# Advances in Neural Network-Based Image, Thermal, Infrared, and X-Ray Technologies
## 2 Literature Review Methodology and State-of-the-Art
### State-of-the-Art: A Technology Review from 2005 to 2024
#### 2.3.1 Image Sensors  
Comparing existing technology of imaging sensors is complicated, because very often they are used in very strict problems. Thus, there are not enough source dates which are \begin{table}
\begin{tabular}{c c c} \hline
**Task** & **Classical Method** & **Neural Networks** \\ \hline Denoising [12] & Median filter, wavelet denoising, BM3D & Autoencoders, DnCNN, U-Net \\ \hline Object Detection [1,13] & Haar cascades, HOG + SVM & YOLO, SSD, Faster R-CNN, \\  & RetinaNet \\ \hline Image Segmentation [14,15] & Thresholding, watershed, region growing & U-Net, FCN, DeepLab, Mask R-CNN \\ \hline Image Classification [16,17] & SIFT + SVM, k-NN, PCA & LeNet, AlexNet, VGGNet, ResNet, \\  & & EfficientNet \\ \hline Super-resolution [2,18] & Bicubic interpolation, Lanczos filter & SRCNN, EDSR, ESRGAN, RCAN \\ \hline Image Reconstruction [19,20] & Filtered back projection, iterative methods & Autoencoders, GANs, U-Net \\ \hline \end{tabular}
\end{table} 
Table 1: Comparison of classical methods and neural networks for selected image processing tasks. \begin{table}
\begin{tabular}{c c c c} \hline
**Name** & **Year** & **Important Features** & **Typical Applications** \\ \hline LeNet-5 [21] & 1998 & Early CNN, small depth, & Digit classification, OCR \\  & handwritten digit recognition & & \\ \hline AlexNet [16] & 2012 & Deep CNN, ReLU, dropout, & Image classification \\  & & large-scale training & \\ \hline GoogLeNet [22] & 2015 & Inception modules, parallel & Image classification, object \\  & & convolutions, reduced parameters & detection \\ \hline VGGNet [23] & 2014 & Deep, simple stacked convolutional & Image classification, feature \\  & & layers, small filters & extraction \\ \hline ResNet [17] & 2016 & Residual (skip) connections, very & Classification, detection, \\  & & deep networks & segmentation \\ \hline DenseNet [24] & 2017 & Dense connections between layers, & Classification, feature extraction \\  & & improved gradient flow & \\ \hline U-Net [14] & 2015 & Encoder-decoder with skip & Biomedical segmentation, image \\  & & connections & segmentation \\ \hline GAN [19] & 2014 & Generative adversarial training, & Image generation, super-resolution, \\  & & generator and discriminator & style transfer \\ \hline MobileNet [25] & 2017 & Efficient, lightweight, depthwise & Mobile and edge deployment, \\  & & separable convolutions & classification \\ \hline Vision Transformer (ViT) [26] & 2021 & Transformer blocks, self-attention, & Image classification, segmentation, \\  & & patch embeddings & detection \\ \hline \end{tabular}
\end{table} 
Table 2: Examples of neural network architectures for image analysis and processing. connected with the same case. Despite this, it is still possible to identify some techniques which are used in some class of problems [27]. One of the most frequently repeated motivations to improve image sensors is energy efficiency [8; 9; 28; 29; 30; 31; 32; 33]. It is connected to the application of these sensors to battery-powered devices. For those which are supplied by feeder-cable, it means a reduction of usage cost. The next important reason is to increase the performance of data processing [9; 29; 34; 35]. In [36] it was also mentioned that the additional objective is to reduce memory usage. To solve these problems, some techniques are used. One of the most frequently appearing solutions is to implement a neural network in hardware [28; 29; 30; 34; 36; 37; 38; 39; 40; 41]. This could be a separate device, an on-chip module, or a FPGA component. This is not the only possible way to obtain better results. Another is to introduce techniques that could be used to train a neural network more effectively [9; 32; 42; 43; 44; 45; 46]. They are very different: additional training of the first layer, preparing images, using multisource remote sensing images, or including signals from other sensors. Another significant feature is the ability to detect important elements of an image [8; 38; 40; 44; 47]. This allows limiting the amount of data that must be processed.