LCA-Net: Light Convolutional Autoencoder for Image Dehazing

24 Aug 2020  ·  Pavan A, Adithya Bennur, Mohit Gaggar, Shylaja S S ·

Image dehazing is a crucial image pre-processing task aimed at removing the incoherent noise generated by haze to improve the visual appeal of the image. The existing models use sophisticated networks and custom loss functions which are computationally inefficient and requires heavy hardware to run. Time is of the essence in image pre-processing since real time outputs can be obtained instantly. To overcome these problems, our proposed generic model uses a very light convolutional encoder-decoder network which does not depend on any atmospheric models. The network complexity-image quality trade off is handled well in this neural network and the performance of this network is not limited by low-spec systems. This network achieves optimum dehazing performance at a much faster rate, on several standard datasets, comparable to the state-of-the-art methods in terms of image quality.

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Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Image Dehazing KITTI LCA PSNR 18.32 # 3
Image Dehazing RESIDE LCA-Net PSNR 17.07 # 1


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