Deep Learning in Image Processing and Pattern Recognition

1. Image Preprocessing Field

The current field shows a trend of multi-dimensional fusion [1], the use of lightweight convolutional self-encoder and generative adversarial network in denoising, super-resolution tasks beyond the traditional methods, and multimodal fusion technology through the integration of visible/infrared/depth map data to enhance feature extraction. In future, it is necessary to build a quantum entanglement parallel denoising system, develop neural radiation field three-dimensional dynamic reconstruction technology, and integrate optoelectronic hardware design to guarantee data security [2].

Image preprocessing techniques focus on improving data representation and quality optimization, and the following research articles included in this Special Issue drive innovation in image preprocessing methods in cross-modal scenarios. For image enhancement and dynamic scene analysis, “Fully Automatic Approach for Smoke Tracking Based on Deep Image Quality Enhancement and Adaptive Level Set Model” suppresses noise and reconstructs the image through a convolutional self-encoder, combining HSV thresholding and dynamic energy features to achieve smoke tracking. “Deep Signal-Dependent Denoising Noise Algorithm” combines Retinex enhancement and reparameterized convolution to achieve 30 fps real-time inference in low-illumination fall detection. In the field of HDR tone mapping, “Tone Mapping Method Based on the Least Squares Method” and “Three-Stage Tone Mapping Algorithm” are based on the improved light estimation of the Retinex model and the three-stage compression mechanism, respectively, to balance the dynamic range and detail retention. In terms of image enhancement, “Research on Retinex Algorithm Combining with Attention Mechanism for Image Enhancement” optimizes the decomposition and illumination reconstruction network of Enhance-Net through the attention mechanism to alleviate the noise and color distortion problems of Retinex-Net.

2. Feature and Image Selection

A self-supervised and comparative learning framework significantly reduces the dependence on labeled data [3], and the attention mechanism is combined with reinforcement learning to optimize dynamic sampling. In future, it is necessary to build a self-supervised contrast collaboration framework, develop Transformer–dynamic convolution hybrid architecture [4], and strengthen cross-scale modeling and interpretability.

Feature extraction and image selection techniques, on the other hand, focus on improving data representation, and the following articles in this Special Issue achieve the optimization of feature representation in specific areas. These techniques serve more in the preprocessing stage, providing quality data input for subsequent classification and detection tasks. “Detection of Small Lesions on Grape Leaves Based on Improved YOLOv7” proposes an improved YOLOv7 model for the problems of high leakage rate and inaccurate localization in the detection of small lesions on grape leaves. In “Defect Detection Method of Phosphor in Glass Based on Improved YOLO5 Algorithm”, the YOLOv5 algorithm is improved to detect small defects in PiG materials. “Crowd Counting by Multi-Scale Dilated Convolution Networks” proposes a multi-scale hollow convolution network to solve the problem of the uneven distribution of dense crowds and head scale difference. “Research on Small Acceptance Domain Text Detection Algorithm Based on Attention Mechanism and Hybrid Feature Pyramid” proposes a fusion of attention mechanism and hybrid feature pyramid to address the issue of insufficient feature extraction for small text detection in complex scenes. The lightweight network architecture is embedded with a hybrid attention module, and a hybrid feature pyramid is constructed to integrate shallow details and deep semantic features across layers, and combined with a bidirectional long short-term memory network to strengthen contextual modeling.

3. Image Processing and Pattern Recognition

Vision Transformer dominates image classification and segmentation through the self-attention mechanism, and dynamic sparse attention improves real-time analysis capabilities [5]. In future, we need to design a multimodal synergy framework, develop a physical embedding model to integrate a priori knowledge such as light field equations, and combine it with dynamic pruning to balance performance [6].

