Data cognition plays an important role in cognitive computing. Cognition of low-resolution (LR) image is a long-stand problem because LR images have insufficient information about objects. For better cognition of LR images, a multi-resolution residual network (MRRN) is proposed to improve image resolution in this paper for cognitive computing systems. In MRRN, a multi-resolution feature learning (MRFL) strategy is introduced to achieve satisfying performance with low computational costs. Inspired by image pyramids, a feature pyramid is designed to implement multi-resolution feature learning in the building unit of the proposed MRRN. Specifically, multi-resolution residual units (MRRUs) are introduced as the building units of the proposed network, which consist of a feature pyramid decomposition stage and a feature reconstruction stage. To obtain informative features, transferred skip links (TSLs) are utilized to transfer fine-grain residual features in the pyramid decomposition stage to the reconstruction stage. The effectiveness of MRFL and TSL is demonstrated by ablation experiments. Also, the tests on standard benchmarks indicate the superiority of the proposed MRRN over other state-of-the-art methods.
Capturing the Complexity of Cognitive Computing Systems: Co-Adaptation Theory for Individuals
