Deep Convolution Modulation for Image Super-Resolution

Recently, deep-learning-based super-resolution methods have achieved excellent performances, but mainly focus on training a single generalized deep network by feeding numerous samples. Yet intuitively, each image has its specific representation, and is expected to acquire an adaptive model. For this issue, we propose a novel convolution modulation (CoMo) mechanism to build image-specific deep networks, by exploiting the principal information of the feature to generate a modulation weight, and thereby adaptively modulating the kernel weights of convolution without any additional parameters, which outperforms the vanilla convolution and several existing attention mechanisms when embedding into the state-of-the-art architectures. To optimize the modulated convolutions in mini-batch training, we introduce an image-specific optimization (IsO) algorithm, which tackles the infeasibility of the conventional optimization algorithms on this issue. Furthermore, we investigate the effect of CoMo on state-of-the-art architectures and design a new CoMoNet architecture by employing the U-style residual learning and hourglass dense block learning, which is an appropriate architecture to utmost improve the effectiveness of CoMo theoretically. Extensive experiments on benchmarks show that the proposed methods achieve superior performances and higher flexibility against the state-of-the-art SISR and blind SR methods. The code is available at github.com/YuanfeiHuang/CoMoNet.