Spatially adaptive regularized iterative high resolution image reconstruction algorithm High resolution images are often required in applications such as remote sensing, frame freeze in video, military and medical imaging. Digital image sensor arrays, which are used for image acquisition in many imaging systems, are not dense enough to prevent aliasing, so the acquired images will be degraded by aliasing effects. To prevent aliasing without loss of resolution, a dense detector array is required. But it may be very costly or unavailable, thus, many imaging systems are designed to allow some level of aliasing during image acquisition. The purpose of our work is to reconstruct an unaliased high resolution image from the acquired aliased image sequence. In this paper, we propose a spatially adaptive regularized iterative high resolution image reconstruction algorithm for blurred, noisy and downsampled image sequences. The proposed approach is based on a Constrained Least Squares (CLS) high resolution reconstruction algorithm, with spatially adaptive regularization operators and parameters. These regularization terms are shown to improve the reconstructed image quality by forcing smoothness, while preserving edges in the reconstructed high resolution image. Accurate subpixel motion registration is the key of the success of the high resolution image reconstruction algorithm. However, subpixel motion registration may have some level of registration error. Therefore, a reconstruction algorithm which is robust against the registration error is required. The registration algorithm uses a gradient based subpixel motion estimator which provides shift information for each of the recorded frames. The proposed algorithm is based on a technique of high resolution image reconstruction, and it solves spatially adaptive regularized constrained least squre minimization functionals. In this paper, We show that the reconstruction algorithm gives dramatic improvements in the resolution of the reconstructed image and is effective in handling the aliased information. The proposed algorithm is also shown to be robust in the presence of severe registration error. Experimental results are provided to illustrate the performance of the proposed reconstruction algorithm. Comparative analyses with other reconstruction methods are also provided.

Adaptive regularized image interpolation using data fusion and steerable constraints This paper presents an adaptive regularized image interpolation algorithm from blurred and noisy low resolution image sequence, which is developed in a general framework based on data fusion. This framework can preserve the high frequency components along the edge orientation in a restored high resolution image frame. This multiframe image interpolation algorithm is composed of two levels of fusion algorithm. One is to obtain enhanced low resolution images as an input data of the adaptive regularized image interpolation based on data fusion. The other one is to construct the adaptive fusion algorithm based on regularized image interpolation using steerable orientation analysis. In order to apply the regularization approach to the interpolation procedure, we first present an observation model of low resolution video formation system. Based on the observation model, we can have an interpolated image which minimizes both residual between the high resolution and the interpolated images with a prior constraints. In addition, by combining spatially adaptive constraints, directional high frequency components are preserved with efficiently suppressed noise. In the experimental results, interpolated images using the conventional algorithms are shown to compare the conventional algorithms with the proposed adaptive fusion based algorithm. Experimental results show that the proposed algorithm has the advantage of preserving directional high frequency components and suppressing undesirable artifacts such as noise.