Abstract: An information processing apparatus that performs machine learning of a learning model for improving the accuracy of inference of an image even in a case where the characteristics of an image are changed by correction applied to the image. The image processing apparatus includes an image processor configured to perform image processing on a training image and a teacher image according to each of a plurality of correction conditions. Machine learning of the learning model is performed using the training image and the teacher image. A plurality of parameters obtained by performing the machine learning of the learning model are stored in association with the plurality of correction conditions, respectively.

Abstract: An image processing apparatus compress tones of first image data and, by applying a neural network that performs predetermined image processing on image data whose tones have been compressed, output image data on which the predetermined image processing has been performed. The apparatus decompress the tones of the image data on which the predetermined image processing has been performed. The number of bits that represent a pixel value in the neural network is smaller than the number of bits that represent a pixel value of the first image data, and the apparatus compresses tones using a characteristic that the lower the brightness, more tones are allocated.

Abstract: This disclosure provides an image processing apparatus which comprises a first generation unit configured to generate a plurality of images having an M-bit depth per pixel by inputting an input image having a bit depth of N bits, and clipping M-bit segments from mutually different positions within the N bits; a second generation unit including a neural network for an M-bit-depth image and configured to generate, by inputting the plurality of images generated by the first generation unit into the neural network, a plurality of output images corresponding to each of the plurality of images being input; and a third generation unit configured to generate an image having the N-bit depth from the plurality of output images generated by the second generation unit.

Abstract: A generation unit generates a background separation image in which regions of a captured image are classified as a foreground region, a background region, and an unknown region, based on distance distribution information obtained from a plurality of parallax images. An output unit outputs the captured image and the background separation image. A region in which a distance in the distance distribution information is within a first range is classified as the foreground region. A region in which a distance in the distance distribution information is outside a second range broader than the first range is classified as the background region. A region in which a distance in the distance distribution information is outside the first range and inside the second range is classified as the unknown region.

Abstract: An information processing apparatus that performs machine learning of a learning model for improving the accuracy of inference of an image even in a case where the characteristics of an image are changed by correction applied to the image. The image processing apparatus includes an image processor configured to perform image processing on a training image and a teacher image according to each of a plurality of correction conditions. Machine learning of the learning model is performed using the training image and the teacher image. A plurality of parameters obtained by performing the machine learning of the learning model are stored in association with the plurality of correction conditions, respectively.