Multi-scale Retinex Research Articles

Precision Ice Detection on Power Transmission Lines: A Novel Approach with Multi-Scale Retinex and Advanced Morphological Edge Detection Monitoring

Line icings on the power transmission lines are dangerous risks that may lead to situations like structural damage or power outages. The current techniques used for identifying ice have certain drawbacks, particularly when used in complex environments. This paper aims to detect lines on the top and bottom in PTLI with low illumination and complex backgrounds. The proposed method integrates multistage image processing techniques, including image enhancement, filtering, thresholding, object isolation, edge detection, and line identification. A binocular camera is used to capture images of PTLI. The effectiveness of the method is evaluated through a series of metrics, including accuracy, sensitivity, specificity, and precision, and compared with existing methods. It is observed that the proposed method significantly outperforms the existing methods of ice detection and thickness measurement. This paper uses average accuracy of detection and isolation of ice formations under various conditions at a percentage of 98.35, sensitivity at 91.63%, specificity at 99.42%, and precision of 96.03%. Furthermore, the accuracy of the ice thickness based on the thickness measurements is shown with a much smaller RMSE of 1.20 mm, MAE of 1.10 mm, and R-squared of 0.95. The proposed scheme for ice detection provides a more accurate and reliable method for monitoring ice formation on power transmission lines.

An Enhanced Multiscale Retinex, Oriented FAST and Rotated BRIEF (ORB), and Scale-Invariant Feature Transform (SIFT) Pipeline for Robust Key Point Matching in 3D Monitoring of Power Transmission Line Icing with Binocular Vision

Power transmission line icing (PTLI) poses significant threats to the reliability and safety of electrical power systems, particularly in cold regions. Accumulation of ice on power lines can lead to severe consequences, such as line breaks, tower collapses, and widespread power outages, resulting in economic losses and infrastructure damage. This study proposes an enhanced image processing pipeline to accurately detect and match key points in PTLI images for 3D monitoring of ice thickness using binocular vision. The pipeline integrates established techniques such as multiscale retinex (MSR), oriented FAST and rotated BRIEF (ORB) and scale-invariant feature transform (SIFT) algorithms, further refined with m-estimator sample consensus (MAGSAC)-based random sampling consensus (RANSAC) optimization. The image processing steps include automatic cropping, image enhancement, feature detection, and robust key point matching, all designed to operate in challenging environments with poor lighting and noise. Experiments demonstrate that the proposed method significantly improves key point matching accuracy and computational efficiency, reducing processing time to make it suitable for real-time applications. The effectiveness of the pipeline is validated through 3D ice thickness measurements, with results showing high precision and low error rates, making it a valuable tool for monitoring power transmission lines in harsh conditions.

Research on efficient matching method of coal gangue recognition image and sorting image

When the coal gangue sorting robot sorts coal gangue, the position of the target coal gangue will change due to belt slippage, deviation, and speed fluctuations of the belt conveyor. This will cause the robotic to fail in grasping or miss grasping. We have developed a solution to this problem: the IMSSP-Net two-stage network gangue image fast matching method. This method will reacquire the target gangue position information and improve the robot’s grasping precision and efficiency. In the first stage, we use SuperPoint to guarantee the scene adaptability and credibility of feature point extraction. We have enhanced Superpoint’s ability to detect feature points further by using the improved Multi-scale Retinex with Color Restoration enhancement algorithm. In the second stage, we introduce SuperGlue for feature matching to improve the robustness of the matching network. We eliminated erroneous feature matching point pairs and improved the accuracy of image matching by adopting the PROSAC algorithm. We conducted image matching comparison experiments under different object distances, scales, rotation angles, and complex conditions. The experimental platform adopts the double-manipulator truss-type coal gangue sorting robot independently developed by the team. The matching precision, recall, and matching time of the method are 98.2%, 98.3%, and 84.6ms, respectively. The method can meet the requirements of efficient and accurate matching between coal gangue recognition images and sorting images.

