In this paper, a technique for image enhancement based on proposed edge boosting algorithm to reconstruct high quality image from a single low resolution image is described. The difficulty in single-image super-resolution is that the generic image priors resided in the low resolution input image may not be sufficient to generate the effective solutions. In order to achieve a success in super-resolution reconstruction, efficient prior knowledge should be estimated. The statistics of gradient priors in terms of priority map based on separable gradient estimation, maximum likelihood edge estimation, and local variance are introduced. The proposed edge boosting algorithm takes advantages of these gradient statistics to select the appropriate enhancement weights. The larger weights are applied to the higher frequency details while the low frequency details are smoothed. From the experimental results, the significant performance improvement quantitatively and perceptually is illustrated. It can be seen that the proposed edge boosting algorithm demonstrates high quality results with fewer artifacts, sharper edges, superior texture areas, and finer detail with low noise.
In this paper, an adaptive edge enhancement algorithm is proposed to reconstruct a super resolution image from a
single low resolution one. In order to improve the results of the high resolution reconstruction, edge statistics is learned
from the scenes using a statistical analysis of the maximum likelihood estimation to approximate edge boosting weight
that helps to significantly enhance edge information in the high frequency area. The edge sketch image will be adaptively
combined with the results of wiener filter according to the values of the local variance. The experimental results on
several test images show the success in reconstructing the super resolution both quantitatively and perceptually.