Color Image Research Articles

Classification of plant leaf diseases using deep neural networks in color and grayscale images

For a long time, plants have shown a crucial role in our life, manufacturing and industry. However, a large number of diseases have significantly affected to the production of plants. The detection and diagnosis of the diseases are necessary to improve the production of plants. In recent years, the automatic classification of plant diseases using artificial intelligence and computer vision have attracted a huge number of researches. The paper presents a classification method of plant leaf diseases using various Deep Neural Networks (e.g., Alexnet, Resnet-50, Densenet-121). The color features have significantly affected the classification accuracy. Therefore, we analyzed and compared the classification accuracy on two public datasets (Plant village leaf datasets) that consist of color and grayscale images. The method obtained the classification accuracy of 98.08% and 92% on color and grayscale images, respectively. The obtained results showed the effectiveness of the method. Based on the obtained results, the impact of color features to the classification accuracy of various Deep Neural Networks is analyzed in the paper. Moreover, the paper compares the performance of various optimization algorithms during the training process of deep neural networks to classify leaf diseases.

Low-Rank Tensor Function Representation for Multi-Dimensional Data Recovery.

Since higher-order tensors are naturally suitable for representing multi-dimensional data in real-world, e.g., color images and videos, low-rank tensor representation has become one of the emerging areas in machine learning and computer vision. However, classical low-rank tensor representations can solely represent multi-dimensional discrete data on meshgrid, which hinders their potential applicability in many scenarios beyond meshgrid. To break this barrier, we propose a low-rank tensor function representation (LRTFR) parameterized by multilayer perceptrons (MLPs), which can continuously represent data beyond meshgrid with powerful representation abilities. Specifically, the suggested tensor function, which maps an arbitrary coordinate to the corresponding value, can continuously represent data in an infinite real space. Parallel to discrete tensors, we develop two fundamental concepts for tensor functions, i.e., the tensor function rank and low-rank tensor function factorization, and utilize MLPs to paramterize factor functions of the tensor function factorization. We theoretically justify that both low-rank and smooth regularizations are harmoniously unified in LRTFR, which leads to high effectiveness and efficiency for data continuous representation. Extensive multi-dimensional data recovery applications arising from image processing (image inpainting and denoising), machine learning (hyperparameter optimization), and computer graphics (point cloud upsampling) substantiate the superiority and versatility of our method as compared with state-of-the-art methods. Especially, the experiments beyond the original meshgrid resolution (hyperparameter optimization) or even beyond meshgrid (point cloud upsampling) validate the favorable performances of our method for continuous representation.

Colour Image Enhancement Model of Retinal Fundus Image for Diabetic Retinopathy Recognition

Diabetic retinopathy (DR) features are typically identified through ophthalmologist eye examinations, but these images often facechallenges like low contrast, non-uniform illumination, and colour inconsistency, affecting the diagnosis accuracy. Therefore, this studyintroduces two novel techniques to improve image quality. One is applying colour image processing techniques to original retinalfundus images, overcoming existing algorithm limitations. Firstly, a new colour correction algorithm was proposed based on TunedBrightness Controlled Single-Scale Retinex (TBCSSR) named Fuzzy TBCSSR Histogram Matching (fTBCSSRhm) to address the issue of colour inconsistency in the dataset. Secondly, based on hybrid particle swarm optimisation-contrast stretch (HPSOCS), the hybridof TBCSSR and HPSOCS named eTBCSSR-HPSOCS algorithm is introduced to tackle the limitations of the standard Particle SwarmOptimisation (PSO) algorithm in HPSOCS, which is prone to local optima and exhibits low convergence rates. This technique combinesthe L-component of the LAB colour model with an enhanced velocity mechanism in PSO and contrast stretching (lavHPSOCS). Its goalis to fine-tune parameters automatically, reduce over-enhancement, avoid unwanted artefacts, and preserve intricate details. This approach improves optimisation by balancing exploration and exploitation and refining velocity control. The proposed algorithm underwent both qualitative and quantitative evaluations. Tests on 100 retinal fundus images from primary datasets were performed to benchmark the algorithm against three existing approaches. The results show that the qualitative performance of the proposed enhancement is more favourable to ophthalmologist specialists than other images. Quantitatively, eTBCSSR-HPSOCS outperforms others with the lowest mean squared error (MSE) of 42.72859, the highest peak signal-to-noise ratio (PSNR) of 32.768, and entropy of 0.977.

