Regularized interpolation using Kronecker product for still images

In this paper, we present a new and efficient algorithm for image interpolation. To render high-resolution image from low-resolution image, classical interpolation techniques estimate the missing pixels from the surrounding pixels based on pixel-by-pixel basis. In contrast, this paper proposes an algorithm which is centered on Tikhonov regularization. The regularized solution is derived using the framework of damped least square optimization. Kronecker product and singular value decomposition are employed to reduce the computational cost of the algorithm. Experimental results show that the method produces better interpolation results when compared to other conventional techniques.