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Super-resolving Compressed Images via Parallel and Series Integration of Artifact Reduction and Resolution Enhancement
In this paper, we propose a novel compressed image super resolution (CISR)
framework based on parallel and series integration of artifact removal and
resolution enhancement. Based on maximum a posterior inference for estimating a
clean low-resolution (LR) input image and a clean high resolution (HR) output
image from down-sampled and compressed observations, we have designed a CISR
architecture consisting of two deep neural network modules: the artifact
reduction module (ARM) and resolution enhancement module (REM). ARM and REM
work in parallel with both taking the compressed LR image as their inputs,
while they also work in series with REM taking the output of ARM as one of its
inputs and ARM taking the output of REM as its other input. A unique property
of our CSIR system is that a single trained model is able to super-resolve LR
images compressed by different methods to various qualities. This is achieved
by exploiting deep neural net-works capacity for handling image degradations,
and the parallel and series connections between ARM and REM to reduce the
dependency on specific degradations. ARM and REM are trained simultaneously by
the deep unfolding technique. Experiments are conducted on a mixture of JPEG
and WebP compressed images without a priori knowledge of the compression type
and com-pression factor. Visual and quantitative comparisons demonstrate the
superiority of our method over state-of-the-art super resolu-tion methods.Code
link: https://github.com/luohongming/CISR_PS
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