A robust iterative super-resolution mosaicking algorithm using an adaptive and directional Huber-Markov regularization

• We develop a super-resolution algorithm using directional Huber-Markov regularization.
• We compare our algorithm with two other state-of-the-art algorithms.
• We perform quantitative evaluation using six performance metrics.

A robust spatial-domain based super-resolution mosaicking algorithm is proposed. This technique incorporates a mosaicking algorithm, and a super-resolution reconstruction algorithm. The main contribution of this paper is the development of a super-resolution algorithm using a Huber Norm-based maximum likelihood (ML) estimation in combination with an adaptive directional Huber-Markov regularization. Another contribution is the development of a no-reference performance metric based on reciprocal singular value curve for quantitative evaluation of the proposed algorithm. Along with the above-mentioned metric, five other performance measurement metrics are used to assess the efficiency of the algorithm. The performance of this algorithm is compared with the performances of two different algorithms: the Tikhonov regularization-based and the total variation (TV)-based super-resolution mosaicking algorithms. Results show that the proposed algorithm outperforms the other two techniques in terms of lowest amount of blur and noise in the output.