Image denoising with local dense and adaptive global residual networks

Residual Networks (ResNet) and Dense Convolutional Networks (DenseNet) have shown great success in lots of high-level computer vision applications. In this paper, we propose a novel network with Local Dense and Adaptive Global Residual (LD+AGR) frameworks for fast and accurate image denoising. More precisely, we combine local residual/dense with global residual/dense to investigate the best performance dealing with image denoising problem. In particular, local/global residual/dense means the connection way of inner/outer recursive blocks. And residual/dense represents combining layers by summation/concatenation. Furthermore, when combining skip connections, we add some adaptive and trainable scaling parameters, which could adjust automatically during training to balance the importance of different layers. Numerous experiments demonstrate that the proposed network performs favorably against the state-of-the-art methods in terms of quality and speed.