DJUHNet: A deep representation learning-based scheme for the task of joint image upsampling and hashing

In recent years, numerous efficient schemes that employ deep neural networks have been developed for the task of image hashing. However, not much attention is paid to enhancing the performance and robustness of these deep hashing networks, when the input images do not possess high spatial resolution and visual quality. This is a critical problem, as often accessing high-quality high-resolution images is not guaranteed in real-life applications. In this paper, we propose a novel method for the task of joint image upsampling and hashing, that uses a three-stage design. Specifically, in the first two stages of the proposed scheme, we obtain two deep neural networks, each of which is individually trained for the task of image super resolution and image hashing, respectively. We then fine-tune the two deep networks thus obtained by using the ideas of representation learning and alternating optimization process, in order to produce a set of optimal parameters for the task of joint image upsampling and hashing. The effectiveness of the various ideas utilized for designing the proposed method is demonstrated by performing different experimentations. It is shown that the proposed scheme is able to outperform the state-of-the-art image super resolution and hashing methods, even when they are trained simultaneously in a joint end-to-end manner.