Gamma Transformation Research Articles

The real-time shadow detection of the PV module by computer vision based on histogram matching and gamma transformation method

Solar energy plays an important role in renewable energy generation, with the advantages of low pollution, easy installation, and relatively easy access. However, photovoltaic (PV) modules are susceptible to cause localized shading from external factors such as leaves in the canopy, surrounding buildings, etc., which would affect power generation efficiency and even pose safety risks. Existing methods cannot perform well in real-time conditions. This paper proposes a real-time shading monitoring method for the PV module based on computer vision. The gamma transform and histogram matching were adopted to enhance key features and adjust the global gamut strength distribution in the image of the PV module; then the gray-level slicing method finished the segmentation to detect the shadow from the video. All processing can be realized in the real-time monitor camera and the detection results can be displayed on the HMI in PC with high efficiency and low cost. According to tests in the practical complex environment, the method can have enough detection performance and high real-time performance with an accuracy of 0.98, and the F0.5 and F2 values are 0.87 and 0.85, respectively. The metrics of the proposed method are higher than those of the existing Canny detection method, the Random Forest detection method, and the CNN detection method. In addition, the average time required by the proposed method to process a frame is 0.721 s. In addition, the average time required by the method to process an image frame is 0.721 s, which has good real-time performance.

Qin Seal Script Character Recognition with Fuzzy and Incomplete Information

The dependable and efficient identification of Qin seal script characters is pivotal in the discovery, preservation, and inheritance of the distinctive cultural values embodied by these artifacts. This paper uses image histograms of oriented gradients (HOG) features and an SVM model to discuss a character recognition model for identifying partial and blurred Qin seal script characters. The model achieves accurate recognition on a small, imbalanced dataset. Firstly, a dataset of Qin seal script image samples is established, and Gaussian filtering is employed to remove image noise. Subsequently, the gamma transformation algorithm adjusts the image brightness and enhances the contrast between font structures and image backgrounds. After a series of preprocessing operations, the oriented gradient histograms (HOG) features are extracted from the images. During model training, different weights are assigned to classes with varying sample quantities to address the issue of class imbalance and improve the model's classification accuracy. Results show that the model achieves an accuracy of 95.30%. This research can help historians quickly identify and extract the text content on newly discovered Qin slip cultural relics, shortening the cycle of building a historical database.

A multiscale carotid plaque detection method based on two-stage analysis

To develop a method for accurate identification of multiscale carotid plaques in ultrasound images. We proposed a two-stage carotid plaque detection method based on deep convolutional neural network (SM-YOLO).A series of algorithms such as median filtering, histogram equalization, and Gamma transformation were used to preprocess the dataset to improve image quality. In the first stage of the model construction, a candidate plaque set was built based on the YOLOX_l target detection network, using multiscale image training and multiscale image prediction strategies to accommodate carotid artery plaques of different shapes and sizes. In the second stage, the Histogram of Oriented Gradient (HOG) features and Local Binary Pattern (LBP) features were extracted and fused, and a Support Vector Machine (SVM) classifier was used to screen the candidate plaque set to obtain the final detection results. This model was compared quantitatively and visually with several target detection models (YOLOX_l, SSD, EfficientDet, YOLOV5_l, Faster R-CNN). SM-YOLO achieved a recall of 89.44%, an accuracy of 90.96%, a F1-Score of 90.19%, and an AP of 92.70% on the test set, outperforming other models in all performance indicators and visual effects. The constructed model had a much shorter detection time than the Faster R-CNN model (only one third of that of the latter), thus meeting the requirements of real-time detection. The proposed carotid artery plaque detection method has good performance for accurate identification of carotid plaques in ultrasound images.

VELIE: A Vehicle-Based Efficient Low-Light Image Enhancement Method for Intelligent Vehicles.

In Advanced Driving Assistance Systems (ADAS), Automated Driving Systems (ADS), and Driver Assistance Systems (DAS), RGB camera sensors are extensively utilized for object detection, semantic segmentation, and object tracking. Despite their popularity due to low costs, RGB cameras exhibit weak robustness in complex environments, particularly underperforming in low-light conditions, which raises a significant concern. To address these challenges, multi-sensor fusion systems or specialized low-light cameras have been proposed, but their high costs render them unsuitable for widespread deployment. On the other hand, improvements in post-processing algorithms offer a more economical and effective solution. However, current research in low-light image enhancement still shows substantial gaps in detail enhancement on nighttime driving datasets and is characterized by high deployment costs, failing to achieve real-time inference and edge deployment. Therefore, this paper leverages the Swin Vision Transformer combined with a gamma transformation integrated U-Net for the decoupled enhancement of initial low-light inputs, proposing a deep learning enhancement network named Vehicle-based Efficient Low-light Image Enhancement (VELIE). VELIE achieves state-of-the-art performance on various driving datasets with a processing time of only 0.19 s, significantly enhancing high-dimensional environmental perception tasks in low-light conditions.

