Abstract—This paper present a new method based on
co-sparse with learning paired dictionary. The new framework
is consisted of three parts. Firstly a paired dictionary have been
learned which is used to overcome a low resolution image by
utilizing an externally applied high resolution (HR) dictionary
and then learn based on the internal dictionary. Process the
paired dictionary which consists of low resolution (LR) and
high resolution (HR) dictionary by kernel regression based on
their coefficient respectively, and applied directly to construct
the HR patches. Secondly, co-sparse regularization and features
of self similarity have been introduced to strengthen and
enhanced the image structure. In addition, propagation filtering
is applied to suppress the artefacts generated from neighboring
pixel of an image while reserving the image edges. Finally, the
HR image is generated by reconstructing all superior HR
patches. The effectiveness of the co-sparse demonstrated in real
test images. The proposed method achieved good quality high
resolution images that are superior compared with different SR
methods in terms of peak signal to noise ratio (PSNR), and
structural similarity (SSIM).
Index Terms—Dual dictionary, image resolution,
propagation filter, self-similarity.
Muhammad Sameer Sheikh and Qunsheng Cao are with the College of
Electronics and Information Engineering, Nanjing University of Aeronautics
and Astronautics, Nanjing, China (e-mail: sameer.5@hotmail.com).
Caiyun Wang is with the College of Astronautics, Nanjing University of
Aeronautics and Astronautics, Nanjing, China.
[PDF]
Cite: Muhammad Sameer Sheikh, Qunsheng Cao, and Caiyun Wang, "Image Super-Resolution Reconstruction Using Adaptive
Co-sparse Regularization with Dual Dictionary," International Journal of Modeling and Optimization vol. 6, no. 4, pp. 225-232, 2016.
