Deep Residual Dense Network for Single Image Super-Resolution

In this paper, we propose a deep residual dense network (DRDN) for single image super- resolution. Based on human perceptual characteristics, the in block strategy (RRDB) is exploited to implement various depths architectures. The proposed model exhibits simple sequential structure comprising and blocks with skip connections. It improves stability computational complexity of network, as well quality. We adopt metric learn assess quality reconstructed images. trained Diverse2k dataset, performance evaluated using standard datasets. experimental results confirm that superior performance, better reconstruction than conventional methods.