Iterative decoupling deconvolution network for image restoration

Abstract

The iterative decoupled deblurring BM3D (IDDBM3D) (Danielyan et al., 2011) combines the analysis representation and the synthesis representation, where deblurring and denoising operations are decoupled, so that both problems can be easily solved. However, the IDDBM3D has some limitations. First, the analysis transformation and the synthesis transformation are analytical, thus have limited representation ability. Second, it is difficult to effectively remove image noise from threshold transformation. Third, there exists hyper-parameters to be tuned manually, which is difficult and time consuming. In this work, we propose an iterative decoupling deconvolution network(IDDNet), by unrolling the iterative decoupling algorithm of the IDDBM3D. In the proposed IDDNet, the analysis/synthesis transformation are implemented by encoder/decoder modules; the denoising is implemented by convolutional neural network based denoiser; the hyper-parameters are estimated by hyper-parameter module. We apply our models for image deblurring and super-resolution. Experimental results show that the IDDNet significantly outperforms the state-of-the-art unfolding networks.