4 research outputs found
Progressive Multi-Scale Residual Network for Single Image Super-Resolution
Multi-scale convolutional neural networks (CNNs) achieve significant success
in single image super-resolution (SISR), which considers the comprehensive
information from different receptive fields. However, recent multi-scale
networks usually aim to build the hierarchical exploration with different sizes
of filters, which lead to high computation complexity costs, and seldom focus
on the inherent correlations among different scales. This paper converts the
multi-scale exploration into a sequential manner, and proposes a progressive
multi-scale residual network (PMRN) for SISR problem. Specifically, we devise a
progressive multi-scale residual block (PMRB) to substitute the larger filters
with small filter combinations, and gradually explore the hierarchical
information. Furthermore, channel- and pixel-wise attention mechanism (CPA) is
designed for finding the inherent correlations among image features with
weighting and bias factors, which concentrates more on high-frequency
information. Experimental results show that the proposed PMRN recovers
structural textures more effectively with superior PSNR/SSIM results than other
small networks. The extension model PMRN with self-ensemble achieves
competitive or better results than large networks with much fewer parameters
and lower computation complexity.Comment: This work has been submitted to the IEEE for possible publication.
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