Improved Double Regression Nonlinear Image Super Resolution Model

Lv, Jieyi; Wang, Zhongsheng

doi:10.2478/ijanmc-2023-0055

Figures & Tables

A double regression network training model based on U-Net network transformation is presented

Ablation experiment

PSNR	CA	RB	Direct connection path	PReLu
37.850	No	Yes	Yes	Yes
37.844	Yes	No	Yes	Yes
37.738	No	No	Yes	Yes
37.919	Yes	Yes	No	Yes
37.941	Yes	Yes	Yes	No
37.978	Yes	Yes	Yes	Yes

Adaptation Algorithm on Unpaired Data

Input: Unpaired real-world data: Su;

Paired synthetic data: Sp;

Batch sizes for Su and Sp : x and y;

Indicator function: Sm.

1. Load the pretrained models P and u;

2. while not convergent do

3. Sample unlabeled data {xi} from SU;

4. Sample labeled data {(xi, yi)} from SP;

5. // Update the primal mode

6. Update P by minimizing the objective:

7. ∑i=1m+nIsp(xi)ιp(P(xi),yi)+λιD(D(P(xi)),xi)

\sum\limits_{i = 1}^{m + n} {{I_{{s_p}}}\left( {{x_i}} \right){\iota _p}\left( {P\left( {{x_i}} \right),{y_i}} \right) + \lambda {\iota _D}\left( {D\left( {P\left( {{x_i}} \right)} \right),{x_i}} \right)}

8. // Update the dual model

9. Update D by minimizing the objective:

10. ∑i=1m+nλιp(D(P(xi)),xi)

\sum\limits_{i = 1}^{m + n} {\lambda {\iota _p}\left( {D\left( {P\left( {{x_i}} \right)} \right),{x_i}} \right)}

11. END

Comparison of algorithms for different data sets

Method	Set5PSNR/SSIM	Set14PSNR/SSIM	BSD100PSNR/SSIM
Bicubic	32.40/0.9589	31.32/0.9521	32.87/0.9563
SRCNN	33.36/0.9460	33.78/0.9366	33.57/0.9423
DRCN	33.57/0.9432	33.99/0.9419	33.66/0.9410
ESPCN	34.12/0.9439	34.26/0.9412	34.23/0.9356
SRGAN	34.26/0.9356	34.89/0.9256	34.56/0.9246
Ours	34.12/0.9326	34.56/0.9247	34.57/0.9232

Improved Double Regression Nonlinear Image Super Resolution Model

Figures & Tables

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Ablation experiment

Adaptation Algorithm on Unpaired Data

Comparison of algorithms for different data sets

Paradigm

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