Sep 6, 2016 · The basic idea behind SR is to combine the non-redundant information contained in single low-resolution frame to generate a high-resolution ...

Oct 22, 2024 · The super-resolution using kernel recursive least square significantly provides an improvement up on the support vector regression solution, ...

The super-resolution using kernel recursive least square significantly provides an improvement up on the support vector regression solution, both in terms of ...

The super-resolution using kernel recursive least square significantly provides an improvement up on the support vector regression solution, both in terms of ...

Aug 2, 2019 · The online kernel adaptive filters are non-linear filters which provide impulse response and are more efficient compared to other kernel ...

This paper describes a single-image super-resolution (SR) algorithm based on non- negative neighbor embedding. It belongs to the family of single-image ...

This paper proposes a regression-based method for single-image super-resolution. Kernel ridge regression (KRR) is used to estimate the high-frequency details.

The online kernel adaptive filters are non-linear filters which provide impulse response and are more efficient compared to other kernel algorithms.

Single-Image Super Resolution (SISR) refers to the process of reconstructing a high-resolution image from a low-resolution observation using various techniques, ...

This paper super resolves a low resolution image to high resolution image, with the model generated from the training set using sparse online greedy support ...