Multi-scale architectures have shown effectiveness in a variety of tasks
thanks to appealing cross-scale complementarity. However, existing
architectures treat different scale features equally without considering the
scale-specific characteristics, \textit{i.e.}, the within-scale characteristics
are ignored in the architecture design. In this paper, we reveal this missing
piece for multi-scale architecture design and accordingly propose a novel
Multi-Scale Adaptive Network (MSANet) for single image denoising. Specifically,
MSANet simultaneously embraces the within-scale characteristics and the
cross-scale complementarity thanks to three novel neural blocks, \textit{i.e.},
adaptive feature block (AFeB), adaptive multi-scale block (AMB), and adaptive
fusion block (AFuB). In brief, AFeB is designed to adaptively preserve image
details and filter noises, which is highly expected for the features with mixed
details and noises. AMB could enlarge the receptive field and aggregate the
multi-scale information, which meets the need of contextually informative
features. AFuB devotes to adaptively sampling and transferring the features
from one scale to another scale, which fuses the multi-scale features with
varying characteristics from coarse to fine. Extensive experiments on both
three real and six synthetic noisy image datasets show the superiority of
MSANet compared with 12 methods. The code could be accessed from
this https URL

本文提出了一种基于多尺度自适应网络的单张图像去噪方法 (MSANet), 它同时考虑了尺度间的互补性和尺度内部的特征，并提出了自适应特征块 (AFeB), 自适应多尺度块 (AMB) 和自适应融合块 (AFuB) 进行实现，并在多组图片去噪实验中展现出比传统方法更好的表现。

单图像去噪的多尺度自适应网络

Multi-Scale Adaptive Network for Single Image Denoising

Recent studies on unsupervised image-to-image translation have made a
remarkable progress by training a pair of generative adversarial networks with
a cycle-consistent loss. However, such unsupervised methods may generate
inferior results when the image resolution is high or the two image domains are
of significant appearance differences, such as the translations between
semantic layouts and natural images in the Cityscapes dataset. In this paper,
we propose novel Stacked Cycle-Consistent Adversarial Networks (SCANs) by
decomposing a single translation into multi-stage transformations, which not
only boost the image translation quality but also enable higher resolution
image-to-image translations in a coarse-to-fine manner. Moreover, to properly
exploit the information from the previous stage, an adaptive fusion block is
devised to learn a dynamic integration of the current stage's output and the
previous stage's output. Experiments on multiple datasets demonstrate that our
proposed approach can improve the translation quality compared with previous
single-stage unsupervised methods.

通过将单一翻译分解为多阶段变换并开发一种自适应融合块，我们提出了新颖的堆叠循环一致性对抗网络（SCANs），以在粗糙到精细的方式下提高图像翻译质量并实现更高分辨率的图像翻译。