Combining self-learning based super-resolution with denoising for noisy images

In this paper, we propose a new learning based joint Super-Resolution (SR) and denoising algorithm for noisy images. The individual processing of denoising and SR when super-resolving a noisy image has drawbacks such as noise amplification, blurring and SR performance reduction. In the proposed joint method, principal component analysis (PCA) based denoising is closely combined with a self-learning SR framework in order to minimize the SR visual quality degradation caused by noise. Experimental results show that the joint method achieves an SR image quality improvement in terms of noise and blurring, when compared with the state-of-the-art joint method and sequential combinations of individual denoising and SR.