Adaptive classification of artistic images using multi-scale convolutional neural networks

The current art image classification methods have low recall and accuracy rate issues . To improve the classification performance of art images, a new adaptive classification method is designed employing multi-scale convolutional neural networks (CNNs). Firstly, the multi-scale Retinex algorithm with color recovery is used to complete the enhancement processing of art images. Then the extreme pixel ratio is utilized to evaluate the image quality and obtain the art image that can be analyzed. Afterward, edge detection technology is implemented to extract the key features in the image and use them as initial values of the item to be trained in the classification model. Finally, a multi-scale convolutional neural network (CNN) is constructed by using extended convolutions, and the characteristics of each level network are set. The decision fusion method based on maximum output probability is employed to calculate different subclassifies’ probabilities and determine the final category of an input image to realize the art image adaptive classification. The experimental results show that the proposed method can effectively improve the recall rate and precision rate of art images and obtain reliable image classification results.

Application of End-to-End Perception Framework Based on Boosted DETR in UAV Inspection of Overhead Transmission Lines

As crucial predecessor tasks for fault detection and transmission line inspection, insulators, anti-vibration hammers, and arc sag detection are critical jobs. Due to the complexity of the high-voltage transmission line environment and other factors, target detection work on transmission lines remains challenging. A method for high-voltage transmission line inspection based on DETR (TLI-DETR) is proposed to detect insulators, anti-vibration hammers, and arc sag. This model achieves a better balance in terms of speed and accuracy than previous methods. Due to environmental interference such as mountainous forests, rivers, and lakes, this paper uses the Improved Multi-Scale Retinex with Color Restoration (IMSRCR) algorithm to make edge extraction more robust with less noise interference. Based on the TLI-DETR’s feature extraction network, we introduce the edge and semantic information by Momentum Comparison (MoCo) to boost the model’s feature extraction ability for small targets. The different shooting angles and distances of drones result in the target images taking up small proportions and impeding each other. Consequently, the statistical profiling of the area and aspect ratio of transmission line targets captured by UAV generate target query vectors with prior information to enable the model to adapt to the detection needs of transmission line targets more accurately and effectively improve the detection accuracy of small targets. The experimental results show that this method has excellent performance in high-voltage transmission line detection, achieving up to 91.65% accuracy and a 55FPS detection speed, which provides a technical basis for the online detection of transmission line targets.

Research on Defect Detection Method of Fusion Reactor Vacuum Chamber Based on Photometric Stereo Vision.

This paper addresses image enhancement and 3D reconstruction techniques for dim scenes inside the vacuum chamber of a nuclear fusion reactor. First, an improved multi-scale Retinex low-light image enhancement algorithm with adaptive weights is designed. It can recover image detail information that is not visible in low-light environments, maintaining image clarity and contrast for easy observation. Second, according to the actual needs of target plate defect detection and 3D reconstruction inside the vacuum chamber, a defect reconstruction algorithm based on photometric stereo vision is proposed. To optimize the position of the light source, a light source illumination profile simulation system is designed in this paper to provide an optimized light array for crack detection inside vacuum chambers without the need for extensive experimental testing. Finally, a robotic platform mounted with a binocular stereo-vision camera is constructed and image enhancement and defect reconstruction experiments are performed separately. The results show that the above method can broaden the gray level of low-illumination images and improve the brightness value and contrast. The maximum depth error is less than 24.0% and the maximum width error is less than 15.3%, which achieves the goal of detecting and reconstructing the defects inside the vacuum chamber.

Optimasi Deteksi Tumor Otak Menggunakan Adaptive Multiscale Retinex dan YOLOV10 Pada Citra Digital

The incidence of brain tumors in Indonesia continues to rise annually, affecting both adults and children. Therefore, an effective detection system is required to differentiate MRI images showing the presence of brain tumors from those that do not. This distinction is crucial in the analysis and diagnosis of medical images. This study aims to optimize brain tumor detection in digital images by combining the Adaptive Multiscale Retinex (AMSR) method with the YOLOv10 algorithm. AMSR enhances the quality and contrast of brain images, making critical details more visible. The method adjusts the scale and intensity of image lighting to address uneven illumination and improve the visibility of brain structures. YOLOv10 is recognized for its speed and accuracy in real-time object detection. This algorithm employs deep learning techniques to identify and classify objects with high precision on enhanced brain image datasets. The model's performance was evaluated using metrics such as accuracy, sensitivity, specificity, and computation time. Results indicate that the combination of AMSR and YOLOv10 improves the accuracy and efficiency of brain tumor detection compared to conventional methods. The YOLOv10 model achieved a detection accuracy of 92%. This study provides significant contributions to early brain tumor detection technology, with important implications for the healthcare sector.

