LAUSR

Although recent studies on object recognition using deep neural networks have reported remarkable performance, they have usually assumed that adequate object size and image resolution are available, which may not be guaranteed in real applications. This paper proposes a framework for recognizing objects in very low resolution images through the collaborative learning of two deep neural networks: image enhancement network and object recognition network. The proposed image enhancement network attempts to enhance extremely low resolution images into sharper and more informative images with the use of collaborative learning signals from the object recognition network. The object recognition network with trained weights for high resolution images actively participates in the learning of the image enhancement network. It also utilizes the output from the image enhancement network as augmented learning data to boost its recognition performance on very low resolution objects. Through experiments on various low resolution image benchmark datasets, we verified that the proposed method can improve the image reconstruction and classification performance.