Single image dehazing is very important in intelligent vision systems. Since the dark channel prior (DCP) is invalid in bright areas such as the sky part of the image and will cause the recovered image to suffer severe distortion in the bright area. Therefore, we propose a novel dehazing method based on transmission map segmentation and prior knowledge. First, we divide the hazy input into bright areas and non-bright areas, then estimate the transmission map via DCP in the non-bright area, and propose a transmission map compensation function for correction in the bright area. Then we fuse the DCP and the bright channel prior (BCP) to accurately estimate the atmospheric light, and finally restore the clear image according to the physical model. Experiments show that our method well solves the DCP distortion problem in bright regions of images and is competitive with state-of-the-art methods.
Magnetic induction tomography (MIT) is an emerging imaging technology holding significant promise in the field of cerebral hemorrhage monitoring. The commonly employed imaging method in MIT is time-difference imaging. However, this approach relies on magnetic field signals preceding cerebral hemorrhage, which are often challenging to obtain. Multiple bioelectrical impedance information with different frequencies is added to this study on the basis of single-frequency information, and the collected signals with different frequencies are identified to obtain the magnetic field signal generated by single-layer heterogeneous tissue. The Stacked Autoencoder (SAE) neural network algorithm is used to reconstruct the images of head multi-layer tissues. Both numerical simulation and phantom experiments are carried out. The results indicate that the relative error of the multi-frequency SAE reconstruction is only 7.82%, outperforming traditional algorithms. Moreover, under a noise level of 40 dB, the anti-interference capability of the MIT algorithm based on frequency identification and SAE is superior to traditional algorithms. This research explores a novel approach for the dynamic monitoring of cerebral hemorrhage and demonstrates the potential advantages of MIT in non-invasive monitoring.
The format of multi-view plus depth (MVD) is introduced in 3D-HEVC for improving coding efficiency. However, as the number of viewpoints increases, the computational complexity increases dramatically. To overcome this issue, a method based on RD-cost Bayesian Decision (RDBD) for 3D-HEVC fast depth map inter coding is proposed in this paper, including coding unit (CU) early termination and SKIP/DIS decision. First, an offline training model with the minimum risk Bayesian decision rule is used to predict optimal CU size in depth map. Then, a minimum error rate Bayesian decision rule is proposed to determine whether SKIP or DIS is the best mode and ignores the rate distortion optimization (RDO) process of other modes. Experimental results show that, with the proposed algorithm, 45.7% depth map encoding time saving is achieved compared to the original 3D-HEVC, and outperforms state-of-the-art fast inter coding methods for 3D-HEVC.
Model-based approaches to imaging, such as specialized image enhancements in astronomy, facilitate explanations of relationships between observed inputs and computed outputs. These models may be expressed with extended matrix-vector (EMV) algebra, especially when they involve only scalars, vectors, and matrices, and with n-mode or index notations, when they involve multidimensional arrays, also called numeric tensors or, simply, tensors. Although this paper features an example, inspired by exoplanet imaging, that employs tensors to reveal (inverse) 2D fast Fourier transforms in an image enhancement model, the work is actually about the tensor algebra and software, or tensor frameworks, available for model-based imaging. The paper proposes a Ricci-notation tensor (RT) framework, comprising a dual-variant index notation, with Einstein summation convention, and codesigned object-oriented software, called the RTToolbox for MATLAB. Extensions to Ricci notation offer novel representations for entrywise, pagewise, and broadcasting operations popular in EMV frameworks for imaging. Complementing the EMV algebra computable with MATLAB, the RTToolbox demonstrates programmatic and computational efficiency via careful design of numeric tensor and dual-variant index classes. Compared to its closest competitor, also a numeric tensor framework that uses index notation, the RT framework enables superior ways to model imaging problems and, thereby, to develop solutions.
In response to the current challenges in the detection of solder ball defects in ball grid array (BGA) packaged chips, which include slow detection speed, low efficiency, and poor accuracy, our research has addressed these issues. We have designed an algorithm for detecting solder ball defects in BGA-packaged chips by leveraging the specific characteristics of these defects and harnessing the advantages of deep learning. Building upon the YOLOv8 network model, we have made adaptive improvements to enhance the algorithm. First, we have introduced an adaptive weighted downsampling method to boost detection accuracy and make the model more lightweight. Second, to improve the extraction of image features, we have proposed an efficient multi-scale convolution method. Finally, to enhance convergence speed and regression accuracy, we have replaced the traditional Complete Intersection over Union loss function with Minimum Points Distance Intersection over Union (MPDIoU). Through a series of controlled experiments, our enhanced model has shown significant improvements when compared to the original network. Specifically, we have achieved a 1.7% increase in mean average precision, a 1.5% boost in precision, a 0.9% increase in recall, a reduction of 4.3 M parameters, and a decrease of 0.4 G floating-point operations per second. In comparative experiments, our algorithm has demonstrated superior overall performance when compared to other networks, thereby effectively achieving the goal of solder ball defect detection.