A novel multiscale cGAN approach for enhanced salient object detection in single haze images

In computer vision, image dehazing is a low-level task that employs algorithms to analyze and remove haze from images, resulting in haze-free visuals. The aim of Salient Object Detection (SOD) is to locate the most visually prominent areas in images. However, most SOD techniques applied to visible images struggle in complex scenarios characterized by similarities between the foreground and background, cluttered backgrounds, adverse weather conditions, and low lighting. Identifying objects in hazy images is challenging due to the degradation of visibility caused by atmospheric conditions, leading to diminished visibility and reduced contrast. This paper introduces an innovative approach called Dehaze-SOD, a unique integrated model that addresses two vital tasks: dehazing and salient object detection. The key novelty of Dehaze-SOD lies in its dual functionality, seamlessly integrating dehazing and salient object identification into a unified framework. This is achieved using a conditional Generative Adversarial Network (cGAN) comprising two distinct subnetworks: one for image dehazing and another for salient object detection. The first module, designed with residual blocks, Dark Channel Prior (DCP), total variation, and the multiscale Retinex algorithm, processes the input hazy images. The second module employs an enhanced EfficientNet architecture with added attention mechanisms and pixel-wise refinement to further improve the dehazing process. The outputs from these subnetworks are combined to produce dehazed images, which are then fed into our proposed encoder–decoder framework for salient object detection. The cGAN is trained with two modules working together: the generator aims to produce haze-free images, whereas the discriminator distinguishes between the generated haze-free images and real haze-free images. Dehaze-SOD demonstrates superior performance compared to state-of-the-art dehazing methods in terms of color fidelity, visibility enhancement, and haze removal. The proposed method effectively produces high-quality, haze-free images from various hazy inputs and accurately detects salient objects within them. This makes Dehaze-SOD a promising tool for improving salient object detection in challenging hazy conditions. The effectiveness of our approach has been validated using benchmark evaluation metrics such as mean absolute error (MAE), peak signal-to-noise ratio (PSNR), and structural similarity index measure (SSIM).

RESEARCH ON RETINEX ENHANCEMENT METHOD FOR VASCULAR MEDICAL IMAGES

To improve the feature extraction performance in vascular medical images, a multi-scale Retinex medical image enhancement algorithm based on a four-step method is proposed. This method is based on the multi-scale Retinex enhancement method, which divides the entire enhancement process into four steps: The first step is to reduce the overall complexity of the enhancement process through block processing; second, perform Retinex enhancement in each sub-block image; the third step is to optimize the connection area of each sub-block; and the fourth step is to highlight detailed features through gradient filtering. During the experiment, three different images were enhanced. The experimental results show that the proposed method can be used in enhancing the vascular features in medical images and be extended to other application fields. This method can effectively suppress the blurring and halo of the original image, while achieving enhancement effects and better protecting detailed features. From the four sets of indicators, the proposed method has shown more significant improvement than both reference methods. Regarding information entropy, the doubles have increased by a maximum of 96.2% and a minimum of 30.3%.

A novel and convenient lying cow identification method based on YOLOX and CowbodyNet: A study with applications in a barn

The lying time of cows is a key indicator of their health and comfort. The ability to automatically recognize the lying posture of cows while simultaneously realizing individual cow identification can play an important role in improving cow welfare, increasing milk yield, detecting cow diseases in a timely manner and enabling precision dairy farming management. In this paper, a method of individual identification for lying dairy cows in a barn based on YOLOX and a feature extraction network named CowbodyNet is proposed. When new cows join the herd, there is no need to collect a large number of images to retrain the model. It is very convenient to collect several images of newly added cows lying and store them in the database. First, the low-light images collected at night are enhanced by the multiscale retinex with chromaticity preservation (MSRCP) algorithm to improve the image quality. Then, the YOLOX target detection algorithm is applied to detect and segment cows in the lying posture. Following this, the segmented images of lying cows are input into CowbodyNet to generate feature vectors, which are used to construct a feature vector database. Subsequently, the Euclidean distances between the feature vector of a cow to be identified and the feature vectors in the database are calculated to determine the identification result. The proposed method achieves 94.43% lying cow identification accuracy on a data set containing top-view images of 72 cows. Finally, the individual cow detection and identification model is successfully deployed on the Jetson Xavier NX embedded platform. The results demonstrate the effectiveness and practicability of the proposed cow identification method. This study provides effective technical support for the application of identifying individual lying cows.

