  Although remarkable progress has been made on single image super-resolution
due to the revival of deep convolutional neural networks, deep learning methods
are confronted with the challenges of computation and memory consumption in
practice, especially for mobile devices. Focusing on this issue, we propose an
efficient residual dense block search algorithm with multiple objectives to
hunt for fast, lightweight and accurate networks for image super-resolution.
Firstly, to accelerate super-resolution network, we exploit the variation of
feature scale adequately with the proposed efficient residual dense blocks. In
the proposed evolutionary algorithm, the locations of pooling and upsampling
operator are searched automatically. Secondly, network architecture is evolved
with the guidance of block credits to acquire accurate super-resolution
network. The block credit reflects the effect of current block and is earned
during model evaluation process. It guides the evolution by weighing the
sampling probability of mutation to favor admirable blocks. Extensive
experimental results demonstrate the effectiveness of the proposed searching
method and the found efficient super-resolution models achieve better
performance than the state-of-the-art methods with limited number of parameters
and FLOPs.