Hydroclimatic Impact Assessment Using the SWAT Model in India—State of the Art Review

The Soil and Water Assessment Tool (SWAT) has been widely employed to assist with decision making and management planning for assessing and mitigating the impact of climate change. This model has gained popularity in India as the country is facing increasing water issues under projected climate changes. However, a systematic review of the literature that discusses the applicability of the model, the impact assessment process, and the interpretation of the modeling results in India remains lacking. We synthesized and reviewed 110 recent SWAT modeling studies (published from 2012 to 2022) that evaluated the impact of future climate change on water resources in India to identify research gaps that need to be filled to advance SWAT modeling practices for impact assessments. The review revealed that the SWAT model provided acceptable accuracy statistics in most (90%) of the studies reviewed. Half of these studies identified the base curve number (CN2) as the parameter to which the water balance is the most sensitive; thus, this parameter was included in the calibration process. The accuracy of SWAT modeling is closely associated with the accuracy of the weather data fed to the model. However, extreme events, including heavy storm events and severe droughts, were rarely considered in climate change impact assessments using the SWAT model. Most studies downscaled global-scale climate modeling outputs to local weather stations when applying the SWAT model using various methods, such as the delta change method, multiple linear regression method, gamma–gamma transformation, fitted histogram equalization, and quantile mapping. Further, most studies investigated the performance of the SWAT model before applying the model to quantify the future hydrological consequences of projected climate change in a subsequent scenario analysis. This review suggests that further evaluations of the characteristics and development processes of existing climate data products are needed to effectively consider extreme events in impact assessments. In addition, this review finds that climate change impact modeling has been improved with advances in climate projection preparation, including ensemble averaging, bias correction, and downscaling methods. This regional review of current SWAT modeling practices for climate change impact assessments can be used to create reliable future hydrological projections in India.

GRP-YOLOv5: An Improved Bearing Defect Detection Algorithm Based on YOLOv5.

Currently, most chemical transmission equipment relies on bearings to support rotating shafts and to transmit power. However, bearing defects can lead to a series of failures in the equipment, resulting in reduced production efficiency. To prevent such occurrences, this paper proposes an improved bearing defect detection algorithm based on YOLOv5. Firstly, to mitigate the influence of the similarity between bearing defects and non-defective regions on the detection performance, gamma transformation is introduced in the preprocessing stage of the model to adjust the image's grayscale and contrast. Secondly, to better capture the details and semantic information of the defects, this approach incorporates the ResC2Net model with a residual-like structure during the feature-extraction stage, enabling more nonlinear transformations and channel interaction operations so as to enhance the model's perception and representation capabilities of the defect targets. Additionally, PConv convolution is added in the feature fusion part to increase the network depth and better capture the detailed information of defects while maintaining time complexity. The experimental results demonstrate that the GRP-YOLOv5 model achieves a mAP@0.5 of 93.5%, a mAP@0.5:0.95 of 52.7%, and has a model size of 25 MB. Compared to other experimental models, GRP-YOLOv5 exhibits excellent performance in bearing defect detection accuracy. However, the model's FPS (frames per second) performance is not satisfactory. Despite its small size of 25 MB, the processing speed is relatively slow, which may have some impact on real-time or high-throughput applications. This limitation should be considered in future research and in the optimization efforts to improve the overall performance of the model.

IRNet: An Improved Zero-Shot Retinex Network for Low-Light Image Enhancement

Inadequate illumination often causes severe image degradation, such as noise and artifacts. These types of images do not meet the requirements of advanced visual tasks, so low-light image enhancement is currently a flourishing and challenging research topic. To alleviate the problem of low brightness and low contrast, this paper proposes an improved zero-shot Retinex network, named IRNet, which is composed of two parts: a Decom-Net and an Enhance-Net. The Decom-Net is designed to decompose the raw input into two maps, i.e., illuminance and reflection. Afterwards, the subsequent Enhance-Net takes the decomposed illuminance component as its input, enhances the image brightness and features through gamma transformation and a convolutional network, and fuses the enhanced illumination and reflection maps together to obtain the final enhanced result. Due to the use of zero-shot learning, no previous training is required. IRNet depends on the internal optimization of each individual input image, and the network weights are updated by iteratively minimizing a series of designed loss functions, in which noise reduction loss and color constancy loss are introduced to reduce noise and relieve color distortion during the image enhancement process. Experiments conducted on public datasets and the presented practical applications demonstrate that our method outperforms other counterparts in terms of both visual perception and objective metrics.

