| 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-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Credential=AKIA6AP2G7AKOUXAVR44%2F20251206%2Feu-central-1%2Fs3%2Faws4_request&X-Amz-Date=20251206T035637Z&X-Amz-Expires=3600&X-Amz-Signature=4985cca16d3e6ad717bece15ceb14e5fbdc367a2e06c8e096731f37bdab4d341&X-Amz-SignedHeaders=host&x-amz-checksum-mode=ENABLED&x-id=GetObject)