Development of deblurring algorithm
In the CCTV environment, face recognition rate can be lowered due to image quality degradation
→ Image blurred due to movement and compression artifacts
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
Performance of deblurring algorithm
- Improved performance and speed with advanced training and inference technique
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 |
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