Virtual Canvas using OpenCV

Abstract: The main purpose of this project is to identify the human gestures according to that specific gesturesit will perform specific tasks. Project are going to use OpenCV library, Python programming language and also we will use colour detection and image segmentation techniques to achieve this. OpenCV is an open source computer vision library for performing various advanced image processing. In colour detection we can detect any colour in a given range of HSV colour space. The image segmentation is the process of labelling every pixel in an image, where each pixel shares the same certain characteristic. Python is use for developing this whole project. After processing is complete for those input gestures it will show output on screen according to that user inputs. The main objective of the project is that the “Virtual Canvas” and put before readers some useful information regarding our project. We have made sincere attempts and have tried to present this matter in precise and compact form, the language being as simple as possible. We are sure that the information contained this volume would certainly prove useful for better insight in the scope and the dimension of this project in this true prospective. The perseverance and deep involvement shown by our group members made this vague task look very interesting and simple. We have designed this software project completely from scratch and we have not incorporated any readymade material from the Internet or any other sources to make our project seemingly more attractive and meaningful

Optimizing Visual Sensor Node Image Compression for Resource-Constrained Environments

Abstract: An Approximate hybrid multiplier design is proposed to reduce the data-path width of lifting 2-D discrete wavelet transform (DWT) especially for wireless visual sensor node applications. The proposed multiplier design takes multiplier operand of size less by 3 bit and calculates full-width outputs with marginal loss of accuracy, which is significant. The proposed approximate 2-D DWT structure uses data-path DWT levels and produces reconstructed color images higher than the 70-dB peak-signal-to-noise ratio (PSNR). Compared with the existing fractional wavelet transform (FrWT)-based 2D DWT structures, the proposed structure has a smaller data-path width and free from overhead. Compared with the existing parallel structure

Analysis of a time-dependent memristor-based chaotic system and its application in image encryption

Abstract Compared with ordinary chaotic systems, memristor-based chaotic systems have more complex dynamic behaviors and are more suitable for image encryption algorithms. In this paper, a four-dimensional chaotic system is constructed by introducing a cubic nonlinear memristor into a three-dimensional chaotic system. Firstly, the dynamic characteristics of the constructed memristor-based chaotic system are analyzed in detail, and the simulation results show that the system has different attractors with different topological structures at different simulation times. Within a fixed simulation time, the system has 15 attractors with different topological structures under different parameter values, and there is a phenomenon of multiple stability in the system, indicating high complexity. Based on the above discoveries, a color image encryption algorithm including scrambling and diffusion is designed. Experimental results show that this algorithm can perfectly hide the information of the plaintext image, and the decrypted image is consistent with the plaintext image. Finally, the security of the algorithm is analyzed by using key space and so on. The analysis results indicate that the encryption algorithm designed in this paper can effectively resist external attacks and has high security.

Design of a Multimodal Detection System Tested on Tea Impurity Detection

A multimodal detection system with complementary capabilities for efficient detection was developed for impurity detection. The system consisted of a visible light camera, a multispectral camera, image correction and registration algorithms. It can obtain spectral features and color features at the same time and has higher spatial resolution than a single spectral camera. This system was applied to detect impurities in Pu’er tea to verify its high efficiency. The spectral and color features of each pixel in the images of Pu’er tea were obtained by this system and used for pixel classification. The experimental results showed that the accuracy of a support vector machine (SVM) model based on combined features was 93%, which was 7% higher than that based on spectral features only. By applying a median filtering algorithm and a contour detection algorithm to the label matrix extracted from pixel-classified images, except hair, eight impurities were detected successfully. Moreover, taking advantage of the high resolution of a visible light camera, small impurities could be clearly imaged. By comparing the segmented color image with the pixel-classified image, small impurities such as hair could be detected successfully. Finally, it was proved that the system could obtain multiple images to allow a more detailed and comprehensive understanding of the detected items and had an excellent ability to detect small impurities.

MH‐HMR: Human mesh recovery from monocular images via multi‐hypothesis learning

AbstractRecovering 3D human meshes from monocular images is an inherently ill‐posed and challenging task due to depth ambiguity, joint occlusion, and truncation. However, most existing approaches do not model such uncertainties, typically yielding a single reconstruction for one input. In contrast, the ambiguity of the reconstruction is embraced and the problem is considered as an inverse problem for which multiple feasible solutions exist. To address these issues, the authors propose a multi‐hypothesis approach, multi‐hypothesis human mesh recovery (MH‐HMR), to efficiently model the multi‐hypothesis representation and build strong relationships among the hypothetical features. Specifically, the task is decomposed into three stages: (1) generating a reasonable set of initial recovery results (i.e., multiple hypotheses) given a single colour image; (2) modelling intra‐hypothesis refinement to enhance every single‐hypothesis feature; and (3) establishing inter‐hypothesis communication and regressing the final human meshes. Meanwhile, the authors take further advantage of multiple hypotheses and the recovery process to achieve human mesh recovery from multiple uncalibrated views. Compared with state‐of‐the‐art methods, the MH‐HMR approach achieves superior performance and recovers more accurate human meshes on challenging benchmark datasets, such as Human3.6M and 3DPW, while demonstrating the effectiveness across a variety of settings. The code will be publicly available at https://cic.tju.edu.cn/faculty/likun/projects/MH‐HMR.

