The process of image quality improvement through super-resolution methods is still a gray area in the field of biometric identification. This paper proposes a scheme for fingerprint super-resolution using ridge orientation-based clustered coupled sparse dictionaries. The training image patches are clustered into groups based on dominant orientation and corresponding coupled subdictionaries are learned for each low- and high-resolution patch groups. While reconstructing the image, the minimum residue error criterion is used for choosing a subdictionary for a particular patch. In the final step, back projection is applied to eliminate the discrepancy in the estimate due to noise or inaccuracy in sparse representation. The performance evaluation of the proposed method is accomplished in terms of peak signal-to-noise ratio and structural similarity index. A filter bank-based fingerprint matcher is used for evaluating the performance of the proposed method in terms of matching accuracy. Our experimental results show that the new method achieves better results in comparison with other methods and will establish itself for improving performances of fingerprint-identification systems.