Research on the training method of special strength quality of competitive taekwondo based on multi-scale Retinex algorithm under the background of “Internet+”

Research on the training method of special strength quality of competitive taekwondo based on multi-scale Retinex algorithm under the background of “Internet+”

Extraction method of Yuan blue and white porcelain pattern based on multi-scale Retinex and histogram multi-peak threshold segmentation

Aiming at the problem of "crystallization" on the surface of Yuan blue and white ceramics, which causes reflections and loss of image texture, an image processing method is proposed to repair the image texture information. A multi-scale Retinex pre-processing method is proposed to enhance the contrast between the pattern and the background. A color factor is introduced to prevent color distortion. A weighted average function is constructed to enhance image details and improve texture information. The Yuan blue and white pattern can be effectively segmented from the background using a combination of multi-peak thresholding for segmentation and other techniques. The experimental results demonstrate that, in comparison to other algorithms, the multi-scale Retinex and histogram multi-peak threshold coupled segmentation method proposed in this paper exhibits the highest F1-score of 0.03067 and an accuracy of 92.67% in cross-evaluation with other algorithms. This indicates that the overall performance of the algorithm is the best. The proposed method has the potential to inform the protection of cultural relics.

Single-Shot Direct Transmission Terahertz Imaging Based on Intense Broadband Terahertz Radiation.

There are numerous applications of terahertz (THz) imaging in many fields. However, current THz imaging is generally based on scanning technique due to the limited intensity of the THz sources. Thus, it takes a long time to obtain a frame image of the target and cannot meet the requirement of fast THz imaging. Here, we demonstrate a single-shot direct THz imaging strategy based on a broadband intense THz source with a frequency range of 0.1~23 THz and a THz camera with a frequency response range of 1~7 THz. This THz source was generated from the laser-plasma interaction, with its central frequency at ~12 THz. The frame rate of this imaging system was 8.5 frames per second. The imaging resolution reached 146.2 μm. With this imaging system, a single-shot THz image for a target object with a size of more than 7 cm was routinely obtained, showing a potential application for fast THz imaging. Furthermore, we proposed and tested an image enhancement algorithm based on an improved dark channel prior (DCP) theory and multi-scale retinex (MSR) theory to optimize the image brightness, contrast, entropy and peak signal-to-noise ratio (PSNR).

Advanced optimization-based weighted features for ensemble deep learning smart occupancy detection network for road traffic parking

In day-to-day activities, the advanced technology like Internet of Things (IoT) emerges to improve the lifestyle of people. In metropolitan cities, real-time parking is the long-lasting problem that we face in our daily life activities. Urban parking regulation gained more attention because of its capability to diminish energy consumption, congested traffic, and manifestation. The parking space detection for vehicles in real-time is a crucial role for on-street parking control models where the data is delivered to the drivers through internet with the help of Global Positioning System (GPS). Hence, the network congestion gets increased, where the existing model does not performed well in the parking space effectively. This research paper recommends a novel strategy for detecting occupancy in road traffic parking by IoT and deep networks to overcome these difficulties. At first, the required information is gathered from the standard datasets as CNRPark + EXT for further processing. After the data collection process, the preprocessing phase is executed, where the data cleaning and data transformation approaches are done with a multi-scale retinex network. The pre-processing technique is effectively performed to reduce background noise. Further, the feature extraction is progressed through the Residual Attention Network, where the relevant features are extracted from the pre-processed data. Subsequently, the optimal features and weights are selected by the novel Advanced Pelican Optimization Algorithm (APOA) to get the optimal weighted feature selection. This optimal weighted feature is further given to the Ensemble Deep Networks (EDN) by integrating the Deep Conditional Random Field (DCRF), and Extreme Deep Learning (EDL) model. In final process, the detection of occupancy in road traffic parking is determined by averaging both classifiers outcomes. Finally, the results are compared with numerous optimization algorithms and classifiers to ensure the developed system's efficacy. Throughout the validation, the developed model outperforms with an accuracy as 96.6, precision as 92.9 and Mathew's Correlation Coefficient (MCC) as 92.3. Thus, the developed model shows better performance than the existing traditional approaches.

