Gamma Correction Function Research Articles

Enhancement Method Based on Multi-Strategy Improved Pelican Optimization Algorithm and Application to Low-Illumination Forest Canopy Images

Enhancement is a crucial step in the field of image processing, as it significantly improves image analysis and understanding. One of the most commonly used methods for image contrast enhancement is the incomplete beta function (IBF). However, the key challenge lies in determining the optimal parameters for the IBF. This paper introduces a multi-strategy improved pelican optimization algorithm (MIPOA) to address the low-illumination color image enhancement problem. The MIPOA algorithm utilizes a nonlinear decreasing coefficient to boost the exploration ability and convergence speed, whereas the Hardy–Weinberg principle compensates for the unsound exploitation mechanism. Additionally, the diversity variation operation improves the ability of the algorithm to escape local optimal solutions. The performance of the proposed MIPOA algorithm was evaluated using a benchmark function and was found to outperform five variant algorithms in extensive comparisons. To further harness the potential of the MIPOA algorithm, the authors propose a low-light forest canopy image enhancement method based on the MIPOA algorithm. The MIPOA algorithm searches for the optimal parameters of the IBF, leading to fast contrast enhancement of the image. The segmented gamma correction function is designed to enhance the brightness of the low-light forest canopy images. In determining the optimal parameters of IBF, the MIPOA algorithm demonstrates superior performance compared to other intelligent algorithms in the feature similarity index (FSIM), entropy, and contrast improvement index (CII) of 75%, 58.33%, and 75%, respectively. The proposed MIPOA-based enhancement method achieves a moderate pixel mean and surpasses the conventional enhancement method with an average gradient of 91.67%. The experimental results indicate that the MIPOA effectively addresses the limitations of low optimization accuracy in IBF parameters, and the enhancement method based on the MIPOA provides a more efficacious approach for enhancing low-light forest canopy images.

The Retinex enhancement algorithm for low‐light intensity image based on improved illumination map

AbstractTaken in low‐light intensity conditions, image with low brightness affects processing precision. In this article, the Gamma Function based on the brightness average and weighted fusion method according to gray entropy is proposed, which is combined with the improved Retinex algorithm. First, the maximum values of R, G, and B channels in original image are extracted to generate the primary illumination map. Second, the illumination map is optimized and adjusted via the Gamma correction function based on the average brightness value. Finally, the illumination map and detail layer are fused by a weighted fusion algorithm of gray entropy to obtain the reflection map. Reflection maps are used as enhancement. The algorithm proposed in this article can improve the brightness and maintain light distribution in the original image with higher precision and less color distortion.

Underwater image enhancement via color correction and multi-feature image fusion

Abstract The light attenuation underwater causes the actual underwater images to suffer from color cast, low contrast, and weak illumination. To address these issues, an effective fusion-based method is proposed, which realizes color correction, brightness adjustment, contrast, and detail enhancement of underwater images. Concretely, we first design an adaptive color correction method via dominant color channel judgment and lower color channel compensation. Then, we detect the brightness of each input image and propose a gamma correction function based on the gradient of the cumulative histogram to adjust the brightness of the low-light images. Subsequently, global histogram stretching and adaptive fractional differentiation techniques are employed to process the brightness-adjusted image, and then the global contrast-enhanced version and detail-enhanced version are generated respectively. To integrate the advantages of both versions, a channel fusion method based on the Lab color space is used to fuse the luminance and color of the two versions separately. The experimental results demonstrate the effectiveness of the proposed method in improving the color and illumination of underwater images, as well as enhancing the clarity of images. Moreover, the testing results on multiple datasets validate the excellent stability of this method.

