Directional Reconstruction of Super Resolution Image by Magnifying Curvelet Basis

A super-resolution image reconstruction algorithm was proposed using the 2nd generation curvelet to reduce the edge blur caused by traditional algorithms. In the proposed algorithm, the original image is decomposed into j scales using curvelet. The curvelet coefficients in the j scales of the zoomed-in image are obtained by utilizing the proportionality of curvelet bases between adjacent scales, and the curvelet coefficients in the (j+1)th scale are determined by utilizing the spatial template of curvelet coefficients with the largest scale number. All the curvelet coefficients are processed with a nonlinear function to enhance image quality.The zoomed-in image with fine edges is finally created through curvelet reconstruction because of the good directional characteristic of curvelet. Experiments on two benchmarking images shown that, the proposed algorithm could preserve more image features and edge sharpness, and the peak signal to noise ratios (PSNRs) for the two images increased by 2.2 and 0.6 dB, respectively, compared with those obtained with a traditional interpolation algorithm.