A New Extended Alpha-power Transformation of Burr-Generalized Gamma with an Application to Income

A New Extended Alpha-power Transformation of Burr-Generalized Gamma with an Application to Income

Letter Detecting the One-Shot Dummy Attack on the Power Industrial Control Processes With an Unsupervised Data-Driven Approach

Dear Editor, Dummy attack (DA), a deep stealthy but impactful data integrity attack on power industrial control processes, is recently recognized as hiding the corrupted measurements in normal measurements. In this letter, targeting a more practical case, we aim to detect the one-shot DA, with the purpose of revealing the DA once it is launched. Specifically, we first formulate an optimization problem to generate one-shot DAs. Then, an unsupervised data-driven approach based on a modified local outlier factor (MLOF) is proposed to detect them. To improve the detection performance, the measurements are preprocessed with the gamma transformation and the power patterns are extracted from historical data and integrated into the MLOF algorithm. Finally, extensive experiments are conducted to evaluate the performance of the proposed approach with real-world load data.

An Improved Encoder-Decoder CNN with Region-Based Filtering for Vibrant Colorization

Colorization is the practice of adding appropriate chromatic values to monochrome photographs or videos. A real-valued luminance image can be mapped to a three-dimensional color image. However, it is a severely ill-defined problem and not has a single solution. In this paper, an encoder-decoder Convolutional Neural Network (CNN) model is used for colorizing gray images where the encoder is a Densely Connected Convolutional Network (DenseNet) and the decoder is a conventional CNN. The DenseNet extracts image features from gray images and the conventional CNN outputs a * b * color channels. Due to a large number of desaturated color components compared to saturated color components in the training images, the saturated color components have a strong tendency towards desaturated color components in the predicted a * b * channel. To solve the problems, we rebalance the predicted a * b * color channel by smoothing every subregion individually using the average filter. 2 stage k-means clustering technique is applied to divide the subregions. Then we apply Gamma transformation in the entire a * b * channel to saturate the image. We compare our proposed method with several existing methods. From the experimental results, we see that our proposed method has made some notable improvements over the existing methods and color representation of gray-scale images by our proposed method is more plausible to visualize. Additionally, our suggested approach beats other approaches in terms of Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM) and Histogram.

Recurrent Residual Dual Attention Network for Airborne Laser Scanning Point Cloud Semantic Segmentation

Kernel point convolution (KPConv) can effectively represent the point features of point cloud data. However, KPConv-based methods just consider the local information of each point, which is very difficult to characterize the intrinsic properties of ALS point clouds for complex laser scanning conditions. Therefore, we rethink KPConv and propose a recurrent residual dual attention network (RRDAN) based on the encoder-decoder structure for the semantic segmentation of ALS point cloud data. In the encoder stage, we design an attention kernel point convolution (AKPConv) block by using a scaling factor of batch normalization to highlight the significant channel information. Then, we use the AKPConv block to develop a recurrent residual kernel attention (RRKA) module to iteratively aggregate the local neighborhood features. In the decoder stage, we design a global and local channel attention (GLCA) module with global connection and local 1D convolution to interact the global and local information after fusing the upsampled high-level representations and the skip-connected low-level features. In addition, to reduce the influence of the long-tail distribution of reflection intensity, we apply gamma transformation to correct the data as normal distribution. The proposed RRDAN can achieve diversified feature aggregation to implement the refined semantic segmentation of ALS point clouds. We evaluate our method on two ALS datasets (i.e., ISPRS and DCF2019) to demonstrate its performance compared to a few advanced methods. Code: https://github.com/SC-shendazt/RRDAN.

Tracing weak neuron fibers.

Precise reconstruction of neuronal arbors is important for circuitry mapping. Many auto-tracing algorithms have been developed toward full reconstruction. However, it is still challenging to trace the weak signals of neurite fibers that often correspond to axons. We proposed a method, named the NeuMiner, for tracing weak fibers by combining two strategies: an online sample mining strategy and a modified gamma transformation. NeuMiner improved the recall of weak signals (voxel values <20) by a large margin, from 5.1 to 27.8%. This is prominent for axons, which increased by 6.4 times, compared to 2.0 times for dendrites. Both strategies were shown to be beneficial for weak fiber recognition, and they reduced the average axonal spatial distances to gold standards by 46 and 13%, respectively. The improvement was observed on two prevalent automatic tracing algorithms and can be applied to any other tracers and image types. Source codes of NeuMiner are freely available on GitHub (https://github.com/crazylyf/neuronet/tree/semantic_fnm). Image visualization, preprocessing and tracing are conducted on the Vaa3D platform, which is accessible at the Vaa3D GitHub repository (https://github.com/Vaa3D). All training and testing images are cropped from high-resolution fMOST mouse brains downloaded from the Brain Image Library (https://www.brainimagelibrary.org/), and the corresponding gold standards are available at https://doi.brainimagelibrary.org/doi/10.35077/g.25. Supplementary data are available at Bioinformatics online.

