DeblurGAN-v2: Deblurring (Orders-of-Magnitude) Faster and Better

Abstract

We present a new end-to-end generative adversarial network (GAN) for single image motion deblurring, named DeblurGAN-v2, which considerably boosts state-of-the-art deblurring efficiency, quality, and flexibility. DeblurGAN-v2 is based on a relativistic conditional GAN with a doublescale discriminator. For the first time, we introduce the Feature Pyramid Network into deblurring, as a core building block in the generator of DeblurGAN-v2. It can flexibly work with a wide range of backbones, to navigate the balance between performance and efficiency. The plugin of sophisticated backbones (e.g., Inception-ResNet-v2) can lead to solid state-of-the-art deblurring. Meanwhile, with light-weight backbones (e.g., MobileNet and its variants), DeblurGAN-v2 reaches 10-100 times faster than the nearest competitors, while maintaining close to state-ofthe-art results, implying the option of real-time video deblurring. We demonstrate that DeblurGAN-v2 obtains very competitive performance on several popular benchmarks, in terms of deblurring quality (both objective and subjective), as well as efficiency. Besides, we show the architecture to be effective for general image restoration tasks too. Our codes, models and data are available at: https: //github.com/KupynOrest/DeblurGANv2.

Face and Body-Based Human Recognition by GAN-Based Blur Restoration

“…First, blur restoration is performed using other GAN methods besides DeblurGAN, which was proposed in this study for comparison. Specifically, CycleGAN [ 20 ], Pix2pix [ 22 ], attention-guided GAN (AGGAN) [ 37 , 38 ], and DeblurGAN version 2 (DeblurGANv2) [ 39 ] were used for GAN models. Table 7 and Figure 8 show the comparison results of GAN for DFB-DB2, and our method outperforms the state-of-the-art methods.…”

“…There were lots of works that use GAN for deblur [ 38 , 39 , 54 , 55 , 56 ]. However, most previous works aimed at the visibility enhancement of general scene images, whereas the main purpose of our research is to enhance the recognition accuracy of face and body images.…”

“…It means that the maximization of intra-class consistency (from matching between same people) and inter-class variation (from matching between different people) in the generated image is more important than the visibility enhancement in our GAN. Therefore, we compared the recognition accuracies of face and body images by our GAN with those by other GANs, as shown in Table 7 and Table 12 and Figure 8 and Figure 15 , instead of the metrics showing the image visibility, such as peak signal-to-noise ratio (PSNR) and structural similarity (SSIM), like previous works [ 38 , 39 , 54 , 55 , 56 ]. Consequently, it is not appropriate to use our method to handle the natural images.…”

Estimation of the Gender Ratio of Chickens Based on Computer Vision: Dataset and Exploration

“…In recent years, many related theoretical methods for image deblurring have been proposed. Based on cGAN and "content loss", the DeblurGAN network was proposed, which had the best deblurring effect at that time [ 37 ]. In the following improved version, DeblurGAN-v2 builds a deblurring framework for FPN [ 37 ], and it can select the backbone network and apply different requirements to select different backbone networks.…”

Nonlinear decoding of natural images from large-scale primate retinal ganglion recordings

“…Specifically, this step represents an unknown combination of super-resolution, deblurring, denoising, and inpainting. With ongoing advances in image restoration networks that can handle more complex blur kernels and noise, it is likely that further improvements in performance are possible[36][37][38][39][40][41][42][43][44].Finally, while our decoding approach helped shed some light on the importance of nonlinear spike temporal correlations and OFF midget cell signals on accurate, high-pass decoding, the specific mechanisms of visual decoding have yet to be fully investigated. Indeed, many other sources of nonlinearity, including nonlinear spatial interactions within RGCs or nonlinear interactions between RGCs or RGC types, are all factors that could help justify nonlinear decoding that we did not explore…”