Image processing pattern recognition technology is directly aimed at target detection, classification, and segmentation tasks, and solves practical challenges such as occlusion and small targets through algorithm optimization. The following articles in this Special Issue reflect task-specific algorithm improvements; aiming at the frontiers of multimodal perception and intelligent computing, this multi-disciplinary research proposes innovative solutions. “Offline Mongolian Handwriting Recognition Based on Data Augmentation and Improved ECA-Net” improves the recognition capability of complex concatenated characters by combining the dual data augmentation strategy of random erasure and horizontal waveform transformation, combined with an improved channel attention mechanism, enhancing complex concatenated character recognition. In the field of target detection, “YOLO-Rlepose: Improved YOLO Based on Swin Transformer and Rle-Oks Loss for Multi-Person Pose Estimation” integrates Swin Transformer and Rle-Oks loss function to solve the occlusion interference problem in multi-person pose estimation. This problem is also solved by “A Residual Network with Efficient Transformer for Lightweight Image Super-Resolution” and “Pixel-Level Degradation for Text Image Super-Resolution and Recognition” which used residual network and Rle-Oks loss function, respectively.

In terms of line-of-sight and pose tracking, “Gaze Estimation Method Combining Facial Feature Extractor with Pyramid Squeeze Attention Mechanism” combines pyramid squeeze attention to improve environmental robustness, whereas “An Improved Unscented Kalman Filtering Combined with Feature Triangle for Head Position Tracking” improves the head tracking accuracy in surgical scenes using geometric feature triangles. In the field of multimodal recognition, “A Multi-View Face Expression Recognition Method Based on DenseNet and GAN” adopts lightweight DSC-DenseNet and a dual-path LD-GAN model, while “Speech Emotion Recognition Based on Deep Residual Shrinkage Network” combines Deep Residual Shrinkage Network with bidirectional GRU to extract speech emotion features. Finally, “Anomalous Behavior Detection with Spatiotemporal Interaction and Autoencoder Enhancement” proposes a joint framework of spatiotemporal interaction graph convolution and confidence-enhanced autoencoder.

4. Image Processing in Intelligent Transportation

In the field of intelligent transportation, multi-sensor fusion is used to build high-precision 3D environment models [7], event cameras help to break through the traditional frame rate limitations, and federated learning is employed to optimize global traffic prediction. In future, we need to develop an impulse neural network to drive heterogeneous data alignment, construct a meta-learning cross-domain adaptive framework, and establish a privacy security sharing mechanism [8].

The intelligent transportation field relies on technologies such as monocular depth estimation and lightweight CNN models to promote the implementation of autonomous driving and traffic management. The following articles in this Special Issue promote the development of technology in this field. “Quality of Life Prediction on Walking Scenes Using Deep Neural Networks and Performance Improvement Using Knowledge Distillation” proposes a deep learning and knowledge distillation-based walking scenes quality-of-life prediction method to replace traditional high-cost questionnaires. The authors extract 15 types of scene features such as pedestrian density and road width using YOLOv4 target detection and DDRNet-23-slim semantic segmentation, and construct a deep model integrating one-dimensional convolution, bidirectional LSTM, and gated recurrent units to capture spatiotemporal features in order to infer QoL scores.

The articles “YOLO-Rlepose: Improved YOLO Based on Swin Transformer and Rle-Oks Loss for Multi-Person Pose Estimation” and “Detecting Human Falls in Poor Lighting: Object Detection and Tracking Approach for Indoor Safety”, mentioned in the previous section, also make contributions to this field.

5. Hyperspectral Image Processing

End-to-end models are employed to realize the accurate classification of agricultural pests and diseases, whereas physically driven unmixing algorithms can break through the hardware acquisition limit [9]. In future, it is necessary to build a hybrid architecture of self-attention–deformable convolution, develop a semi-supervised generation framework to integrate spectral unmixing theory, and promote hardware collaborative optimization [10].

The optimization of neural network architecture design and training strategy constitutes another important direction. The following articles in this Special Issue balance the efficiency and performance of the model and optimize the network for specific scenarios. “Multi-Scale Spatial-Spectral Attention-Based Neural Architecture Search for Hyperspectral Image Classification” designs a multi-scale attention-extended search space, integrating different scale convolution and attention modules such as CBAM and SE to strengthen spatial feature extraction; a slow–fast learning paradigm is introduced to optimize the architecture search efficiency by combining group intelligence; and the Lion optimizer is used to update the parameters by symbolic operations to reduce the memory and training costs.

