Joint super-resolution and compression artifact reduction based on dual-learning
Oh‐Young Lee, Jae Wook Lee, Dae-Yeol Lee, Jong‐Ok Kim · 2016
We propose a novel integrated framework to combine the self-learning super-resolution (SR) with dual-learning noise-reduction (NR) for compressed images. Contrary to existing learning based denoising approach, dual-learning based joint SR and NR is proposed by adding a denoised training set. It makes the proposed framework more suitable for highly compressed noise by referring to closer patch in a training set. Also, it is robust for SR artifacts since the joint framework is designed in such a way that one could learn a process to simultaneously perform NR and SR. Experimental results show that the proposed joint SR and NR framework can achieve higher objective and subjective qualities, compared with individual processing of NR and SR.