摘要
Because of the quantization noise introduced during the compression,super-resolution reconstruction(SRR)techniques are complicated for the compressed images.This paper aims to incorporate the prior knowledge of discrete cosine transform(DCT)coefficients into modeling the quantization noise.The spatial covariance matrix of the quantization noise is estimated by utilizing the Laplacian distribution of the alternating current(AC)coefficients.After estimating the spatial joint covariance of overall noises for the imaging system,we propose a general Bayesian framework to enhance the resolution for compressed images.Experiments demonstrate the effectiveness of the proposed algorithm and show the superiority to previous methods in objective and subjective aspects.
Because of the quantization noise introduced during the compression, super-resolution reconstruction （SRR） techniques are complicated for the compressed images. This paper aims to incorporate the prior knowledge of discrete cosine transform （DCT） coefficients into modeling the quantization noise. The spatial covariance matrix of the quantization noise is estimated by utilizing the Laplacian distribution of the alternating current （AC） coefficients. After estimating the spatial joint covariance of overall noises for the imaging system, we propose a general Bayesian framework to enhance the resolution for compressed images. Experiments demonstrate the effectiveness of the proposed algorithm and show the superiority to previous methods in objective and subjective aspects.
基金
The Advanced Research of Shanghai Technical Committee(No.03DZ05020)
