Image Super-Resolution Reconstruction Using Map Estimation
Xinlong Lu, Shengyong Chen, Xin Wang, Sheng Liu, Chunyan Yao, Xianping Huan · 2013
This paper presents a promising super-resolution (SR) approach using maximum a posteriori (MAP) estimation. We consider the high resolution (HR) estimation as a Markov Random Field (MRF), using a transformed gradient field prior to repair the image fuzzy problem caused by MRF. An improved Normalized Convolution method is proposed to obtain a first good estimation. We build a reasonable energy function and minimize the posterior energy by gradient descent algorithm. Experimental results on realistic image sequence and comparisons with several other SR techniques show that our approach gives the best results both qualitative and quantitative.