Blind image super-resolution reconstruction based on dual regression network

Existing deep learning-based Super Resolution (SR) reconstruction algorithms achieve remarkable performance on images with known degradation. Most of the degradation models exists problems in self-adaptations when facing with the deviation of the degradation model of the image of the real scene, and the effect is not good. Therefore, this paper proposes a blind image super-resolution reconstruction algorithm based on dual regression, which aims to solve the problem of poor performance of super-resolution networks in real scenes. Firstly, the closed-loop network is used to constrain the mapping space, and the optimal reconstruction function is found to improve the network reconstruction performance. Secondly, the attention mechanism is adopted into the residual block of feature extraction to expand the receptive field of the feature map, improve the reuse of features, and strengthen the reconstruction of high-frequency information. Finally, the frequency-domain blur kernel map estimates the down sampling kernel and reconstructs the low-resolution image, adaptively extracts the feature expression, enhances the ability to restore texture details, and reconstructs the real-world image better.