This injection of visual attention to both generative and discriminative networks is the main contribution of this paper.
At each stage, we introduce a novel per-pixel adaptive design that leverages in-situ supervised attention to reweight the local features.
Ranked #1 on
Image Denoising
on DND
DEBLURRING IMAGE DENOISING IMAGE RESTORATION SINGLE IMAGE DERAINING
Hence, it is important to solve the problem of single image de-raining/de-snowing.
The investigations on rain removal from video or a single image has thus been attracting much research attention in the field of computer vision and pattern recognition, and various methods have been proposed against this task in the recent years.
To handle this issue, this paper provides a better and simpler baseline deraining network by considering network architecture, input and output, and loss functions.
Ranked #2 on
Single Image Deraining
on Rain100L
First, we propose a semi-automatic method that incorporates temporal priors and human supervision to generate a high-quality clean image from each input sequence of real rain images.
Image dehazing aims to recover the uncorrupted content from a hazy image.
Ranked #1 on
Rain Removal
on DID-MDN
We present a comprehensive study and evaluation of existing single image deraining algorithms, using a new large-scale benchmark consisting of both synthetic and real-world rainy images. This dataset highlights diverse data sources and image contents, and is divided into three subsets (rain streak, rain drop, rain and mist), each serving different training or evaluation purposes.
This filtering is guided by a rain-free residue image --- its content is used to set the passbands for the two channels in a spatially-variant manner so that the background details do not get mixed up with the rain-streaks.
This filtering is guided by a rain-free residue image --- its content is used to set the passbands for the two channels in a spatially-variant manner so that the background details do not get mixed up with the rain-streaks.