| Spatial variation RNN[22] |
Motion blur, dynamic scene blur |
The deblurring process is formulated through the wireless impulse response model |
Weights can be learned from another network and different weights can be learned for different fuzzy systems |
Large regional and spatial change structures need to be involved at the same time’ |
| SRN[20] |
Motion blur |
New multiscale cyclic network structure |
The number of trainable parameters is reduced and the training efficiency is improved |
Limited to fixed data sets and training periods |
| DMPHN[21] |
Motion blur |
End to end CNN hierarchical model similar to spatial pyramid matching |
The required filter is small and can be inferred quickly |
Requires large GPU memory |
| DPSR[32] |
LR blurred image |
A new SISR degradation model is designed |
The deep plug and play framework can deal with any fuzzy kernel |
For most real images, it does not match the degradation model |
| BIE-RVD[33] |
Motion blur |
Automatic coding structure of spatiotemporal video screen based on end-to-end differentiable structure |
High accuracy and fast network running speed |
The task of training is complex and difficult |
| DDMS[34] |
Motion blur |
A full convolution structure with filtering transformation and characteristic modulation is constructed |
Real time filtering completely eliminates multi-scale processing and large filters |
Real time filtering completely eliminates multi-scale processing and large filters |
| deblurGAN[27]DeblurGANV2[28] |
Motion blurMotion blur |
The generated countermeasure network based on perceptual loss [9] (perceptual loss) constraint is used for deblurring |
The restored image is more similar to the target image in semantics and closer to people's subjective evaluation of image quality |
The influence of different feature layers in the perceptual network on the perceptual loss is not considered, so that the restored image details are still smooth. |
| Deepdeblur[18] |
dynamic scene blur |
End to end multiscale convolution network |
Without estimating the fuzzy kernel, multi-scale CNN can restore clear images directly and flexibly |
The multi-scale stacked sub network results in large amount of parameters, large consumption of video memory and great difficulty in training |
| SRN-deblur[20] |
Blur of dynamic scene |
End to end multiscale cyclic network |
Multi-scale structure and parameter sharing alleviate the problem of large amount of parameters, and the learning ability is more stable |
The edge is too smooth and there are artifacts |
| DMPHN[21] |
Motion blur |
The deep-seated multi-facet network based on spatial pyramid matching processes fuzzy images through fine hierarchical representation. |
It can solve the problem of performance saturation and run faster than multi-scale method |
It can solve the problem of performance saturation and run faster than multi-scale method |
| MPRnet[35] |
Deblurring, rain removing and noise removing |
A multi-stage progressive image restoration |
It can output accurate spatial details and context information. The network structure is simple and the effect is good |
The deblurring effect under the dark light line is not good |
| MIMO-Net[29] |
Motion blur |
Single encoder multiple input single decoder multiple output |
Increase the network feeling field and make the training less difficult |
The spatial details are lost and the texture is not clear enough |
![Visual comparison of image deblurring results of GoPro test set [13]. Patches blurred by key points are displayed in (b), while patches magnified from deblurring results are displayed in (c) – (h).](https://sciendo-parsed.s3.eu-central-1.amazonaws.com/6471f620215d2f6c89db6dcf/j_ijanmc-2021-040_fig_004.jpg?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20240701T071748Z&X-Amz-SignedHeaders=host&X-Amz-Expires=18000&X-Amz-Credential=AKIA6AP2G7AKP25APDM2%2F20240701%2Feu-central-1%2Fs3%2Faws4_request&X-Amz-Signature=90d83672b541d108494bdba451191d11aa4c1999998357d1bf6c285a20b8cae3)