We first partition the training samples into different subsets, and then learn an incoherent sub-dictionary for every subset. Finally, the input patches are ...
Sep 25, 2012 · solve the problem of limited resolution in electronic imaging devices. Inspired by the success of the sparse representation in image SR ...
We address single image super-resolution using a statistical prediction model based on sparse representations of low- and high-resolution image patches. The ...
Missing: sub- | Show results with:sub-
Jan 31, 2020 · We propose a Deep Analysis dictionary Model (DeepAM) which is optimized to address a specific regression task known as single image super-resolution.
Missing: incoherent | Show results with:incoherent
... Single image super-resolution using incoherent sub- dictionaries learning. IEEE Transactions on Consumer Electronics, 891–897 (2012). 12. Yang, J., Wang, Z ...
In this study, the authors propose a novel approach for single image super-resolution. ... image super-resolution using incoherent sub-dictionaries learning', ...
This paper focuses on recovering super-resolution version given only one single low-resolution LR image, and applies guided filter, the first-order and ...
Apr 18, 2019 · In this paper, we propose a novel method for super-resolution reconstruction of neonate images with the help of toddler images, using residual- ...
People also ask
What are the applications of single image super-resolution?
What is single image super-resolution vs multiple image super-resolution?
In this paper, we mainly focus on recovering super-resolution version given only one single low-resolution (LR) image. The proposed method is combined with the ...
Feb 16, 2022 · In this article, we present a new image super-resolution framework that is capable of striking a balance between distortion and perception.