Memory‐ and time‐efficient dense network for single‐image super‐resolution

Abstract

Dense connections in convolutional neural networks (CNNs), which connect each layer to every other layer, can compensate for mid/high-frequency information loss and further enhance high-frequency signals. However, dense CNNs suffer from high memory usage due to the accumulation of concatenating feature-maps stored in memory. To overcome this problem, a two-step approach is proposed that learns the representative concatenating feature-maps. Specifically, a convolutional layer with many more filters is used before concatenating layers to learn richer feature-maps. Therefore, the irrelevant and redundant feature-maps are discarded in the concatenating layers. The proposed method results in 24% and 6% less memory usage and test time, respectively, in comparison to single-image super-resolution (SISR) with the basic dense block. It also improves the peak signal-to-noise ratio by 0.24 dB. Moreover, the proposed method, while producing competitive results, decreases the number of filters in concatenating layers by at least a factor of 2 and reduces the memory consumption and test time by 40% and 12%, respectively. These results suggest that the proposed approach is a more practical method for SISR.