Adaptive regularization-based super resolution reconstruction technique for multi-focus low-resolution images

Super Resolution (SR) is the process of enhancing the visual quality of a sequence of observed low-resolution images by constructing a single high-resolution image. This paper is interested in the super resolution of multi-focus low-resolution images. It is assumed that, the images are acquired by low precision optics with limited depth of focus and from different viewpoints. Hence, the acquired images will be unregistered and multi-focus low-resolution images. The proposed SR reconstruction technique depends on using regularization-based schemes which have been demonstrated to be effective because SR reconstruction is actually an ill-posed problem. The proposed technique is based on using a local adaptive regularization parameter. This parameter can deal with the partial smoothness, which is located in images due to the multi-focus phenomenon. Moreover, the selection of the optimal value of this parameter is proposed using the particle swarm optimization method. The experimental results proved that the proposed SR reconstruction technique achieves better results than the more recent SR reconstruction technique named by locally adaptive bilateral total variation method which is also interested in treating the partial smoothness in low-resolution images by means of using a local adaptive regularization parameter. A reconstruction technique, depends on using an adaptive regularization parameter (λA), is proposed to handle the problem of multi-focus images.The smooth area needs certain value of λA that differs from the value that is used in the sharp area.The proposed reconstruction technique uses the optimal value for the selected regularization parameter λA by using the PSO.