SIFT-based image super-resolution

This paper presents a new exemplar-based image super-resolution (SR) method in which we propose making use of scale invariant image features for high frequency (HF) approximation. We introduce the scale invariant feature transform (SIFT) descriptors in both building an exemplar dataset adaptively and producing the HF details with respect to the features of an input low resolution image. Given a large image database, we propose using the highly correlated images retrieved by SIFT descriptors for exemplar training rather than using a general set of images to increase the matching accuracy. Through building the training set of high resolution/low resolution exemplar pairs, the HF details for SR are retrieved from the training set by matching the SIFT features in a dense way. The flexibility as well as effectiveness of our SR approach is demonstrated at different magnification factors, e.g. 3 and 4. Experimental results show that our SIFT-based SR approach achieves enhanced high resolution images in terms of both objective and subjective qualities in comparison with the state-of-the-art exemplar-based methods.