Abstract
The primary goal of the multiframe super-resolution image reconstruction is to produce an image with a higher resolution by integrating information extracted from a set of corresponding images with low resolution, which is used in various fields. However, super-resolution image reconstruction approaches are typically affected by annoying restorative artifacts, including blurring, noise, and staircasing effect. Accordingly, it is always difficult to balance between smoothness and edge preservation. In this paper, we intend to enhance the efficiency of multiframe super-resolution image reconstruction in order to optimize both analysis and human interpretation processes by improving the pictorial information and enhancing the automatic machine perception. As a result, we propose new approaches that firstly rely on estimating the initial high-resolution image through preprocessing of the reference low-resolution image based on median, mean, Lucy-Richardson, and Wiener filters. This preprocessing stage is used to overcome the degradation present in the reference low-resolution image, which is a suitable kernel for producing the initial high-resolution image to be used in the reconstruction phase of the final image. Then, L2 norm is employed for the data-fidelity term to minimize the residual among the predicted high-resolution image and the observed low-resolution images. Finally, bilateral total variation prior model is utilized to restrict the minimization function to a stable state of the generated HR image. The experimental results of the synthetic data indicate that the proposed approaches have enhanced efficiency visually and quantitatively compared to other existing approaches.
Highlights
Over the last two decades, the world has experienced an enormous advancement in software and hardware technologies
We propose the new multiframe image SR approaches that firstly rely on estimating the initial HR image through preprocessing of the reference LR image with different filters. is preprocessing stage is used to overcome the degradation present in the reference LR image, which is a suitable kernel for producing the initial HR image to be used in the reconstruction phase of the final image. en, the L2 norm is employed for the data-fidelity term to minimize the residual among the predicted HR image and the observed LR images
Regardless of whether the first or the second proposed method is selected to generate the composed LR image, the initial HR image is estimated through the bicubic interpolation method form the composed LR image. en, the L2 norm is employed for the data-fidelity term to minimize the residual among the predicted HR image and the observed LR images
Summary
Over the last two decades, the world has experienced an enormous advancement in software and hardware technologies. Industrial sectors have made the best use of modern technology to generate electronic devices such as computer systems, cellular mobile phones, personal digital assistant (PDA), and innumerable devices at inexpensive costs [1]. The manufacturing methods of camera sensors have been highly developed to generate high-quality digital cameras. Additional information is provided from high-resolution (HR) images to have a greater visual perspective. HR images are used widely, including but not limited to video surveillance [2], medical imaging [3], forensic imaging [4], and remote sensing [5]. Ey are still in urgent need for HR image which frequently exceeds the abilities of the HR digital cameras [6, 7]. The existing imagery system produces lowresolution (LR) images which must be improved in order to obtain HR images
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