Abstract: Example methods, apparatuses, and computer program products for processing images by using a convolutional neural network (CNN) are described herein. In one example, an original image is received from an image source. The original image has a predefined size and high resolution, and is represented in a first color space supported by the image source. Then, an intermediate image is obtained by downscaling the original image in the first color space, and converted from the first color space to a second color space. Next, a restored image is obtained by upscaling the converted intermediate image to the predefined size of the original image. Upscaling is performed by using the CNN on the original image and the converted intermediate image as inputs and return the restored image. The CNN is pre-trained on a set of triplets, comprising a past original image, a converted past intermediate image and a past restored image.

Abstract: An image processing method. The method includes: An electronic device obtains N images, where the N images have a same quantity of pixels and a same pixel location arrangement, and N is an integer greater than 1; the electronic device obtains, based on feature values of pixels located at a same location in the N images, a reference value of the corresponding location; the electronic device determines a target pixel of each location based on a reference value of the location; and the electronic device generates a target image based on the target pixel of each location.

Abstract: A method, apparatus, and computer program product for processing images by using a convolutional neural network (CNN) are proposed. An original image is received from an image source. The original image has a predefined size and high resolution, and is represented in a first color space supported by the image source. Then, an intermediate image is obtained by downscaling the original image in the first color space, and converted from the first color space to a second color space. Next, a restored image is obtained by upscaling the converted intermediate image to the predefined size of the original image. Said upscaling is performed by using the CNN on the original image and the converted intermediate image as inputs and return the restored image. The CNN is pre-trained on a set of triplets, comprising a past original image, a converted past intermediate image and a past restored image.

Abstract: The present application relates to the field of neural networks, in particular Binary Neural Networks (BNN). The application proposes a device and method for regularization of a BNN. The device is configured to obtain binary weights of the BNN, and to change the binary weights of the BNN using a backpropagation method. Thereby, changing the binary weights increases or minimizes decrease of an information entropy of a weight distribution.

Abstract: An image processing method. The method includes: An electronic device obtains N images, where the N images have a same quantity of pixels and a same pixel location arrangement, and N is an integer greater than 1; the electronic device obtains, based on feature values of pixels located at a same location in the N images, a reference value of the corresponding location; the electronic device determines a target pixel of each location based on a reference value of the location; and the electronic device generates a target image based on the target pixel of each location.