553 research outputs found
Physical-based optimization for non-physical image dehazing methods
Images captured under hazy conditions (e.g. fog, air pollution) usually present faded colors and loss of contrast. To improve their visibility, a process called image dehazing can be applied. Some of the most successful image dehazing algorithms are based on image processing methods but do not follow any physical image formation model, which limits their performance. In this paper, we propose a post-processing technique to alleviate this handicap by enforcing the original method to be consistent with a popular physical model for image formation under haze. Our results improve upon those of the original methods qualitatively and according to several metrics, and they have also been validated via psychophysical experiments. These results are particularly striking in terms of avoiding over-saturation and reducing color artifacts, which are the most common shortcomings faced by image dehazing methods
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
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