Super-resolution (SR) methods based on total variation (TV) prior model is a very popular
method because of its ability of edge preservation. However, as TV prior model favors a …

Image super-resolution (SR) plays an important role in many areas as it promises to
generate high-resolution (HR) images without upgrading image sensors. Many existing SR …

A new algorithm for reconstructing the fingerprint super‐resolution (SR) image is presented.
The basic idea of the algorithm is to reconstruct the SR image by using sparse …

Super-resolution (SR) methods are effective for generating a high-resolution image from a
single low-resolution image. However, four problems are observed in existing SR …

A high-resolution image is obtained by fusing the information derived from blurred, sub-pixel
shifted, and noisy low-resolution observations. In this paper, a novel regularization model …

Recent research has demonstrated that the performance of sparse representation based
methods for single image super-resolution (SISR) reconstruction relies strongly on the …

Video super-resolution (SR) is a process for reconstructing high-resolution (HR) images by
utilizing complementary information among multiple low-resolution (LR) images. Accurate …

High-resolution image reconstruction obtains one high-resolution image from multiple
lowresolution, shifted, degraded samples of a true scene. This is a typical ill-posed problem …

In this letter, we propose a new graph cuts multi-frame super resolution method. The method
is carried out in 3 steps. First, we project each high-resolution pixel p onto the low-resolution …

Multi-frame super-resolution methods can generate high resolution images by combining
multiple captures of the same scene; however, the performance of merged results are …