19 research outputs found
A^2Net: Adjacent Aggregation Networks for Image Raindrop Removal
Existing methods for single images raindrop removal either have poor
robustness or suffer from parameter burdens. In this paper, we propose a new
Adjacent Aggregation Network (A^2Net) with lightweight architectures to remove
raindrops from single images. Instead of directly cascading convolutional
layers, we design an adjacent aggregation architecture to better fuse features
for rich representations generation, which can lead to high quality images
reconstruction. To further simplify the learning process, we utilize a
problem-specific knowledge to force the network focus on the luminance channel
in the YUV color space instead of all RGB channels. By combining adjacent
aggregating operation with color space transformation, the proposed A^2Net can
achieve state-of-the-art performances on raindrop removal with significant
parameters reduction