Abstract: An input-dependent uncorrelated weighting method is provided. The input-dependent uncorrelated weighting method includes: receiving an independent image and a correlated image; calculating differences between pixel estimates at a center pixel and neighboring pixels in the independent image and the correlated image, and an input-dependent kernel; and generating a denoised output image using the denoised pixel estimate at the center pixel, wherein the input-dependent kernel may be calculated by assuming that a sub-averaged estimate for calculating the difference in at least one of the independent image and the correlated image has a symmetric distribution.

Abstract: An apparatus for enhancing image quality includes a combining module configured to combine an independent image having pixels independent from each other and a correlated image including correlation information between pixels and an artificial intelligence (AI) apparatus configured to provide a weight used to combine the independent image and the correlated image.

Abstract: An apparatus for enhancing image quality includes a combining module configured to combine an independent image having pixels independent from each other and a correlated image including correlation information between pixels and an artificial intelligence (AI) apparatus configured to provide a weight used to combine the independent image and the correlated image.

Abstract: The present disclosure relates to an apparatus and method for improving no-flash image quality by removing noise included in a no-flash image using a flash image. An apparatus for improving no-flash image quality using a flash image according to an example embodiment of the present disclosure includes an artificial neural network module which is trained by receiving an image pair of a flash image and a no-flash image and configured to output a convolutional kernel kc and a composite weight wci from a local flash image patch and a no-flash image patch, a convolution unit configured to convolve the flash image patch and the convolutional kernel to generate a consistent flash image patch, and a combining module configured to combine the no-flash image patch and the consistent flash image patch using the composite weight wci to produce a denoised image.

Abstract: The present disclosure provides an apparatus for improving image quality including an image rendering module configured to generate a first data buffer and a second data buffer; an artificial neural network module configured to receive and learn the first data buffer and the second data buffer generated from the image rendering module; and a self-supervised loss calculation module that includes a first self-supervised loss function using pixel values of the first corrected image and the independent and correlated images of the second data buffer and a second self-supervised loss function using pixel values of the second corrected image and the independent and correlated images of the first data buffer, and a method for improving image quality.

Abstract: An apparatus for enhancing image quality includes a combining module configured to combine an independent image having pixels independent from each other and a correlated image including correlation information between pixels and an artificial intelligence (AI) apparatus configured to provide a weight used to combine the independent image and the correlated image.