Selective local binary pattern with convolutional neural network for facial expression recognition

&lt;span lang="EN-US"&gt;Variation in images in terms of head pose and illumination is a challenge in facial expression recognition. This research presents a hybrid approach that combines the conventional and deep learning, to improve facial expression recognition performance and aims to solve the challenge. We propose a selective local binary pattern (SLBP) method to obtain a more stable image representation fed to the learning process in convolutional neural network (CNN). In the preprocessing stage, we use adaptive gamma transformation to reduce illumination variability. The proposed SLBP selects the discriminant features in facial images with head pose variation using the median-based standard deviation of local binary pattern images. We experimented on the Karolinska directed emotional faces (KDEF) dataset containing thousands of images with variations in head pose and illumination and Japanese female facial expression (JAFFE) dataset containing seven facial expressions of Japanese females’ frontal faces. The experiments show that the proposed method is superior compared to the other related approaches with an accuracy of 92.21% on KDEF dataset and 94.28% on JAFFE dataset.&lt;/span&gt;

Image Illumination Adaptive Correction Algorithm Based on a Combined Model of Bottom-Hat and Improved Gamma Transformation

Image Illumination Adaptive Correction Algorithm Based on a Combined Model of Bottom-Hat and Improved Gamma Transformation

An improved U-net based retinal vessel image segmentation method

Diabetic retinopathy is not just the most common complication of diabetes but also the leading cause of adult blindness. Currently, doctors determine the cause of diabetic retinopathy primarily by diagnosing fundus images. Large-scale manual screening is difficult to achieve for retinal health screen. In this paper, we proposed an improved U-net network for segmenting retinal vessels. Firstly, due to the lack of retinal data, pre-processing of the raw data is required. The data processed by grayscale transformation, normalization, CLAHE, gamma transformation. Data augmentation can prevent overfitting in the training process. Secondly, the basic network structure model U-net is built, and the Bi-FPN network is fused based on U-net. Datasets from a public challenge are used to evaluate the performance of the proposed method, which is able to detect vessel SP of 0.8604, SE of 0.9767, ACC of 0.9651, and AUC of 0.9787.

Insulator defect detection for power grid based on light correction enhancement and YOLOv5 model

To guarantee the safety of the power grid system, it is essential to proceed reliable powerline inspection. Insulators are key devices in the powerlines. Their major function is to achieve mechanical fixing and electrical insulation, they play a key role in power lines. Insulators are deployed outdoors. Therefore, ensuring the safe operation of insulators is significant in the powerline inspection. Among all the inspection method, visual inspection is the key way. However, problems such as large changes in outdoor lighting have a strong impact on the accuracy of insulator detection. To overcome the shortcomings of uneven illumination, low contrast and poor details display in outdoor images, in this paper we introduces an image enhancement method based on illumination correction and compensation. First, the input data is converted from RGB color space to the HSV space, and three components, H, S and V, are obtained. The saturation component S is enhanced adaptively, and the brightness component V is processed by multi-scale gradient domain guided filter (MGDGF). Then the illumination component of the image is extracted, and corrected by two-dimensional adaptive Gamma transformation. The new brightness component is fused by Retinex based models. It helps to enhance the dark details and overall brightness of the image. This method not only solves the uneven illumination problem of the image, but also improves the contrast and details, while maintaining the original naturalness. Further, we introduce a real-time one step detection model based on YOLOv5, to detect the defect of the insulator. We evaluate the proposed method on an open public dataset. The evaluation results demonstrate that our proposed method can get very competitive results while maintaining real-time performance.

