Image super-resolution (SR) is widely used in the fields of civil and military, especially for the low-resolution remote
sensing images limited by the sensor. Single-image SR refers to the task of restoring a high-resolution (HR) image from
the low-resolution image coupled with some prior knowledge as a regularization term. One classic method regularizes
image by total variation (TV) and/or wavelet or some other transform which introduce some artifacts. To compress these
shortages, a new framework for single image SR is proposed by utilizing an adaptive filter before regularization. The key
of our model is that the adaptive filter is used to remove the spatial relevance among pixels first and then only the high
frequency (HF) part, which is sparser in TV and transform domain, is considered as the regularization term. Concretely,
through transforming the original model, the SR question can be solved by two alternate iteration sub-problems. Before
each iteration, the adaptive filter should be updated to estimate the initial HF. A high quality HF part and HR image can
be obtained by solving the first and second sub-problem, respectively. In experimental part, a set of remote sensing
images captured by Landsat satellites are tested to demonstrate the effectiveness of the proposed framework.
Experimental results show the outstanding performance of the proposed method in quantitative evaluation and visual
fidelity compared with the state-of-the-art methods.