Single Frame Super-Resolution: A New Learning Based Approach and Use of IGMRF Prior

In this paper, we propose a new learning based approach for super-resolving an image captured at low spatial resolution. Given the low spatial resolution test image and a training set consisting of low and high spatial resolution images, all captured using the same camera, we obtain super-resolution for the test image. We propose a new wavelet based learning technique that learns the high frequency details for the test image from the training set and thus obtain an initial high resolution estimate. Since super-resolution is an ill-posed problem we solve it using regularization framework. We model the low resolution image as the aliased and noisy version of the corresponding high resolution image and estimate the aliasing matrix using the test image and the initial high resolution (HR) estimate. The super-resolved image is modeled as an inhomogeneous Gaussian Markov Random Field (IGMRF) and the IGMRF prior model parameters are estimated using the initial HR estimate. Finally, the cost function formed is minimized using simple gradient descent approach. We demonstrate the effectiveness of the proposed approach by conducting experiments on gray scale as well as on color images. The method is compared with another existing learning-based approach which uses training set consisting of HR images only and employs autoregressive (AR) and wavelet priors. The advantage of the our approach when compared to motion-based methods is that there is no need of multiple observations and also registration. The proposed approach can be used in applications such as wildlife sensor network where memory, transmission bandwidth and camera cost are main constraints.