An efficient single image dehazing algorithm based on transmission map estimation with image fusion

Single image dehazing is always the focus of attention in the field of image processing. The major improvements in most optical scattering model based dehazing algorithms focused on the transmission map estimation since that the improper of transmission map estimation may cause over-high contrast or color distortion in the restored images. In this paper, we proposed an efficient single image dehazing algorithm in which the image fusion strategy is employed to adaptively improve the accuracy of transmission map estimation. Specifically, the transmission map obtained by the dark channel is corrected with Gamma transformation firstly. Then the original transmission map and its corrected version are fused with the weighting average image fusion algorithm to preserve the details in original transmission map. Moreover, the fused transmission map is filtered by guided filter to avoid the halo and block artifacts in the dehazed images. Finally, the dehazed images are reconstructed with the revised transmission map by optical scattering model. We test the proposed method on both natural and artifact haze images. The proposed method is compared with 8 state-of-the-art dehazing methods on three indexes include natural image quality evaluator (NIQE) index, structural similarity (SSIM), and peak signal-to-noise ratio (PSNR) indexes. Both subjective and objective evaluations demonstrate that the proposed method achieves competitive results.

A Deep Learning–Based Diagnosis Model Driven by Tuyere Images Big Data for Iron‐Making Blast Furnaces

Deep learning, applied to traditional industries, makes many challenges and previously impossible tasks turn practicable. However, progress is still limited in the steelmaking industry. This study aims to establish a deep convolutional neural network–based diagnosis model for blast furnaces (BF) driven by tuyere image big data, which is not effectively exploited but contains valuable information. The model is characterized by having the ability to learn value features of abnormal events hidden in images and acts as a high‐dimensional complex function of abnormal events correlated to image features. The implementation framework of the model includes two stages: offline training based on the labeled historical database and online real‐time diagnosis. Specifically, the image data are first de‐noised and enhanced via the median filter and Gamma transformation algorithms. Then, when the model is offline trained, a preferable clustering algorithm combined with the manual determination is applied to labeling image data. Finally, the built model is examined for accuracy and performance. It is shown in the results that the model has an accuracy of 98% and 88.58% for diagnosing results in training and validating datasets, respectively. Thereby, the established model may be applied to the online diagnosis of an industrial BF using real‐time images.

A two-way flexible generalized gamma transformation cure rate model.

We propose a two-way flexible cure rate model. The first flexibility is provided by considering a family of Box-Cox transformation cure models that include the commonly used cure models as special cases. The second flexibility is provided by proposing the wider class of generalized gamma distributions to model the associated lifetime. The advantage of this two-way flexibility is that it allows us to carry out tests of hypotheses to select an adequate cure model (within the family of Box-Cox transformation cure models) and a suitable lifetime distribution (within the wider class of generalized gamma distributions) that jointly provides the best fit to a given data. First, we study the maximum likelihood estimation of the generalized gamma Box-Cox transformation (GGBCT) model parameters. Then, we use the flexibility of our proposed model to carry out power studies to demonstrate the power of likelihood ratio test in rejecting mis-specified models. Furthermore, we study the bias and efficiency of the estimators of the cure rates under model mis-specification. Our findings strongly suggest the importance of selecting a correct lifetime distribution and a correct cure rate model, which can be achieved through the proposed two-way flexible model. Finally, we illustrate the applicability of our proposed model using a data from a breast cancer study and show that our model provides a better fit than the existing semiparametric Box-Cox transformation cure model with piecewise exponential approximation to the lifetime distribution.

Research on the Region-Growing and Segmentation Technology of Micro-Particle Microscopic Images Based on Color Features

Silkworm microparticle disease is a legal quarantine standard in the detection of silkworm disease all over the world. The current common detection method, the Pasteur manual microscopy method, has a low detection efficiency all over the world. The low efficiency of the current Pasteur manual microscopy detection method makes the application of machine vision technology to detect microparticle spores an important technology to advance silkworm disease research. For the problems of the low contrast, different illumination conditions and complex image background of microscopic images of the ellipsoidal symmetrical shape of silkworm microparticle spores collected in the detection solution, a region growth segmentation method based on microparticle color and grayscale information is proposed. In this method, the fuzzy contrast enhancement algorithm is used to enhance the color information of micro-particles and improve the discrimination between the micro-particles and background. In the HSV color space with stable color, the color information of micro-particles is extracted as seed points to eliminate the influence of light and reduce the interference of impurities to locate the distribution area of micro-particles accurately. Combined with the neighborhood gamma transformation, the highlight feature of the micro-particle target in the grayscale image is enhanced for region growing. Mea6nwhile, the accurate and complete micro-particle target is segmented from the complex background, which reduces the background impurity segmentation caused by a single feature in the complex background. In order to evaluate the segmentation performance, we calculate the IOU of the microparticle sample image segmented by this method with its corresponding true value image, and the experiments show that the combination of color and grayscale features using the region growth technique can accurately and completely segment the microparticle target in complex backgrounds with a segmentation accuracy IOU as high as 83.1%.