Super-resolution Reconstruction Based on Re-parameterization
In view of the contradiction between the speed and accuracy of the existing single image super-resolution(SISR)model,this paper presents a lightweight re-parameterization model for image realization reconstruction.The model is trained to en-sure accuracy by using a model with a more complex structure,and the model is equivalently transformed into a simple convolution to improve the speed during inference.At the same time,the addition of a multi-supervisory structure makes the model converge faster and more flexible.The quality and efficiency of the reconstruction model are evaluated by the peak signal-to-noise ratio and structural similarity.It is verified that the proposed model has the advantages of light weight and good reconstruction quality in the existing super-resolution reconstruction methods.
single image super-resolutionconvolutional neural networkmulti-supervised learningre-parameterization
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