A traditional optical imaging device uses a single high quality lens designed to project the field of view onto a sensor array for direct image creation. A camera with a flatter form factor using an array of smaller lens elements would create an array of smaller lower resolution images. Reconstruction of high resolution images from multiple low resolution images at various displacements is a we...

In this paper, an iterative super-resolution reconstruction method is introduced for license plate recognition. A high-resolution image of the license plate is reconstructed by fusing the information derived from a set of subpixel shifted low-resolution images. The reconstruction problem is formulated as a system of linear equations that is solved by using the simultaneous algebraic reconstruct...

In this paper we study and represent various techniques of image super resolution (SR), it is also called as super resolution reconstruction. Super resolution reconstruction produces high resolution image from sequence of low resolution images. The main aim of super resolution is to improve visual quality of available low resolution image. Also existing Low Resolution (LR) imaging can be utiliz...

Simulating fluid flow at micro level is an ongoing problem. Simplified macroscopic flow models like Darcy’s law is unable to estimate fluid dynamic properties of porous media. The digital sample reconstruction by high resolution X-ray computed tomography scanning and fluid-dynamics simulation, together with the increasing power of super-computers, allow to carry out pore-scale simulations throu...

Magnetic resonance (MR) images of the tongue have been used in both clinical medicine and scientific research to reveal tongue structure and motion. In order to see different features of the tongue and its relation to the vocal tract it is beneficial to acquire three orthogonal image stacks-e.g., axial, sagittal and coronal volumes. In order to maintain both low noise and high visual detail, ea...

Nearly all super-resolution algorithms are based on the fundamental constraints that the super-resolution image should generate the low resolution input images when appropriately warped and down-sampled to model the image formation process. (These reconstruction constraints are normally combined with some form of smoothness prior to regularize their solution.) In the first part of this paper, w...

Multispectral image reconstruction allows to combine a multispectral low resolution image with a panchromatic high resolution one to obtain a new multispectral image with the spectral properties of the lower resolution image and the level of detail of the higher resolution one. In this paper we formulate the problem of multispectral image reconstruction based on super-resolution techniques and ...

MR images typically have poorer resolution in the slice-selection direction than the in-plane directions. In this work, we adopt an MAP super-resolution method to reconstruct a high-resolution image from two orthogonal scans of the same subject. The new image shows improved SNR and has a resolution that approximates the original in-plane resolution in all directions. Experimental results on bot...

Super-resolution reconstruction in medical imaging has become more demanding due to the necessity of obtaining high-quality images with minimal radiation dose, such as low-field magnetic resonance (MRI). However, image super-resolution remains a difficult task because complexity and high textual requirements for diagnosis purpose. In this paper, we offer deep learning based strategy reconstruct...