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Development of deblurring algorithm
  • Development of deblurring algorithm 1번 상세이미지 썸네일
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Development of deblurring algorithm

Development of deblurring algorithm

Development background

In the CCTV environment, face recognition rate can be lowered due to image quality degradation

→ Image blurred due to movement and compression artifacts

Project details

A function that supplements the recognition rate by improving image quality as object detection/classification and face recognition rates may be lowered due to image quality degradation in the CCTV environment

Details of deblurring algorithm development

deblurring 01.png
  • 1. Development of deblurring algorithm to remove blur caused by motion
  • 2. Video deblurring model established based on deep learning
  • 3. Deblurring datasets created for training and evaluation

Performance of deblurring algorithm

- Improved performance and speed with advanced training and inference technique

Current performance
Method PSNR SSIM FPS
IFI_RNN (C1H1) 28.79 0.8647 57.93
IFI_RNN (C1H2) 29.03 0.8712 45.53
IFI_RNN (C1H3) 29.07 0.8730 37.16
IFI_RNN (C1H4) 29.16 0.8730 30.67
IFI_RNN (C2H1) 29.72 0.8884 41.50
IFI_RNN (C2H2) 29.72 0.8884 34.04
IFI_RNN (C2H3) 29.97 0.8947 28.84
IFI_RNN (C2H4) 29.93 0.8943 25.29
Improved performance
PSNR SSIM FPS
29.05 (+0.26) 0.8749 (+0.011) 111.93 (x1.93)
29.59 (+0.56) 0.8887 (+0.018) 81.96 (x1.80)
29.81 (+0.74) 0.8943 (+0.021) 68.78 (x1.85)
29.83 (+0.67) 0.8950 (+0.022) 56.81 (x1.85)
30.28 (+0.56) 0.9037 (+0.015) 65.05 (x1.56)
30.73 (+1.01) 0.9125 (+0.024) 53.22 (x1.56)
30.72 (+0.75) 0.9121 (+0.017) 46.75 (x1.62)
30.76 (+0.83) 0.9126 (+0.018) 41.42 (x1.63)

Results of deblurring algorithm development

deblurring 03.png