Abstract: A method of joint denoising and demosaicing using a neural network to generate an output image and to a computing device for implementing the method is provided. The method includes obtaining image data collected by a color filter array (CFA), and jointly performing denoising and demosaicing on the CFA image data using a trained neural network to generate an output image, wherein the neural network has a lightweight U-Net architecture and is trained on a plurality of pairs of training images. One image in each pair of training images is obtained with a lower ISO value than another image in the pair of training images, and is processed by a processing algorithm (image signal processor (ISP)), and the other image in each pair of training images is in a format of CFA image data.

Abstract: Provided is a two-dimensional (2D) image processing method including obtaining a 2D image, processing the obtained 2D image by using a trained convolutional neural network (CNN) to obtain at least one camera parameter and at least one face model parameter from the 2D image, and generating a three-dimensional (3D) face model, based on the obtained at least one camera parameter and at least one face model parameter.

Abstract: A method of joint denoising and demosaicing using a neural network to generate an output image and to a computing device for implementing the method is provided. The method includes obtaining image data collected by a color filter array (CFA), and jointly performing denoising and demosaicing on the CFA image data using a trained neural network to generate an output image, wherein the neural network has a lightweight U-Net architecture and is trained on a plurality of pairs of training images. One image in each pair of training images is obtained with a lower ISO value than another image in the pair of training images, and is processed by a processing algorithm (image signal processor (ISP)), and the other image in each pair of training images is in a format of CFA image data.

Abstract: A method and an illumination manipulation device of performing an object illumination manipulation operation on a first image are provided. The method includes receiving the first image and an input of a light direction, generating a second image with a first object detected from the first image, determining a second object illumination of the second image based on the light direction and replacing a first object illumination of the first image with the second object illumination.

Abstract: Provided is a two-dimensional (2D) image processing method including obtaining a 2D image, processing the obtained 2D image by using a trained convolutional neural network (CNN) to obtain at least one camera parameter and at least one face model parameter from the 2D image, and generating a three-dimensional (3D) face model, based on the obtained at least one camera parameter and at least one face model parameter.

Abstract: A method and an illumination manipulation device of performing an object illumination manipulation operation on a first image are provided. The method includes receiving the first image and an input of a light direction, generating a second image with a first object detected from the first image, determining a second object illumination of the second image based on the light direction and replacing a first object illumination of the first image with the second object illumination.