LIASM-NRID: Constructing an atmospheric scattering model for low-light conditions and dehazing nighttime road images

Dehazing of nighttime road images holds significant practical relevance for autonomous driving and intelligent transportation systems operating in nocturnal conditions. To address the limitations of traditional atmospheric scattering models in accurately describing the imaging process in low illumination environments such as nighttime, this study constructs a low illumination environment atmospheric scattering model. Based on this newly constructed model, we propose an algorithm specifically designed for dehazing road images at nighttime. The proposed model incorporates an incident light attenuation factor, an additive noise factor, and a global compensation factor related to the scene's depth. The proposed nighttime road image dehazing algorithm firstly suppresses the interference of additive noise in the original image using wavelet decomposition and soft thresholding operation. Then, a transmittance-solving method containing the primary structural information of the image is designed based on the color attenuation linear model, and the solved transmittance is refined based on the interval gradient image texture structure method. Consequently, the global light map estimation process is formulated as an optimization problem to ensure the accuracy of the atmospheric light value calculation. In the final stages, a novel RGB color equalization method is introduced to address the issue of non-uniform incident light color bias in the dehazed images. Additionally, to eliminate the halo problem in the dehazed images, the adaptive histogram equalization algorithm is modified by incorporating a normalized gamma correction function. The experimental results show that the proposed nighttime road image dehazing algorithm exhibits robust performance and generality across various nighttime scenes. Compared with the representative algorithms for nighttime image dehazing, it can get more excellent dehazing results and has more outstanding performance in all numerical evaluation indexes.

An optimal system for increasing the contrast resolution qualities of histopathology images in the wavelet domain

AbstractIn the field of histopathology, it has been observed that most images are of poor quality as a result of inadequate luminance and contrast. The first step in identifying and analyzing them is to enhance their quality while maintaining their inherent characteristics. This paper presents a novel method for enhancing histopathology images' important features, brightness preservation, and contrast by utilizing the adaptive intensity transformation gamma function in the wavelet domain. The RGB histopathological color image is first converted to the HSV color model. The V component is then stretched in order to compensate for any color distortion. In the proposed scheme, the stretched V component is split into four sub‐bands using discrete wavelet transform (DWT). Next, it determines the optimal value of the gamma correction function in the DWT lower sub‐band. A correction factor is applied using the singular value decomposition (SVD) technique to determine the optimal gamma function value using the nature‐inspired particle swarm optimization method (NIPSO). The corrected LL sub‐band is then applied to the inverse DWT (IDWT) together with the other unprocessed sub‐bands. In addition, the contrast of each feature is enhanced by employing an adaptive histogram equalization model that is contrast‐limited. The proposed method was evaluated through experiments and validations on a variety of histopathology images retrieved from multiple databases. Several quantitative studies have demonstrated that the proposed method outperforms existing enhancement techniques in terms of entropy, edge preservation index, contrast ratio, and Universal Image Quality Index (UIQI). This proposed technique increases contrast, while preserving the inherent characteristics of the original image, resulting in better quality histopathology images that can be used in disease diagnosis and inspection.

A saturation channel detection method for surface defects of silicon nitride bearing rollers based on adaptive gamma correction-edge threshold segmentation coupling algorithm

For the problem that the surface defects in Si3N4 ceramic bearing rollers are fuzzy, it is difficult for traditional single algorithms to achieve accurate localization and detection of surface defects. An adaptive gamma correction and edge threshold segmentation coupling algorithm is designed to accurately detect surface defects in the intensity channel of Si3N4 ceramic bearing rollers. To preserve the surface defects information to the greatest extent, the HSI intensity channel image characteristics of a single surface defects in Si3N4 ceramic bearing roller is analyzed. To obtain the best γ value of a single image, an adaptive gamma correction function equation is constructed. Analyze the frequency-domain characteristics of the three-dimensional grayscale curve of surface defects, and the grayscale gradient of surface defects is extended. Use the non-maximum value suppression model to suppress noise and background information, and perform sharpening enhancement and threshold segmentation on defect edge information. The mean PSNR of the surface defects images of the Si3N4 ceramic bearing roller is 28.9161 dB. The mean information entropy is 3.7572 bit. The accuracy of the detection result is 96.3%. The surface defects intensity channel detection method effectively improves the accuracy and efficiency of locating and detecting surface defects of Si3N4 ceramic bearing rollers.

