Efficient multiquality super-resolution using a deep convolutional neural network for an FPGA implementation

摘要

We propose an efficient deep convolutional neural network for a super-resolution which is capable of multiple-quality input, by analyzing the input quality and choosing appropriate features automatically. To implement the network in an FPGA and an ASIC, we employ a network trimming technique to compress the neural network.