6. Biomedical Image Processing

In biomedicine, multimodal fusion technology improves tumor localization accuracy, self-supervised models reduce the cost of medical diagnosis [11], and adversarial networks solve the problem of long-tailed distribution. In future, we need to develop a three-dimensional cross-modal alignment framework, integrate adversarial generation and domain adaptation techniques, and revolutionize the paradigm of medical image analysis [12].

The field of biomedical image processing is supported by medical imaging-specific algorithms to improve diagnostic accuracy. “Fully Automatic Approach for Smoke Tracking Based on Deep Image Quality Enhancement and Adaptive Level Set Model” and “Speech Emotion Recognition Based on Deep Residual Shrinkage Network” provide technical support in this context.

7. Advances, Challenges, and Research Trends in Deep Learning for Image Processing and Pattern Recognitions

Graph convolutional networks realize the real-time detection of abnormal events, and federated learning promotes cross-border security compliance [13]. In future, we need to couple privacy computing and edge reasoning, deepen spatiotemporal graph convolutional cross-modal parsing, and build a multimodal meta-knowledge base [14].

Image processing techniques for intelligent surveillance scenarios focus on human behavior analysis and security applications, and the following articles in this Special Issue reflect the importance of scenario-based algorithm design. In “X-ray Security Inspection Image Dangerous Goods Detection Algorithm Based on Improved YOLOv4”, the deformable convolutional reconstruction PANet module is introduced to dynamically adjust the sensing field to align the features and solve the problem of misalignment in complex shape detection; the Focal-EIoU loss function is used to balance the weights of difficult and easy samples to accelerate convergence; and the confidence of overlapping frames is dynamically adjusted with Soft-NMS to reduce leakage detection.

Moreover, the previously mentioned articles “Fully Automatic Approach for Smoke Tracking Based on Deep Image Quality Enhancement and Adaptive Level Set Model”, “Quality of Life Prediction on Walking Scenes Using Deep Neural Networks and Performance Improvement Using Knowledge Distillation”, “Anomalous Behavior Detection with Spatiotemporal Interaction and Autoencoder Enhancement”, and “Detecting Human Falls in Poor Lighting: Object Detection and Tracking Approach for Indoor Safety” all contribute to this field to varying degrees.

8. Deep Learning for Image Processing

Dynamic inference technology balances the efficiency of denoising tasks, whereas model compression technology adapts to embedded devices [15]. In future, it is necessary to integrate adaptive computing flow and heterogeneous compression frameworks [16] and construct a causal graph network to improve the robustness of industrial algorithms.

Deep learning-based end-to-end image processing techniques focus on image generation, reconstruction, and editing, and the following articles in this Special Issue provide technical support in this area. Aiming at the problem of target detection and text processing optimization, “A Multi-Stage Adaptive Copy-Paste Data Augmentation Algorithm Based on Model Training Preferences” proposes a staged adaptive copy-paste augmentation strategy to dynamically adjust the tail category sample size to balance the detection task category bias. “Text Emotion Recognition Based on XLNet-BiGRU-Attention” fuses XLNet two-stream self-attention and BiGRU-attention mechanisms to improve the efficiency of text emotion feature extraction through semantic–positional cooperative coding.

In the field of wireless sensor networks, “E-ReInForMIF Routing Algorithm Based on Energy Selection and Erasure Code Tolerance Machine” combines erasure code redundancy coding and energy equalization routing to enhance the fault tolerance of data transmission, whereas “Improved Reconstruction Algorithm of Wireless Sensor Network Based on BFGSQuasi-Newton Method” optimizes compressed sensory reconstruction using the L-BFGS quasi-Newton method and guarantees the convergence of the algorithm through Wolfe line search. On the other hand, “Research on Spectrum Prediction Technology Based on B-LTF” constructs a hybrid BP-LSTM model, which utilizes BP to extract nonlinear features and LSTM to capture temporal dependencies, so as to enhance the accuracy of dynamic spectrum prediction.

