Image Super-Resolution Algorithms Based on Deep Feature Differentiation Network
Traditional deep neural networks usually stack deep features in a way such as skip connection, which is easy to cause information redundancy. To improve the utilization of deep feature information, a Deep Feature Differentiation Network (DFDN) is proposed and applied to single image super-resolution. First, multi-scale deep feature differentiation information is extracted and fused by Mutual-Projected Fusion Block (MPFB) to reduce the contextual information loss. Second, a differential feature attention module is proposed to further learn the differences of deep features while expanding the perception field. Third, the modules are connected in a recursive form to increase the network depth and realize feature reuse. The DIV2K dataset is used as the training dataset, and the pre-trained model is tested with four benchmark datasets, and the results are obtained by comparing the reconstructed images with popular algorithms. Extensive experiments show that the algorithm proposed in this study learns richer texture information than existing algorithms and achieves the best rankings in both subjective visualization and quantitative evaluation metrics, which again proves its robustness and superiority.
Super resolutionDeep featureFeature fusionConvolution neural networkDifferentiation
万方数据
维普
