## 4 Model Training and Discussion of the Results Obtained
### Performance Results of Module 1 (of Figure 3) for Document-Image Deblurring
The number of test images were 11 images from the standard test images (\"Pepper\", \"Lena\", \"Baboon\", \"Bridge\", \"Barbara\", \"Fruit\", \"Cameraman\", \"House\", \"Starfish\", \"Monarch\", \"Plane\") and 100 images from the [PERSON] et al. [78] test dataset. Some of the results of single images such as Lena and Pepper directly show the difference amongst the results obtained from the different involved models. We can see (see Table 3) that our model provides the best PSNR results compared to the other models. For example, compared to the [PERSON] et al. [60] model, our model shows a PSNR improvement ranging between 5% to 8%. Performance Results with Respect to the OCR Performance of Module 2 for Document-Image Contrast and Brightness Enhancement  
One of the sensitive problems while recognizing text through OCR is low contrast and brightness issues. An improvement of the text contrast makes the dark or highlighted parts more readable. For example, the input texts in Figure 10 are not recognizable by an OCR software, but after their contrast and brightness are adjusted, they can recognize text with 95% accuracy. Figure 10 shows that the second module is capable of significant contrast and brightness enhancement. Performance Evaluation of Our Novel Global Model including \"Module 1 + Module 2\" (of Figure 3) for Blindly Enhancing Document Images Simultaneously Distorted by Blur + Noise + Contrast  
After comparing comprehensively the performance of separated submodules in the previous sub-sections in the presence of different artifacts, we discuss in this section the overall performance of the global model (Module 1 + Module 2) compared to other competing related models in the presence of a mix of artifacts or distortions; this test context shall be considered as an extreme stress test of the performance of the best models available. The dataset which is presented in this section consists of document images obtained by our team in harsh acquisition conditions (by cameras) (see Figure 11). This selection of 100 samples has been used for stress testing the different models. As one can see on this collection of images, different forms of distortions are present in the images, for example noise, contrast, and shadows; mostly, various distortions are simultaneously present in the images. (Note: since the images do partially contain some personal data, those related parts have been covered by a black rectangle for privacy reasons.) \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \multirow{2}{*}{**Test Image**} & **Noise** & **PSNR for** & **PSNR for** & **PSNR for** & **PSNR for** & **PSNR for** & **PSNR for** \\  & **Levels in the** & **DBA [51]** & **NASNIM** & **PARICI** & **NLSF** & **NLSF** & **NLSF** & **Our Model** \\  & **Test Image** & **[82]** & **[83]** & **[60]** & **MLP** & **CNN [60]** & **(Module 2)** \\ \hline \multirow{3}{*}{**Pepper**} & 30 & 26.85 & 22.38 & 28.88 & 32.27 & 30.01 & 32.99 & **34.66** \\  & 50 & 25.27 & 21.82 & 25.44 & 27.99 & 28.57 & 30.23 & **32.57** \\  & 70 & 22.11 & 21.58 & 21.46 & 23.04 & 27.04 & 27.70 & **30.01** \\ \hline \multirow{3}{*}{**Lena**} & 30 & 34.35 & 28.18 & 33.88 & 34.21 & 30.01 & 35.19 & **35.66** \\  & 50 & 30.13 & 26.15 & 29.44 & 30.14 & 29.30 & 32.23 & **32.57** \\  & 70 & 25.21 & 25.88 & 25.46 & 25.04 & 27.34 & 30.70 & **30.81** \\ \hline \multirow{3}{*}{**Average Over**} & 30 & 31.79 & 27.07 & 30.86 & 32.28 & 29.77 & 33.35 & **33.76** \\
**11 Images from the** & 50 & 28.27 & 26.38 & 27.47 & 29.28 & 28.09 & 31.34 & **32.57** \\
**Standard Test Images** & 70 & 24.38 & 26.98 & 23.87 & 25.09 & 26.36 & 29.15 & **29.80** \\ \hline \multirow{3}{*}{**Average Over**} & 30 & 31.37 & 26.69 & 29.92 & 31.45 & 28.89 & 32.45 & **32.91** \\
**100 Images from Hradis** & 50 & 27.36 & 25.32 & 27.35 & 28.84 & 27.08 & 30.35 & **31.70** \\ \cline{1-1} \cline{2-1} \cline  
Figure 11: Different image qualities which were extracted from our own made dataset. Image (**a**) shows very bad-quality samples. Image (**b**) shows bad-quality samples.