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FMD-cGAN: Fast Motion Deblurring using Conditional Generative Adversarial Networks

arXiv 2111.15438 ↗First posted 30 Nov 2021cs.CV eess.IVcited by 0

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Abstract (arXiv)

In this paper, we present a Fast Motion Deblurring-Conditional Generative Adversarial Network (FMD-cGAN) that helps in blind motion deblurring of a single image. FMD-cGAN delivers impressive structural similarity and visual appearance after deblurring an image. Like other deep neural network architectures, GANs also suffer from large model size (parameters) and computations. It is not easy to deploy the model on resource constraint devices such as mobile and robotics. With the help of MobileNet based architecture that consists of depthwise separable convolution, we reduce the model size and inference time, without losing the quality of the images. More specifically, we reduce the model size by 3-60x compare to the nearest competitor. The resulting compressed Deblurring cGAN faster than its closest competitors and even qualitative and quantitative results outperform various recently proposed state-of-the-art blind motion deblurring models. We can also use our model for real-time image deblurring tasks. The current experiment on the standard datasets shows the effectiveness of the proposed method.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 25 of its 46 references that are in the map. First posted on arXiv 30 Nov 2021.

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25 of 46 references.

References · 25

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13 more
  1. 2016Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Networkcited by 1,898
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  12. 2020Raw Image Deblurringcited by 22
  13. 2017Deep Generative Filter for Motion Deblurringcited by 14

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