Optimization of BBR Congestion Control Algorithm Based on Pacing Gain Model.

In 2016, Google proposed a congestion control algorithm based on bottleneck bandwidth and round-trip propagation time (BBR). The BBR congestion control algorithm measures the network bottleneck bandwidth and minimum delay in real-time to calculate the bandwidth delay product (BDP) and then adjusts the transmission rate to maximize throughput and minimize latency. However, relevant research reveals that BBR still has issues such as RTT unfairness, high packet loss rate, and deep buffer performance degradation. This article focuses on its most prominent RTT fairness issue as a starting point for optimization research. Using fluid models to describe the data transmission process in BBR congestion control, a fairness optimization strategy based on pacing gain is proposed. Triangular functions, inverse proportional functions, and gamma correction functions are analyzed and selected to construct the pacing gain model, forming three different adjustment functions for adaptive adjustment of the transmission rate. Simulation and real experiments show that the three optimization algorithms significantly improve the fairness and network transmission performance of the original BBR algorithm. In particular, the optimization algorithm that employs the gamma correction function as the gain model exhibits the best stability.

A novel scaled-gamma-tanh (SGT) activation function in 3D CNN applied for MRI classification

Activation functions in the neural network are responsible for ‘firing’ the nodes in it. In a deep neural network they ‘activate’ the features to reduce feature redundancy and learn the complex pattern by adding non-linearity in the network to learn task-specific goals. In this paper, we propose a simple and interesting activation function based on the combination of scaled gamma correction and hyperbolic tangent function, which we call Scaled Gamma Tanh (SGT) activation. The proposed activation function is applied in two steps, first is the calculation of gamma version as y = f(x) = axα for x < 0 and y = f(x) = bxβ for x ≥ 0, second is obtaining the squashed value as z = tanh(y). The variables a and b are user-defined constant values whereas alpha and beta are channel-based learnable parameters. We analyzed the behavior of the proposed SGT activation function against other popular activation functions like ReLU, Leaky-ReLU, and tanh along with their role to confront vanishing/exploding gradient problems. For this, we implemented the SGT activation functions in a 3D Convolutional neural network (CNN) for the classification of magnetic resonance imaging (MRIs). More importantly to support our proposed idea we have presented a thorough analysis via histogram of inputs and outputs in activation layers along with weights/bias plot and t-SNE (t-Distributed Stochastic Neighbor Embedding) projection of fully connected layer for the trained CNN models. Our results in MRI classification show SGT outperforms standard ReLU and tanh activation in all cases i.e., final validation accuracy, final validation loss, test accuracy, Cohen’s kappa score, and Precision.

Low-Light Image Enhancement Method Based on Retinex Theory by Improving Illumination Map

Recently, low-light image enhancement has attracted much attention. However, some problems still exist. For instance, sometimes dark regions are not fully improved, but bright regions near the light source or auxiliary light source are overexposed. To address these problems, a retinex based method that strengthens the illumination map is proposed, which utilizes a brightness enhancement function (BEF) that is a weighted sum of the Sigmoid function cascading by Gamma correction (GC) and Sine function, and an improved adaptive contrast enhancement (IACE) to enhance the estimated illumination map through multi-scale fusion. Specifically, firstly, the illumination map is obtained according to retinex theory via the weighted sum method, which considers neighborhood information. Then, the Gaussian Laplacian pyramid is used to fuse two input images that are derived by BEF and IACE, so that it can improve brightness and contrast of the illuminance component acquired above. Finally, the adjusted illuminance map is multiplied by the reflection map to obtain the enhanced image according to the retinex theory. Extensive experiments show that our method has better results in subjective vision and quantitative index evaluation compared with other state-of-the-art methods.

Quaternion Screened Poisson Equation for Low-Light Image Enhancement

Image enhancement is a technique to enhance the illumination of dark images while keeping the reality and the naturalness of the enhanced images at the same time. For color images, most methods tackle different color channels in a separate way, which overlooks the connection between the color channels. Therefore, in this paper, we consider a quaternion-based model to reserve the color connectivity, which integrates the color information of a pixel by a quaternion number. Moreover, we propose a regularizer based on the gamma-correction function and incorporate it into a screened Poisson equation&#x00A0;for the image enhancement task. The uniqueness and existence of the solution of the proposed model are analyzed. The numerical results also prove the superiority of our scheme for color image enhancement.

