1,516 research outputs found
Fast Deep Multi-patch Hierarchical Network for Nonhomogeneous Image Dehazing
Recently, CNN based end-to-end deep learning methods achieve superiority in
Image Dehazing but they tend to fail drastically in Non-homogeneous dehazing.
Apart from that, existing popular Multi-scale approaches are runtime intensive
and memory inefficient. In this context, we proposed a fast Deep Multi-patch
Hierarchical Network to restore Non-homogeneous hazed images by aggregating
features from multiple image patches from different spatial sections of the
hazed image with fewer number of network parameters. Our proposed method is
quite robust for different environments with various density of the haze or fog
in the scene and very lightweight as the total size of the model is around 21.7
MB. It also provides faster runtime compared to current multi-scale methods
with an average runtime of 0.0145s to process 1200x1600 HD quality image.
Finally, we show the superiority of this network on Dense Haze Removal to other
state-of-the-art models.Comment: CVPR Workshops Proceedings 202
Hybrid Pixel-Unshuffled Network for Lightweight Image Super-Resolution
Convolutional neural network (CNN) has achieved great success on image
super-resolution (SR). However, most deep CNN-based SR models take massive
computations to obtain high performance. Downsampling features for
multi-resolution fusion is an efficient and effective way to improve the
performance of visual recognition. Still, it is counter-intuitive in the SR
task, which needs to project a low-resolution input to high-resolution. In this
paper, we propose a novel Hybrid Pixel-Unshuffled Network (HPUN) by introducing
an efficient and effective downsampling module into the SR task. The network
contains pixel-unshuffled downsampling and Self-Residual Depthwise Separable
Convolutions. Specifically, we utilize pixel-unshuffle operation to downsample
the input features and use grouped convolution to reduce the channels. Besides,
we enhance the depthwise convolution's performance by adding the input feature
to its output. Experiments on benchmark datasets show that our HPUN achieves
and surpasses the state-of-the-art reconstruction performance with fewer
parameters and computation costs
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