Image super-resolution (SR) is a significant technique in image processing as it enhances the spatial resolution of images, enabling various downstream applications. Based on recent achievements SR studies computer vision, deep-learning-based methods have been widely investigated for remote sensing images. In this study, we proposed two-stage approach called bicubic-downsampled low-resolution (...

Accurate registration of multiple low resolution images is of central importance in many advanced image processing applications, since capturing of multiple low-resolution images taken of the same scene results in a distortion between each image. Image super-resolution is a typical application where the quality of the super-resolved image is degraded as registration errors increase. In this pap...

Image restoration is the method of restoring the corrupted images from the degradation. The degradation can happen due to some external and internal noise like blur, relative motion between the camera and the object whose image is to be captured and many more. Super resolution helps in increasing the resolution of the images. Therefore, image restoration used along with the super resolution not...

Most of the existing deep learning-based image super-resolution methods require a large number datasets or ground truth. However, these are not suitable for restoration real with different domains. Recently, Deep Image Prior (DIP) based on single-image explores prior and uses network structure as implicit to recover images, but it ignores explicit information actual itself. The addition can eff...

Super-resolution image zooming is possible when the image has some geometric regularity. Directional interpolation algorithms are used in industry, with ad-hoc regularity measurements. Sparse signal decompositions in dictionaries of curvelets or bandlets find indirectly the directions of regularity by optimizing the sparsity. However, super-resolution interpolations in such dictionaries do not ...

Recently single image super resolution is very important research area to generate high-resolution image from given low-resolution image. Algorithms of single image resolution are mainly based on wavelet domain and spatial domain. Filter’s support to model the regularity of natural images is exploited in wavelet domain while edges of images get sharp during up sampling in spatial domain. Here s...