In image processing, “Multi-Task Learning for Scene Text Image Super-Resolution with Multiple Transformers” designs a multi-task Transformer architecture to optimize the super-scoring and denoising tasks through joint feature sharing and enhancement modules, and combines positional coding to cope with text deformation. In network coverage optimization, “A Coverage Hole Patching Algorithm for Heterogeneous Wireless Sensor Networks” proposes a mobile node scheduling strategy based on outer circle localization to dynamically fill the coverage holes in heterogeneous networks according to vulnerability size priority.

Moreover, the previously mentioned articles “Offline Mongolian Handwriting Recognition Based on Data Augmentation and Improved ECA-Net”, “YOLO-Rlepose: Improved YOLO Based on Swin Transformer and Rle-Oks Loss for Multi-Person Pose Estimation”, “Detection of Small Lesions on Grape Leaves Based on Improved YOLOv7”, “A Residual Network with Efficient Transformer for Lightweight Image Super-Resolution”, “Pixel-Level Degradation for Text Image Super-Resolution and Recognition”, “Multi-Scale Spatial–Spectral Attention-Based Neural Architecture Search for Hyperspectral Image Classification”, and “Deep Learning in the Phase Extraction of Electronic Speckle Pattern Interferometry” all contribute to this field to varying degrees.

9. AI-Based Image Processing, Understanding, Recognition, Compression, and Reconstruction

Generative AI expands the boundaries of creative design, neural compression technology saves 40% of the bit rate [17], and 3D reconstruction technology empowers digital twins. In future, it is necessary to develop a diffusion model-driven reconstruction framework and promote the lightweight Transformer architecture to reduce computational costs [18].

AI-driven full-flow image processing technology integrates compression, analysis, and generation, and the following articles in this Special Issue provide technical support in this area. “Domain-Aware Adaptive Logarithmic Transformation” proposes a method called AdaLogT to solve the problem of oversaturation and detail loss caused by ignoring the differences in image properties in traditional tone mapping. A sub-domain objective function is constructed by the parameter p: for the luminance domain algorithm, the exposure bias and histogram skewness are jointly optimized, and the optimal p-value is solved using the trilateration method; for the gradient domain algorithm, the texture information is maximized based on the exponential mean and local variance; and the DNN algorithm fuses the luminance-gradient domain features to balance the global and local information.

Taken together, the 15 articles mentioned above, “A Residual Network with Efficient Transformer for Lightweight Image Super-Resolution”, “Pixel-Level Degradation for Text Image Super-Resolution and Recognition”, “A Multi-Stage Adaptive Copy-Paste Data Augmentation Algorithm Based on Model Training Preferences”, “Quality of Life Prediction on Walking Scenes Using Deep Neural Networks and Performance Improvement Using Knowledge Distillation”, “X-ray Security Inspection Image Dangerous Goods Detection Algorithm Based on Improved YOLOv4”, “E-ReInForMIF Routing Algorithm Based on Energy Selection and Erasure Code Tolerance Machine”, “Improved Reconstruction Algorithm of Wireless Sensor Network Based on BFGSQuasi-Newton Method”, “Research on Spectrum Prediction Technology Based on B-LTF”, “Tone Mapping Method Based on the Least Squares Method”, “Three-Stage Tone Mapping Algorithm”, “Multi-Task Learning for Scene Text Image Super-Resolution with Multiple Transformers”, “Research on Retinex Algorithm Combining with Attention Mechanism for Image Enhancement”, “A Coverage Hole Patching Algorithm for Heterogeneous Wireless Sensor Networks”, “Modulation Recognition of Digital Signal Using Graph Feature and Improved K-Means”, and “Deep Learning in the Phase Extraction of Electronic Speckle Pattern Interferometry”, provide a complete solution from data preprocessing to application.