Adaptive Image Enhancement Method Based on Gamma Correction

To address the problems of local detail loss and sharpness degradation in traditional image enhancement algorithms under low illumination conditions, an image enhancement algorithm based on the Weber-Fechner law is proposed. The adaptive gamma correction function and the adaptive contrast enhancement function are used to reduce the noise interference of the original image, the RGB mode of the original image is converted to HSV mode to improve the overall visual comfort of the image, the classical adaptive correction algorithm is optimised to separate the luminance components into blocks and obtain two images; Finally, the image fusion technique is used to extract the details from the two images and synthesise the final image. The images enhanced are clearer, brighter and more natural than the classical algorithms.

Improvement of RTT Fairness Problem in BBR Congestion Control Algorithm by Gamma Correction

Google proposed the bottleneck bandwidth and round-trip propagation time (BBR), which is a new congestion control algorithm. BBR creates a network path model by measuring the available bottleneck bandwidth and the minimum round-trip time (RTT) to maximize delivery rate and minimize latency. However, some studies have shown that there are serious RTT fairness problems in the BBR algorithm. The flow with longer RTT will consume more bandwidth and the flows with shorter RTT will be severely squeezed or even starved to death. Moreover, these studies pointed out that even small RTT differences will lead to the throughput of BBR flows being unfair. In order to solve the problem of RTT fairness, an improved algorithm BBR-gamma correction (BBR-GC) is proposed. BBR-GC algorithm takes RTT as feedback information, and then uses the gamma correction function to fit the adaptive pacing gain. This approach can make different RTT flows compete for bandwidth more fairly, thus alleviating the RTT fairness issue. The simulation results of Network Simulator 3 (NS3) show that that BBR-GC algorithm cannot only ensure the channel utilization, but also alleviate the RTT fairness problem of BBR flow in different periods. Through the BBR-GC algorithm, RTT fairness is improved by 50% and the retransmission rate is reduced by more than 26%, compared with that of the original BBR in different buffer sizes.

Computational Complexity Reduction of Neural Networks of Brain Tumor Image Segmentation by Introducing Fermi-Dirac Correction Functions.

Nowadays, deep learning methods with high structural complexity and flexibility inevitably lean on the computational capability of the hardware. A platform with high-performance GPUs and large amounts of memory could support neural networks having large numbers of layers and kernels. However, naively pursuing high-cost hardware would probably drag the technical development of deep learning methods. In the article, we thus establish a new preprocessing method to reduce the computational complexity of the neural networks. Inspired by the band theory of solids in physics, we map the image space into a noninteraction physical system isomorphically and then treat image voxels as particle-like clusters. Then, we reconstruct the Fermi–Dirac distribution to be a correction function for the normalization of the voxel intensity and as a filter of insignificant cluster components. The filtered clusters at the circumstance can delineate the morphological heterogeneity of the image voxels. We used the BraTS 2019 datasets and the dimensional fusion U-net for the algorithmic validation, and the proposed Fermi–Dirac correction function exhibited comparable performance to other employed preprocessing methods. By comparing to the conventional z-score normalization function and the Gamma correction function, the proposed algorithm can save at least 38% of computational time cost under a low-cost hardware architecture. Even though the correction function of global histogram equalization has the lowest computational time among the employed correction functions, the proposed Fermi–Dirac correction function exhibits better capabilities of image augmentation and segmentation.

