School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China.
Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou 310018, China.
Sensors (Basel). 2022 Aug 18;22(16):6216. doi: 10.3390/s22166216.
Blind image deblurring is a challenging problem in computer vision, aiming to restore the sharp image from blurred observation. Due to the incompatibility between the complex unknown degradation and the simple synthetic model, directly training a deep convolutional neural network (CNN) usually cannot sufficiently handle real-world blurry images. An existed generative adversarial network (GAN) can generate more detailed and realistic images, but the game between generator and discriminator is unbalancing, which leads to the training parameters not being able to converge to the ideal Nash equilibrium points. In this paper, we propose a GAN with a dual-branch discriminator using multiple sparse priors for image deblurring (DBSGAN) to overcome this limitation. By adding the multiple sparse priors into the other branch of the discriminator, the task of the discriminator is more complex. It can balance the game between the generator and the discriminator. Extensive experimental results on both synthetic and real-world blurry image datasets demonstrate the superior performance of our method over the state of the art in terms of quantitative metrics and visual quality. Especially for the GOPRO dataset, the averaged PSNR improves 1.7% over others.
盲图像去模糊是计算机视觉中的一个具有挑战性的问题，旨在从模糊观测中恢复清晰的图像。由于复杂的未知退化与简单的合成模型之间的不兼容性，直接训练深度卷积神经网络（CNN）通常无法充分处理真实世界的模糊图像。现有的生成对抗网络（GAN）可以生成更详细和逼真的图像，但生成器和判别器之间的博弈是不平衡的，这导致训练参数无法收敛到理想的纳什平衡点。在本文中，我们提出了一种使用多稀疏先验的具有双分支判别器的 GAN 进行图像去模糊（DBSGAN），以克服这一限制。通过将多个稀疏先验添加到判别器的另一个分支中，判别器的任务更加复杂。它可以平衡生成器和判别器之间的博弈。在合成和真实模糊图像数据集上的广泛实验结果表明，我们的方法在定量指标和视觉质量方面都优于最先进的方法。特别是对于 GOPRO 数据集，平均 PSNR 比其他方法提高了 1.7%。
