Purpose: To detect small diagnostic signals such as lung nodules in chest radiographs, radiologists magnify a region-of-interest using linear interpolation methods. However, such methods tend to generate over-smoothed images with artifacts that can make interpretation difficult. The purpose of this study was to investigate the effectiveness of super-resolution methods for improving the image qu...

Super Resolution (SR) image can be obtained from a set of Low Resolution (LR) images with noise and blur. The main object of Super Resolution is to get high resolution, high quality image from Low Resolution images. To remove the blur and noises caused by the imaging system as well as recover information, restoration techniques are used. Super resolution imaging processes one or more low resolu...

AbstractDespite achieving remarkable progress in recent years, single-image super-resolution methods are developed with several limitations. Specifically, they trained on fixed content domains certain degradations (whether synthetic or real). The priors learn prone to overfitting the training configuration. Therefore, generalization novel such as drone top view data, and across altitudes, is cu...

Single image super-resolution (SISR), as a traditional ill-conditioned inverse problem, has been greatly revitalized by the recent development of convolutional neural networks (CNN). These CNN-based methods generally map low-resolution to its corresponding high-resolution version with sophisticated network structures and loss functions, showing impressive performances. This paper provides new i...

Image super-resolution aims to recover a visually pleasing high resolution image from one or multiple low resolution images. It plays an essential role in a variety of real-world applications. In this paper, we propose a novel hybrid example-based single image super-resolution approach which integrates learning from both external and internal exemplars. Given an input image, a proxy image with ...

We present a novel method of Bayesian image super-resolution in which marginalization is carried out over latent parameters such as geometric and photometric registration and the image pointspread function. Related Bayesian super-resolution approaches marginalize over the high-resolution image, necessitating the use of an unfavourable image prior, whereas our method allows for more realistic im...

There are some scenarios where the images taken of low resolution and it is hard to judge features from them, resulting in need for enhancement. Super-resolution a technique produce high-resolution image lower-resolution image. The intention here develop system that enhances faces satellite by integrating these models providing an interface access this model. have been various ways achieving su...

We present a novel approach for online shrinkage functions learning in single image super-resolution. The proposed approach leverages the classical Wavelet Shrinkage denoising technique where a set of scalar shrinkage functions is applied to the wavelet coefficients of a noisy image. In the proposed approach, a unique set of learned shrinkage functions is applied to the overcomplete representat...