Abstract:
Adherent raindrops, rainstreaks and snow severely
degrade the perceptual quality of an image, eventually affecting
the performance of several computer vision based applications
which are applied in outdoor scenarios, e.g., traffic monitoring, autonomous driving, etc. Due to the complex appearance
properties, removal of such degradations from an image is
a challenging task. Working towards mitigating this problem,
in this paper, a lightweight network named as WiperNet is
proposed which tackles the problem of raindrops, rain streaks
and snow removal present in an image. The WiperNet makes
use of the proposed Dual Restoration (DR) mechanism, where
the input features are processed twice through the network.
In the network, Multi-scale Context Aware Residual Block
(MCARB) is proposed for integrating contextual information
from various scales. Also, Adaptive Varying Receptive Fusion
Block (AVRFB) is proposed for adaptively fusing the information
acquired through different dilation rates. Finally, we propose a
Feature Refinement Stream which makes use of multiple kernel
sizes of convolution filters and spatio-channel attention blocks
for focusing on relevant information for effective removal of
the degradations while using the coarse outputs of the features
from the initial layers of the network. Substantial experiments
and ablation study scrutinize that the proposed lightweight
WiperNet outperforms the existing state-of-the-art methods for
raindrop, rain streak and snow removal.
