## 3 Our Novel Deep Neural Method for Blind Enhancement  
The basic problem formulation is graphically presented in Figure 1, which essentially underscores the CNN deep neural model's goal to be developed. However, for reaching the goal, it has been shown in the relevant literature that one single deep neural network cannot solve the complex problem at hand. Instead, a much more complex and modular deep neural architecture is needed. Indeed, the primary distorting artifacts that can be found in document images can be categorized into the following categories (see Figure 2):  
* Blur problem(s), e.g., focus blur, Gaussian blur, motions blur, etc. ;
* Noise problem(s), e.g., salt noise or pepper noise, depending on image sensor sensitivity;
* Contrast problem(s), e.g., shadows, spotlight, and contrast deficits. For solving (removing) the mentioned distortions from a given document image, our overall model (see Figure 3) is designed with two sequential modules: (a) a document-image blur enhancement module, and (b) a document-image noise and contrast enhancement module. Figure 3: The novel global model is composed of (**a**) document-image deblurring (Module 1), and (**b**) document-image denoising and contrast adjustment modules (Module 2). Figure 2: Main distortion problems encountered in document images: (**a**) a document image, usually taken from a mobile phone with focus blur distortion; (**b**) a document image with motion blur; (**c**) a document image with salt and pepper noise; (**d**) a document image with spotlight blocking/disturbing the readability of part of the text contained in the document. (Source: our own pictures.) The blur enhancement module is responsible for removing blur artifacts of the image. It has two types: motion blur and focus blur. The second module is responsible for eliminating or removing noise and contrast problems. The noise types can be salt and pepper noise and Gaussian noise. This module also improves contrast by fixing issues related to a spotlight, shadows, and overall image contrast enhancement. Document-Image Deblurring (Module 1, see Figure 3) and Document-Image Joint Contrast and Noise Enhancement (Module 2, See Figure 3)  
Figure 4 shows the structure of our overall CNN architecture for deblurring, as well as contrast and noise enhancement. As we can see from a comprehensive review of the relevant state of the art, the enhancement process is one of the most challenging tasks in image enhancement research. Thereby, it is always required to perform various additional processing steps before the CNN model can enhance images with an acceptable resulting or final image quality. Therefore, in this work, we introduce two different steps before providing the input document image to the core convolutional neural network model. The input image in our illustrative experiments has a size of 246 \(\times\) 246 pixels. Figure 4: Our novel CNN model architecture (Modules 1 and 2, see Figure 3) for enhancement. Notice that the input image is split into the patches, here just an example for illustration, into four sub-images (each of which have 3 RGB channels). The patches (i.e., the patch concept) will then be applied to each of these 4 sub-images. First, we split the image into small patches, all with the size of 128 \(\times\) 128 pixels. The patches have a little overlap for later enabling a better reconstruction of the original complete image. In sum, in our illustrative experimental example, we have 12 images (each one patch) used for the next step (i.e., four split image patches, each with its corresponding three RGB channels). In a second step, we involve a bank of preprocessing layers, in which we intentionally add blur effects, Gabor with different blur, and Gabor kernels. This set of blurred images and Gabor images, along with the original patches, are taken as our basis for the CNN model's input (see Section A of Figure 4). The main idea behind this complex preprocessing structure is the intention of pre-filtering non-appropriate data.