Detection of Malfunctioning Photovoltaic Modules Based on Machine Learning Algorithms

In recent years, with the rise of environmental awareness worldwide, the number of solar power plants has significantly increased. However, the maintenance of solar power plants is not an easy job, especially the detection of malfunctioning photovoltaic (PV) cells in large-scale or remote power plants. Therefore, finding these cells and replacing them in time before severe events occur is increasingly important. In this paper, we propose a hybrid scheme with three embedded learning methods to enhance the detection of malfunctioning PV modules with validated efficiencies. For the first method, we combine the improved gamma correction function (preprocess) with a convolutional neural network (CNN). Infrared (IR) thermographic images of solar modules are used to train the abovementioned improved algorithm. For the second method, we train a CNN model using the IR temperatures of PV modules with the preprocessing of a threshold function. A compression procedure is then designed to cut the time-consuming preprocesses. The third method is to replace the CNN with the eXtreme Gradient Boosting (XGBoost) algorithm and the selected temperature statistics. The experimental results show that all three methods can be implemented with high detection accuracy and low time consumption, and furthermore, the hybrid scheme provides an even better accuracy.

Normalised gamma transformation‐based contrast‐limited adaptive histogram equalisation with colour correction for sand–dust image enhancement

Images captured in the sand-dust weather often suffer from serious colour cast and poor contrast, and this has serious implications for outdoor computer vision systems. To address these problems, a normalised gamma transformation-based contrast-limited adaptive histogram equalisation (CLAHE) with colour correction in Lab colour space for sand-dust image enhancement is proposed in this study. This method consists of image contrast enhancement and image colour correction. To avoid producing new colour deviation, the input sand-dust images are first transformed from red, green, and blue colour space into Lab colour space. Then, the contrast of the lightness component (L channel) of the sand-dust image is enhanced using CLAHE. To avoid unbalanced contrast, as well as to reduce the overincreased brightness caused by CLAHE, a normalised gamma correction function is introduced to CLAHE. After that, the a and b chromatic components are recovered by a grey-world-based colour correction method. Experiments on real sand-dust images demonstrate that the proposed method can obtain the highest percentage of new visible edges for all testing images. The contrast restoration exhibits good colour fidelity and proper brightness.

Smartphone-Based Diagnosis of Parasitic Infections With Colorimetric Assays in Centrifuge Tubes

A smartphone-based platform for the diagnosis of parasitic infections has been developed, tested and validated. The system is capable of making automatic and accurate analysis of millimetric colorimetric arrays in centrifuge collection tubes, which are well established tools used in clinical analysis. To that end, an Android-based software application has been developed, making use of the smartphone rear camera, enabling precise image processing of the colorimetric spot arrays. A low-cost plastic accessory has been developed using 3D-printing to provide controlled illumination, fixed sample positioning and cell phone attachment. The platform was then tested repeatedly for its size detection, edge blurriness and colour detection capabilities. A minimum spot radius of $175~\mu \text{m}$ is detectable when using the developed app, with a tolerance of 15%, corresponding to 0.25% of the area where the spot array is printed. Spot edge definition has been studied up to 40% of blurriness, resulting in a low average percentage error of 1.24%. Colour detection follows the well-known Gamma correction function. Finally, the whole platform was tested and validated using real DNA to analyse for accurate discrimination of Trypanosomatid species, which are responsible for devastating diseases in humans and livestock. The smartphone-based platform can be further extended to other clinical analysis. Its simplicity and reliable performance mean it can be used in remote, limited-resource settings by relatively unskilled technicians/nurses, where diagnostic laboratories are sparsely distributed. The results can however be sent easily via the smartphone to medical experts as well as government health agencies.

A New Adaptive Gamma Correction Based Algorithm Using DWT-SVD for Non-Contrast CT Image Enhancement.

