A Fast Super-resolution Image Reconstruction Method Based on Learning

摘要

Aiming at the long running time problem of the super-resolution image reconstruction method based on learning, this paper proposes a novel fast method. Principal Component Analysis (PCA) is used to reduce the data dimensionality of training data set and Vector Quantization (VQ) is introduced into super-resolution image reconstruction to divide subset. Both accelerate running of the method speed and solve the long running time problem caused by large amount of training data. In order to ensure the quality of the output image, Stationary Wavelet Transform (SWT) is used to extract the low and high frequency information of the sample image. And Markov Network is improved to find the best candidate block. Experiments show that without sacrificing the quality of final output high resolution image, the execution speed of the proposed method is greatly improved.