A no-reference perceptual blurriness metric based fast super-resolution of still pictures using sparse representation
Jae-Seok Choi, Sung‐Ho Bae, Munchurl Kim · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2015
In recent years, perceptually-driven super-resolution (SR) methods have been proposed to lower computational complexity. Furthermore, sparse representation based super-resolution is known to produce competitive high-resolution images with lower computational costs compared to other SR methods. Nevertheless, super-resolution is still difficult to be implemented with substantially low processing power for real-time applications. In order to speed up the processing time of SR, much effort has been made with efficient methods, which selectively incorporate elaborate computation algorithms for perceptually sensitive image regions based on a metric, such as just noticeable distortion (JND). Inspired by the previous works, we first propose a novel fast super-resolution method with sparse representation, which incorporates a no-reference just noticeable blur (JNB) metric. That is, the proposed fast super-resolution method efficiently generates super-resolution images by selectively applying a sparse representation method for perceptually sensitive image areas which are detected based on the JNB metric. Experimental results show that our JNB-based fast super-resolution method is about 4 times faster than a non-perceptual sparse representation based SR method for 256× 256 test LR images. Compared to a JND-based SR method, the proposed fast JNB-based SR method is about 3 times faster, with approximately 0.1 dB higher PSNR and a slightly higher SSIM value in average. This indicates that our proposed perceptual JNB-based SR method generates high-quality SR images with much lower computational costs, opening a new possibility for real-time hardware implementations.