  Most existing deep learning-based image restoration methods usually aim to
remove degradation with uniform spatial distribution and constant intensity,
making insufficient use of degradation prior knowledge. Here we bootstrap the
deep neural networks to suppress complex image degradation whose intensity is
spatially variable, through utilizing prior knowledge from degraded images.
Specifically, we propose an ingenious and efficient multi-frame image
restoration network (DparNet) with wide & deep architecture, which integrates
degraded images and prior knowledge of degradation to reconstruct images with
ideal clarity and stability. The degradation prior is directly learned from
degraded images in form of key degradation parameter matrix, with no
requirement of any off-site knowledge. The wide & deep architecture in DparNet
enables the learned parameters to directly modulate the final restoring
results, boosting spatial & intensity adaptive image restoration. We
demonstrate the proposed method on two representative image restoration
applications: image denoising and suppression of atmospheric turbulence effects
in images. Two large datasets, containing 109,536 and 49,744 images
respectively, were constructed to support our experiments. The experimental
results show that our DparNet significantly outperform SoTA methods in
restoration performance and network efficiency. More importantly, by utilizing
the learned degradation parameters via wide & deep learning, we can improve the
PSNR of image restoration by 0.6~1.1 dB with less than 2% increasing in model
parameter numbers and computational complexity. Our work suggests that degraded
images may hide key information of the degradation process, which can be
utilized to boost spatial & intensity adaptive image restoration.
