Superresolution From A Single Noisy Image By The Median Filter Transform

The task of single-image superresolution is to obtain a high resolution image from a low resolution input. Many strategies have achieved considerable success by learning from image databases so as to find suitable replacements for the missing information. However, little effort has been devoted to coping with significant amounts of noise in the input. Noise makes the problem even harder, since its distribution is unknown in many practical cases. Here an approach is proposed to solve this problem irrespective of the noise type, which supports both integer and fractional zoom factors. It is based on the application of the median filter on parallelogram shaped windows chosen according to a suitable probability distribution. The resulting outputs are then median filtered again to obtain the final output. Experimental results are reported for artificial and natural images under Gaussian and impulsive uniform noise. It is found that the proposed approach outperforms several state-of-the-art single-image superresolution methods both quantitatively and qualitatively.