Degraded low-resolution (LR) images are often obtained from cameras. Resolution enhancement and image restoration are very practical in many fields such as medical imaging, surveillance system and remote sensing. Single image super-resolution is a technique which reconstruct a restored high-resolution (HR) image from a degraded LR image. In this paper, we propose single image super-resolution based on sparse coding using self-similarity prior. A sparsity constraint is used to jointly train coupled dictionaries which can generate high frequency details. Reconstructed HR output is enhanced with non-local means based on self-similarity prior. Experimental results demonstrate that our method shows higher performance than other existing algorithms.
|Title of host publication||2019 IEEE International Conference on Consumer Electronics, ICCE 2019|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Publication status||Published - 2019 Mar 6|
|Event||2019 IEEE International Conference on Consumer Electronics, ICCE 2019 - Las Vegas, United States|
Duration: 2019 Jan 11 → 2019 Jan 13
|Name||2019 IEEE International Conference on Consumer Electronics, ICCE 2019|
|Conference||2019 IEEE International Conference on Consumer Electronics, ICCE 2019|
|Period||19/1/11 → 19/1/13|
Bibliographical noteFunding Information:
This material is based upon work supported by the Ministry of Trade, Industry & Energy(MOTIE, Korea) under Industrial Technology Innovation Program(10080619).
© 2019 IEEE.
All Science Journal Classification (ASJC) codes
- Industrial and Manufacturing Engineering
- Media Technology
- Electrical and Electronic Engineering