Abstract: An apparatus for multi-label class classification based on a coarse-to-fine convolutional neural network includes: a processor; and a memory connected to the processor, in which the memory stores program instructions executed by the processor to generate a plurality of hierarchical structure based group labels for a plurality of classes to be classified by using a disjoint grouping method, predict classes which belong to the plurality of group labels, respectively among the plurality of classes by using a coarse-to-fine convolutional neural network including a main network and one or more subnetworks, complete learning of the coarse-to-fine convolutional network through the prediction, and classify one or more classes included in the image by receiving a feature map input from a last convolutional layer of the one or more subnetworks by the main network of the coarse-to-fine convolutional neural network of which learning is completed.

Abstract: The apparatus for removing haze from an image using a fuzzy membership function includes memory configured to store computer-readable instructions; and a processor configured to execute the instructions, wherein the processor calculates a hazy value and a transmission map using an input image including a hazy component and generates a restored image from which the hazy component has been removed using the hazy value and the transmission map, and wherein the processor selects a first area in the input image, calculates a fuzzy membership function of pixels of the input image with respect to the hazy value using brightness values of the pixels of the input image and brightness values of pixels of the first area, and calculates the transmission map using the brightness values of pixels of the first area and the fuzzy membership function.

Abstract: The apparatus for removing haze from an image using a fuzzy membership function includes memory configured to store computer-readable instructions; and a processor configured to execute the instructions, wherein the processor calculates a hazy value and a transmission map using an input image including a hazy component and generates a restored image from which the hazy component has been removed using the hazy value and the transmission map, and wherein the processor selects a first area in the input image, calculates a fuzzy membership function of pixels of the input image with respect to the hazy value using brightness values of the pixels of the input image and brightness values of pixels of the first area, and calculates the transmission map using the brightness values of pixels of the first area and the fuzzy membership function.