Abstract: The present disclosure provides a method of effectively deblurring a defocus blur in an input image based on an inverse kernel. The defocus deblurring method includes: generating, by an encoder network, an input feature map by encoding the input image; filtering, by an atrous convolution network including a plurality of atrous convolutional layers arranged in parallel, the input feature map to generate an output feature map having reduced blur component; and generating, by a decoder network, an output image having reduced blur from the output feature map with the reduced blur component and the input image.

Abstract: An image processing method and apparatus based on machine learning are disclosed. The image processing method based on machine learning, according to the present invention, may comprise the steps of: generating a first corrected image by inputting an input image to a first convolution neural network; generating an intermediate image on the basis of the input image; performing machine learning on a first loss function of the first convolution neural network on the basis of the first corrected image and the intermediate image; and performing machine learning on a second loss function of the first convolution neural network on the basis of the first corrected image and a natural image.

Abstract: A video quality improvement method may comprise: inputting a structure feature map converted from current target frame by first convolution layer to first multi-task unit and second multi-task unit, which is connected to an output side of first multi-task unit, among the plurality of multi-task units; inputting a main input obtained by adding the structure feature map to a feature space, which is converted by second convolution layer from those obtained by concatenating, in channel dimension, a previous target frame and a correction frame of the previous frame to first multi-task unit; and inputting current target frame to Nth multi-task unit connected to an end of output side of second multi-task unit, wherein Nth multi-task unit outputs a correction frame of current target frame, and machine learning of the video quality improvement model is performed using an objective function calculated through the correction frame of current target frame.

Abstract: Disclosed are an image processing apparatus and an image processing method. The image processing method performed in the image processing apparatus comprises receiving a blurred image; generating an intermediate image from the blurred image, the intermediate image including artifacts with less sensitivity to changes in a blur kernel than the blurred image; and generating a first corrected image by removing the artifacts of the intermediate image using an artifact removal model. Therefore, it is made possible to learn how to remove artifacts of the intermediate image without the image processing apparatus learning a direct deconvolution method.

Abstract: Disclosed are an image processing apparatus and an image processing method. The image processing method performed in the image processing apparatus comprises receiving a blurred image; generating an intermediate image from the blurred image, the intermediate image including artifacts with less sensitivity to changes in a blur kernel than the blurred image; and generating a first corrected image by removing the artifacts of the intermediate image using an artifact removal model. Therefore, it is made possible to learn how to remove artifacts of the intermediate image without the image processing apparatus learning a direct deconvolution method.