In this paper, we propose an image super-resolution (resolution enhancement) algorithm that takes into account inaccurate estimates of the registration parameters and the point spread function. These inaccurate estimates, along with the additive Gaussian noise in the low-resolution (LR) image sequence, result in different noise level for each frame. In the proposed algorithm, the LR frames are adaptively weighted according to their reliability and the regularization parameter is simultaneously estimated. A translational motion model is assumed. The convergence property of the proposed algorithm is analyzed in detail. Our experimental results using both real and synthetic data show the effectiveness of the proposed algorithm.

In traditional digital image restoration, the blurring process of the optic is assumed known. Many previous research efforts have been trying to reconstruct the degraded image or video sequence with either partially known or totally unknown point spread function (PSF) of the optical lens, which is commonly called the blind deconvolution problem. Many methods have been proposed in the application to image restoration. However, little work has been done in the super-resolution scenario. In this paper, we propose a generalized framework of regularized image/video iterative blind deconvolution / super-resolution (IBD-SR) algorithm, using some information from the more matured blind deconvolution techniques from image restoration. The initial estimates for the image restoration help the iterative image/video super-resolution algorithm converge faster and be stable. Experimental results are presented and conclusions are drawn.

In many imaging systems, the resolution of the detector array of the camera is not sufficiently high for a particular application. Furthermore, the capturing process introduces additive noise and the point spread function of the lens and the effects of the finite size of the photo-detectors further degrade the acquired video frames. The goal of resolution enhancement is to estimate a high-resolution image from a sequence of low-resolution images while also compensating for the degradations. We propose a technique for image resolution enhancement with adaptively weighted low-resolution images (channels) and simultaneous estimation of the regularization parameter. The weight coefficients work as the cross-channel fidelity to each low-resolution image, while the regularization parameter acts as the within-channel balance between data and prior model for each channel. Experimental results are presented and conclusions are drawn.

In this paper, we propose two approaches for video sequence resolution enhancement using maximum a posteriori (MAP) estimation. Huber-Markov random fields (HMRF) are used as prior models. These models can better preserve image discontinuities (edges) when compared with Gaussian prior models. The two proposed approaches differ in the selection of image smoothness measure. The first approach employs a measure that is based on a discrete Laplacian kernel, while the second approach uses a finite difference approximation of second order derivatives at each pixel of the high-resolution image estimate. Experimental results are presented and conclusions are drawn.