Super-resolution Reconstruction Using Multiconnection Deep Residual Network Combined an Improved Loss Function for Single-frame Image

Super-resolution reconstruction plays an important role in reconstructing image detail and improving image visual effects, which is achieved mostly by linear interpolation algorithm in the existing display equipment, and there are no obvious details and blurred edges. In this paper, a multi-connection convolution network is proposed to solve the problems of the super-resolution reconstruction (SR) algorithm. The network constructs a multi-connection network structure, where a long skip strategy is used to obtain the identity mapping. It can concatenate the low-level features and high-level features, and can simultaneously represent various complex reconstruction scenes. In addition, a strong and flexible two parameter loss function is used to optimize and train the deep network and improve the generalization ability of the network model. The simulation results show that the proposed SR algorithm in this paper can generate high resolution images with rich details and clearness, and the objective quantitative evaluation is greatly improved.

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