2 research outputs found
Iterative Network for Image Super-Resolution
Single image super-resolution (SISR), as a traditional ill-conditioned
inverse problem, has been greatly revitalized by the recent development of
convolutional neural networks (CNN). These CNN-based methods generally map a
low-resolution image to its corresponding high-resolution version with
sophisticated network structures and loss functions, showing impressive
performances. This paper proposes a substantially different approach relying on
the iterative optimization on HR space with an iterative super-resolution
network (ISRN). We first analyze the observation model of image SR problem,
inspiring a feasible solution by mimicking and fusing each iteration in a more
general and efficient manner. Considering the drawbacks of batch normalization,
we propose a feature normalization (FNorm) method to regulate the features in
network. Furthermore, a novel block with F-Norm is developed to improve the
network representation, termed as FNB. Residual-in-residual structure is
proposed to form a very deep network, which groups FNBs with a long skip
connection for better information delivery and stabling the training phase.
Extensive experimental results on testing benchmarks with bicubic (BI)
degradation show our ISRN can not only recover more structural information, but
also achieve competitive or better PSNR/SSIM results with much fewer parameters
compared to other works. Besides BI, we simulate the real-world degradation
with blur-downscale (BD) and downscalenoise (DN). ISRN and its extension ISRN+
both achieve better performance than others with BD and DN degradation models.Comment: 12 pages, 14 figure