The performances of medical image processing techniques, in particular CT scans, are usually affected by poor contrast quality introduced by some medical imaging devices. This suggests the use of contrast enhancement methods as a solution to adjust the intensity distribution of the dark image. In this paper, an advanced adaptive and simple algorithm for dark medical image enhancement is proposed. This approach is principally based on adaptive gamma correction using discrete wavelet transform with singular-value decomposition (DWT-SVD). In a first step, the technique decomposes the input medical image into four frequency sub-bands by using DWT and then estimates the singular-value matrix of the low-low (LL) sub-band image. In a second step, an enhanced LL component is generated using an adequate correction factor and inverse singular value decomposition (SVD). In a third step, for an additional improvement of LL component, obtained LL sub-band image from SVD enhancement stage is classified into two main classes (low contrast and moderate contrast classes) based on their statistical information and therefore processed using an adaptive dynamic gamma correction function. In fact, an adaptive gamma correction factor is calculated for each image according to its class. Finally, the obtained LL sub-band image undergoes inverse DWT together with the unprocessed low-high (LH), high-low (HL), and high-high (HH) sub-bands for enhanced image generation. Different types of non-contrast CT medical images are considered for performance evaluation of the proposed contrast enhancement algorithm based on adaptive gamma correction using DWT-SVD (DWT-SVD-AGC). Results show that our proposed algorithm performs better than other state-of-the-art techniques.

Experimental Demonstration of a 1024-QAM Optical Camera Communication System

In this letter, we experimentally demonstrate a 1024-quadrature-amplitude-modulation (QAM) optical camera communications (OCC) system using a dual light-emitting diode (LED) and a commercial digital single-lens reflex camera. An undersampled QAM subcarrier modulation (UQAMSM) is proposed to support a high-efficiency and non-flickering OCC system. Owing to the built-in gamma correction function of the camera, pre- and post-compensation techniques are successfully applied to compensate for the non-linear impairment. A dedicated framing structure is also designed to support the proposed UQAMSM and compensation techniques. The experimental results show that this system is able to achieve a data rate of 500 b/s using a dual LED and a 50 ft/s commercial camera over a transmission span of 1.5 m, which is suitable for the transmission and reception of location-based information.

An innovative technique for contrast enhancement of computed tomography images using normalized gamma-corrected contrast-limited adaptive histogram equalization

Abstract Image contrast is an essential visual feature that determines whether an image is of good quality. In computed tomography (CT), captured images tend to be low contrast, which is a prevalent artifact that reduces the image quality and hampers the process of extracting its useful information. A common tactic to process such artifact is by using histogram-based techniques. However, although these techniques may improve the contrast for different grayscale imaging applications, the results are mostly unacceptable for CT images due to the presentation of various faults, noise amplification, excess brightness, and imperfect contrast. Therefore, an ameliorated version of the contrast-limited adaptive histogram equalization (CLAHE) is introduced in this article to provide a good brightness with decent contrast for CT images. The novel modification to the aforesaid technique is done by adding an initial phase of a normalized gamma correction function that helps in adjusting the gamma of the processed image to avoid the common errors of the basic CLAHE of the excess brightness and imperfect contrast it produces. The newly developed technique is tested with synthetic and real-degraded low-contrast CT images, in which it highly contributed in producing better quality results. Moreover, a low intricacy technique for contrast enhancement is proposed, and its performance is also exhibited against various versions of histogram-based enhancement technique using three advanced image quality assessment metrics of Universal Image Quality Index (UIQI), Structural Similarity Index (SSIM), and Feature Similarity Index (FSIM). Finally, the proposed technique provided acceptable results with no visible artifacts and outperformed all the comparable techniques.

A New PAPR Reduction Scheme for OFDM Systems Based on Gamma Correction

Orthogonal frequency division multiplexing (OFDM) is perhaps the most spectrally efficient method discovered so far for communication systems and yet have an excellent immunity against multipath fading and inter-symbol interference. One of the major drawbacks of OFDM systems is their high peak-to-average power ratio (PAPR), which degrades system performance when nonlinear high power amplifiers (HPA) are employed. In this article, a new companding technique based on gamma correction (GC) function is proposed and analyzed. Through extensive computer simulations, it is shown that the proposed technique outperforms the previously suggested A-law and μ-law companding methods for PAPR reduction. A thorough investigation of GCC companding is presented in terms of computational complexity (CC), complementary cumulative distribution function (CCDF), power spectral density (PSD), and bit-error-rate (BER). It is also shown that the proposed method is independent of modulation schemes and can be applied to any number of subcarriers.