6 research outputs found
MARA-Net: Single Image Deraining Network with Multi-level connections and Adaptive Regional Attentions
Removing rain streaks from single images is an important problem in various
computer vision tasks because rain streaks can degrade outdoor images and
reduce their visibility. While recent convolutional neural network-based
deraining models have succeeded in capturing rain streaks effectively,
difficulties in recovering the details in rain-free images still remain. In
this paper, we present a multi-level connection and adaptive regional attention
network (MARA-Net) to properly restore the original background textures in
rainy images. The first main idea is a multi-level connection design that
repeatedly connects multi-level features of the encoder network to the decoder
network. Multi-level connections encourage the decoding process to use the
feature information of all levels. Channel attention is considered in
multi-level connections to learn which level of features is important in the
decoding process of the current level. The second main idea is a wide regional
non-local block (WRNL). As rain streaks primarily exhibit a vertical
distribution, we divide the grid of the image into horizontally-wide patches
and apply a non-local operation to each region to explore the rich rain-free
background information. Experimental results on both synthetic and real-world
rainy datasets demonstrate that the proposed model significantly outperforms
existing state-of-the-art models. Furthermore, the results of the joint
deraining and segmentation experiment prove that our model contributes
effectively to other vision tasks
Towards Robust Rain Removal Against Adversarial Attacks: A Comprehensive Benchmark Analysis and Beyond
Rain removal aims to remove rain streaks from images/videos and reduce the
disruptive effects caused by rain. It not only enhances image/video visibility
but also allows many computer vision algorithms to function properly. This
paper makes the first attempt to conduct a comprehensive study on the
robustness of deep learning-based rain removal methods against adversarial
attacks. Our study shows that, when the image/video is highly degraded, rain
removal methods are more vulnerable to the adversarial attacks as small
distortions/perturbations become less noticeable or detectable. In this paper,
we first present a comprehensive empirical evaluation of various methods at
different levels of attacks and with various losses/targets to generate the
perturbations from the perspective of human perception and machine analysis
tasks. A systematic evaluation of key modules in existing methods is performed
in terms of their robustness against adversarial attacks. From the insights of
our analysis, we construct a more robust deraining method by integrating these
effective modules. Finally, we examine various types of adversarial attacks
that are specific to deraining problems and their effects on both human and
machine vision tasks, including 1) rain region attacks, adding perturbations
only in the rain regions to make the perturbations in the attacked rain images
less visible; 2) object-sensitive attacks, adding perturbations only in regions
near the given objects. Code is available at
https://github.com/yuyi-sd/Robust_Rain_Removal.Comment: 10 pages, 6 figures, to appear in CVPR 202