  Given a degraded input image, image restoration aims to recover the missing
high-quality image content. Numerous applications demand effective image
restoration, e.g., computational photography, surveillance, autonomous
vehicles, and remote sensing. Significant advances in image restoration have
been made in recent years, dominated by convolutional neural networks (CNNs).
The widely-used CNN-based methods typically operate either on full-resolution
or on progressively low-resolution representations. In the former case, spatial
details are preserved but the contextual information cannot be precisely
encoded. In the latter case, generated outputs are semantically reliable but
spatially less accurate. This paper presents a new architecture with a holistic
goal of maintaining spatially-precise high-resolution representations through
the entire network, and receiving complementary contextual information from the
low-resolution representations. The core of our approach is a multi-scale
residual block containing the following key elements: (a) parallel
multi-resolution convolution streams for extracting multi-scale features, (b)
information exchange across the multi-resolution streams, (c) non-local
attention mechanism for capturing contextual information, and (d) attention
based multi-scale feature aggregation. Our approach learns an enriched set of
features that combines contextual information from multiple scales, while
simultaneously preserving the high-resolution spatial details. Extensive
experiments on six real image benchmark datasets demonstrate that our method,
named as MIRNet-v2 , achieves state-of-the-art results for a variety of image
processing tasks, including defocus deblurring, image denoising,
super-resolution, and image enhancement. The source code and pre-trained models
are available at https://github.com/swz30/MIRNetv2