A Multi-Step Image Pre-Enhancement Strategy for a Fish Feeding Behavior Analysis Using Efficientnet

To enhance the accuracy of lightweight CNN classification models in analyzing fish feeding behavior, this paper addresses the image quality issues caused by external environmental factors and lighting conditions, such as low contrast and uneven illumination, by proposing a Multi-step Image Pre-enhancement Strategy (MIPS). This strategy includes three critical steps: initially, images undergo a preliminary processing using the Multi-Scale Retinex with Color Restoration (MSRCR) algorithm, effectively reducing the impact of water surface reflections and enhancing the visual effect of the images; secondly, the Multi-Metric-Driven Contrast Limited Adaptive Histogram Equalization (mdc) technique is applied to further improve image contrast, especially in areas of low contrast, by adjusting the local contrast levels to enhance the clarity of the image details; finally, Unsharp Masking (UM) technology is employed to sharpen the images, emphasizing their edges to increase the clarity of the image details, thereby significantly improving the overall image quality. Experimental results on a custom dataset have confirmed that this pre-enhancement strategy significantly boosts the accuracy of various CNN-based classification models, particularly for lightweight CNN models, and drastically reduces the time required for model training compared to the use of advanced ResNet models. This research provides an effective technical route for improving the accuracy and efficiency of an image-based analysis of fish feeding behavior in complex environments.

Vehicle detection in varied weather conditions using enhanced deep YOLO with complex wavelet

Traffic congestion is prevalent in many major and medium-sized cities throughout different countries in contemporary society. In traffic images, various multi-sized vehicles are tightly clustered together and obstructed from one another. Identifying vehicles in such instances is crucial for urban traffic surveillance, safety monitoring, and legal concerns but it also presents major challenges. The remarkable detection accuracy and efficiency of deep learning-based systems have led to their recent and extensive use in vehicle identification. There are significant advanced YOLO models with different backbone architectures and frameworks developed for vehicle detection. Yet, the performance of YOLO variants are facing the challenges of handling false detection against occluded and densely sophisticated scenarios. The proposed model is developed to address such types of limitations, for example; dynamic illumination, noisy images, and scale sensitivity to improve the vehicle detection rate in different traffic scenarios and varying weather conditions. The proposed study employs an improved YOLOv4 to identify moving vehicles in different lighting conditions including daylight, cloudy, rainy, and night. For hybridization, three techniques are utilized such as the Multiscale Retinex, Dual tree complex wavelet transform (DTCWT), and Pulse Coupled Neural Networks (PCNN). The DTCWT is employed for multiscale decomposition and to denoise the complex high frequency subband information, then the denoised subbands are reconstructed into a denoised image. The Multiscale retinex is utilized to reduce the halo artifacts on high-contrast edges and maintain the balance with dynamic range compression and color reproduction. The synchronizing pulse burst property of PCNN is used to detect the isolated noisy pixels and modify the detected noisy pixels. From the results it is worth noting that the developed model surpasses state-of-the-art methods in sunny, night, cloudy, and rainy modes. The proposed method using the DTCWT technique can detect the vehicles with mAP of 91.09% and 35FPS.

A Multi-Scale Fusion Strategy for Side Scan Sonar Image Correction to Improve Low Contrast and Noise Interference

Side scan sonar images have great application prospects in underwater surveys, target detection, and engineering activities. However, the acquired sonar images exhibit low illumination, scattered noise, distorted outlines, and unclear edge textures due to the complicated undersea environment and intrinsic device flaws. Hence, this paper proposes a multi-scale fusion strategy for side scan sonar (SSS) image correction to improve the low contrast and noise interference. Initially, an SSS image was decomposed into low and high frequency sub-bands via the non-subsampled shearlet transform (NSST). Then, modified multi-scale retinex (MMSR) was employed to enhance the contrast of the low frequency sub-band. Next, sparse dictionary learning (SDL) was utilized to eliminate high frequency noise. Finally, the process of NSST reconstruction was completed by fusing the emerging low and high frequency sub-band images to generate a new sonar image. The experimental results demonstrate that the target features, underwater terrain, and edge contours could be clearly displayed in the image corrected by the multi-scale fusion strategy when compared to eight correction techniques: BPDHE, MSRCR, NPE, ALTM, LIME, FE, WT, and TVRLRA. Effective control was achieved over the speckle noise of the sonar image. Furthermore, the AG, STD, and E values illustrated the delicacy and contrast of the corrected images processed by the proposed strategy. The PSNR value revealed that the proposed strategy outperformed the advanced TVRLRA technology in terms of filtering performance by at least 8.8%. It can provide sonar imagery that is appropriate for various circumstances.

