signal by a color space conversion part. A gain setting part sets a gain for the color signal obtained by color space conversion according to a signal level of a setting reference signal generated on the basis of the input signal, for example, the luminance signal. A gain adjustment part performs gain adjustment of the color signal with the gain set by the gain setting part, and in a case where the luminance signal is larger than a threshold set according to a dynamic range for each color component, the gain adjustment part makes the subject achromatic so that, even in a case where a light amount of the subject is high, influence of a difference in the dynamic range is little.

An image processing apparatus and a method are provided. The apparatus comprises a plurality of processing modules configured to operate in series to refine a raw image captured by a camera, the modules comprising a first module and a second module, each of which independently implements a respective trained artificial intelligence model, wherein: the first module implements an image transformation operation that performs an operation from the set comprising: (i) an essentially pixel-level operation that increases sharpness of an image input to the module, (ii) an essentially pixel-level operation that decreases sharpness of an image input to the module, (iii) an essentially pixel-block-level operation on an image input to the module; and the second module as a whole implements a different operation from the said set.

An image processing apparatus and a method are provided. The apparatus comprises a plurality of processing modules configured to operate in series to refine a raw image captured by a camera, the modules comprising a first module and a second module, each of which independently implements a respective trained artificial intelligence model, wherein: the first module implements an image transformation operation that performs an operation from the set comprising: (i) an essentially pixel-level operation that increases sharpness of an image input to the module, (ii) an essentially pixel-level operation that decreases sharpness of an image input to the module, (iii) an essentially pixel-block-level operation on an image input to the module; and the second module as a whole implements a different operation from the said set.

An image processing apparatus for controlling white balance of an image is provided. The apparatus detects a white region from image data and calculates a first white balance correction value based on data of the white region. The apparatus also detects, with use of machine learning, a region of a subject that has a preset specific color, from the image data, and calculates a second white balance correction value based on a color of the region of the subject. The apparatus calculates a white balance correction value to be applied to the image data, based on the first white balance correction value and the second white balance correction value.

A method of auto white balancing, including, receiving an original image, determining an RG logarithmic ratio of a set of red to green channel values of the original image, determining a BG logarithmic ratio of a set of blue to green channel values of the original image, determine an original two-dimensional histogram utilizing the RG logarithmic ratio and the BG logarithmic ratio, determine a Gaussian-blur two-dimensional histogram utilizing the RG logarithmic ratio and the BG logarithmic ratio, determining a sharpened two-dimensional histogram of a sharpened image utilizing the RG logarithmic ratio and the BG logarithmic ratio, determining a Laplacian-edge two-dimensional histogram of a Laplacian-edge image utilizing the RG logarithmic ratio and the BG logarithmic ratio and determining a white balancing gain utilizing a neural network based on the original 2D histogram, the Gaussian-blur 2D histogram, the sharpened 2D histogram and the Laplacian-edge 2D histogram.

Methods, systems, devices, and tangible non-transitory computer readable media for haze reduction are provided. The disclosed technology can include generating feature vectors based on an input image including points. The feature vectors can correspond to feature windows associated with features of different portions of the points. Based on the feature vectors and a machine-learned model, a haze thickness map can be generated. The haze thickness map can be associated with an estimate of haze thickness at each of the points. Further, the machine-learned model can estimate haze thickness associated with the features. A refined haze thickness map can be generated based on the haze thickness map and a guided filter. A dehazed image can be generated based on application of the refined haze thickness map to the input image. Furthermore, a color corrected dehazed image can be generated based on performance of color correction operations on the dehazed image.

An image processing device for performing correction processing on original image data generated by an image-capturing element configured to receive light with a plurality of pixels through a color filter including segments of a red color and at least one complementary color includes a processing circuitry being configured to perform operations including converting the original image data into primary color-based image data represented in a primary color-based color space, acquiring a statistical value of a plurality of pieces of pixel data corresponding to the plurality of pixels from the primary color-based image data, calculating a correction parameter by using the statistical value, and correcting the original image data based on the correction parameter.

To realize an illumination control device and the like that can make color reproductivity of an image compatible with an S/N ratio at the time of infrared illumination, the illumination control device includes a visible light measurement unit configured to measure an amount of visible light components; an infrared illumination unit configured to perform infrared illumination; and a control unit configured to cause an intensity of the infrared illumination by the infrared illumination unit to gradually change as the amount of visible light components measured by the visible light measurement unit decreases from a first threshold to a second threshold.

A processor-implemented method with image processing includes: providing retouch result candidates of an input image to a user in response to applying vector value candidates to a style vector; determining a vector value of the style vector based on a selection of the user for the retouch result candidates; determining an adjustment parameter set corresponding to the determined vector value of the style vector; and generating a retouch result by adjusting the input image based on the adjustment parameter set.