Aerial work vehicles are widely used in a variety of aerial work scenarios. In these vehicles, energy-saving is realized by telescopic motion control. Due to telescopic boom and person-mounting, their work safety requirements are very high. The anti-rollover protection function is one of their important active safety technologies, which is mainly realized through electrical, electronic, and programmable systems. At present, there is a lack of accurate and dynamic quantitative evaluation methods for functional safety level, and this problem can be solved by a quantitative evaluation method based on the Markov model. In this paper, the evaluation method for a safety system with a heterogeneous redundant structure based on the Markov model is first studied. Through this method, a Markov model is established for the active safety system of an aerial work vehicle, and its safety parameters such as safety and reliability are calculated through numerical simulation. In this way, the designed safety system is evaluated to meet the design requirements of functional safety, and the changing rules of the relevant parameters of the safety system are dynamically understood through Markov simulation. Finally, by this method, the probability of dangerous failure of a complex safety system can be simulated and calculated so as to accurately and quantitatively evaluate its safety parameters.
The existence of noise components will affect the quality of image super-resolution reconstruction, so an image super-resolution reconstruction method based on improved cyclic generative countermeasure network is proposed. Using the image denoising regularization method, the internal noise of the original image is removed. By introducing the twin attention mechanism into the cyclic generative countermeasure network, an improved cyclic generative countermeasure network is obtained. In the improved cyclic generative countermeasure network, the twin attention model is used to extract the denoised image features, and the super-resolution reconstruction image is generated with the generator. The network discriminator is used to identify whether the reconstructed image is a real image, and the output identification result is a real image to obtain the relevant image super-resolution reconstruction results. Experiments show that this method can effectively denoise the original image and extract image features, and can also reconstruct the image with high quality to improve image resolution and clarity. At different image magnifications, the structural similarity of image reconstruction using this method is high. The subjective opinion score of the image super-resolution reconstruction result of this method is high, with a maximum score of 4.8. The perception index and Fréchet inception distance are both small, with values of 21.65 and 14.84, respectively. The image super-resolution reconstruction effect is good.
Steel surface defect detection in industrial quality control has always been a challenging objective detection task in the field of computer vision. However, unlike other detection problems, some surface defects on steel are relatively small compared to the entire inspection object, leading to less prominent defect features in the detection. To address these issues, we propose a YOLOv5-based steel defect detection method enhanced with multi-scale feature extraction and contextual augmentation (MSCA-YOLO). Specifically, adopting the YOLOv5 as the backbone network, we first add the C3-RFE to expand the receptive. Then, we design a neck network structure via combining multi-scale guided upsampling, which effectively enhances the model’s ability to handle multi-scale features and improves the model’s feature extraction ability for small defects. Finally, we propose a context mechanism that provides the model with a deeper context analysis capability, offering richer up-and-down information. The experiments on the NEU-DET dataset show that MSCA-YOLO achieves a mean Average Precision of 0.645 while maintaining rapid detection, especially at an Intersection over Union threshold of 0.5. It also exhibits substantial improvements in Precision compared to YOLOv5 across six defect types: Crazing (18.5% increase), Inclusion (1.2% increase), Patches (1.9% increase), Pitted_Surface (7.8% increase), Rolled-in_Scale (8.9% increase), and Scratches (6.5% increase). This achievement marks the efficiency and reliability of MSCA-YOLO in automated steel surface defect detection, providing a new solution for real-time inspection of steel surface defects.
Skin tumors have become one of the most common diseases worldwide. Usually, benign skin tumors are not harmful to human health, but malignant skin tumors are highly likely to develop into skin cancer, which is life-threatening. Dermoscopy is currently the most effective method of diagnosing skin tumors. However, the complexity of skin tumor cells makes doctors’ diagnoses subject to error. Therefore, it is essential to use computers for assisted diagnosis, thereby improving the diagnostic accuracy of skin tumors. In this paper, we propose Deep-skin, a model for dermoscopic image classification, which is based on both attention mechanism and ensemble learning. Considering the characteristics of dermoscopic images, we suggest embedding different attention mechanisms on top of Inception-V3 to obtain more potential features. We then improve the classification performance by late fusion of the different models. To demonstrate the effectiveness of Deep-skin, we conduct experiments and evaluations on the publicly available dataset Skin Cancer: Malignant vs. Benign and compare the performance of Deep-skin with other classification models. The experimental results indicate that Deep-skin performs well on the dataset in comparison to other models, achieving a maximum accuracy of 87.8%. In the future, we intend to investigate better classification models for automatic diagnosis of skin tumors. Such models can potentially assist physicians and patients in clinical settings.
Link to dataset: Skin Cancer: Malignant vs. Benign (kaggle.com)