Unsupervised learning-based image recovery and diagnosis method for blade attachment of marine current turbine

Marine current energy is a clean and renewable resource that is an available energy source with future power generation potential. However, complex subsea conditions can cause blade failures in marine current turbines (MCTs); therefore, the fault diagnosis of MCTs is essential to ensure their good operation. Image-based diagnosis is a nondestructive approach. To improve the efficiency of using human capital, this study uses the unsupervised learning network GANomaly trained only with normal samples to diagnose the leaf attachment of MCTs. Marine biofouling is a lengthy process, and according to the literature, the blades were contaminated by a 1.1-mm-thick layer of lithium lubricating grease, therefore, this study uses ropes to simulate this attachment. First, we address the problem of blurred underwater images collected using a multiscale retinex enhancement algorithm with color recovery to form a new dataset. GANomaly is modified to diagnose MCT blades with different attachment degrees. The experimental results show that the accuracy of the enhanced dataset is improved by 1.75 %, and the accuracy of the improved GANomaly model reaches 91.23 %, thereby enhancing the efficiency of the fault diagnosis of MCTs.

Magnetic Flux Leakage Defect Identification Method for Small-Diameter Pipeline Elbow Based on the Improved YOLOv5

Abstract The elbow is an important constituent of oil and gas pipeline systems and plays a key role in changing the direction of pipelines. Corrosion defects pose a significant risk to the safe operation of elbows. Magnetic flux leakage (MFL) detection has been developed as a suitable technique for identifying defects in pipelines. To address the distortion of elbow defect signals in the images arising from variations in the liftoff value of the leakage detector, this paper proposed an image identification method based on an improved YOLOv5 network. The differences in defect images are simulated by analyzing the liftoff value of the magnetization unit. A defect image enhancement method of multiscale retinex with color restoration fusion homomorphic filtering (MSRCR-HF) is employed to enhance the features of defective MFL signal images. To further improve the accuracy of the model, the YOLOv5 network is optimized by integrating the convolutional block attention module (CBAM) and the space-to-depth-nonstrided convolution (SPD-Conv) module. The results show that the proposed image enhancement method effectively accentuates the features of defect images. Moreover, the suggested image identification method exhibits superior accuracy in identification. The mean average precision (mAP) values for the original image set and the enhanced image set are 85.0% and 91.4%, respectively. Consequently, the proposed method is shown to be highly viable for the automatic identification of MFL defects in small-diameter pipe elbows.

DETECTION OF PARKINSON'S DISEASE VIA CLIFFORD GRADIENT-BASED RECURRENT NEURAL NETWORK USING MULTI-DIMENSIONAL DATA

Disease prediction is a vital step in the early diagnosis of many diseases in the overpopulated modern world. The prediction has gotten simpler due to advancements in various machine learning (ML) techniques. However, the complexity of the model and the choice of the best machine-learning method for the given dataset significantly impact its accuracy. Globally, there are many datasets, but their unstructured nature prevents them from being used in any useful way. To extract anything valuable for use in the actual world, various strategies are therefore accessible. To evaluate the model, accuracy now serves as a key metric. This research proposes a novel Cliff-PD to detect Parkinson's disease using a Clifford gradient RNN classifier with MRI and EEG signals. Initially, the MRI images are denoised using multi-scale Retinex (MSR), and the EEG signal is denoised using Stationary Wavelet Transform filters to reduce the noise artifacts. Then, the Clifford Gradient RNN is employed to classify the normal, Non-specific white matter hyperintensity and global brain atrophy using MRI images. Furthermore, the Clifford Gradient RNN is employed to classify the normal, generalized background slowing using an EEG signal. The performance of the proposed Cliff-PD model achieved an accuracy of 99.18 %. Compared with SVMs, AlexNet's, and CROWD autoencoder, the accuracy range is improved overall by 5.38 %, 10.36 %, and 3.2 %, respectively.