Dynamic range issues and image information content when visualizing seismic data

   The purpose of this review is to discuss the problem of seismic data visualization in relation to human vision capabilities based on the seismic signal dynamic range assessment.   The ways of increasing the information content and efficiency of visualization methods in terms of increasing their resolution are considered. The conducted analysis has shown that the dynamic range of seismic recordings exceeds the capabilities of the human eye by 2–3 orders of magnitude and the capabilities of technical visualization means by 4–5 orders of magnitude. Color vision models are considered on example of the most versatile hardware additive RGB color model. The model was chosen based on the idea of the human vision system as a set of photosensitive cones that register red, green and blue colors and the human brain, which processes and forms a color image in one’s consciousness. The relationships between monochrome and color images perceived by the human eye based on several types of sensitive receptors have been established. It is concluded that the use of a color image significantly expands information capabilities. Thus, 8 grey steps are enough for the monochrome method of information output, whereas the use of color devices allows to increase the number of steps up to 14. The methodology for solving this problem consists in the application of new technologies based on the use of large-scale and three-dimensional images providing a significant increase in the values of luminous flux, resolution and contrast based on the expansion of the dynamic range taking into consideration the limits of human vision.

CGIHE-VDSR: Color global image histogram equalization with very deep super resolution networks for color image super resolution

Medical and satellite image analysis require incredibly high resolution. Super-resolution combines several low-resolution images of the same scene to generate a high-resolution image. The Super resolution employing deep learning techniques still has an illumination issue. This paper proposes a novel CGIHE-VDSR algorithm that integrates the Very Deep Super Resolution (VDSR) Network with Color Global Image Histogram Equalization (CGIHE) to improve image resolution. In the proposed method, the low-resolution image is first histogram equalized using the CGIHE algorithm. Then, the VDSR network is applied to the histogram equalized image for super-resolution. The comparison of real-time data with the benchmark images is done using the proposed algorithm in the MATLAB platform. The PSNR and SSIM metrics demonstrate that the super resolution image obtained using the proposed method is significantly better than the existing methods.

Comparison of diagnostic accuracy between linked color imaging and autofluorescence imaging in patients with ulcerative colitis: A prospective observational study.

Image enhancement endoscopy techniques, such as linked color imaging (LCI) and autofluorescence imaging (AFI), have shown promise in diagnosing mucosal inflammation in ulcerative colitis (UC). However, no studies have directly compared the diagnostic efficacy of LCI and AFI. This prospective observational study aimed to compare their diagnostic accuracy for histological healing in UC. This study included 81 UC patients, resulting in a total of 204 endoscopic images captured using LCI and AFI, respectively. Spearman's rank correlation coefficients assessed the correlation between LCI and AFI coloration and Geboes histopathology score (GHS). Six endoscopists, who were blinded to clinicopathological features, evaluated these images, and subsequently, the diagnostic accuracy was evaluated. Spearman's rank correlation coefficients between LCI index, AFI index (reverse gamma value), and GHS were 0.324 and -0.428, respectively (P<0.001), indicating a significant correlation between LCI and AFI coloration and histological healing. In LCI and AFI classifications, mean values for diagnostic accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were 76.3±2.2 versus 77.8±2.7, 91.8±4.0 versus 83.2±7.6, 53.4±10.0 versus 70.0±5.3, 74.0±3.5 versus 80.0±1.6, and 82.9±5.2 versus 75.5±7.5, respectively. No significant difference in diagnostic accuracy existed between LCI and AFI classifications. However, LCI displayed higher sensitivity than AFI while AFI showed higher specificity compared with LCI (P<0.05). LCI and AFI offer comparable diagnostic accuracy for histological healing. Clinically, it is necessary to recognize diagnostic features characterized by higher sensitivity in LCI and greater specificity in AFI.

Image splicing detection using low-dimensional feature vector of texture features and Haralick features based on Gray Level Co-occurrence Matrix

Digital image forgery has become hugely widespread, as numerous easy-to-use, low-cost image manipulation tools have become widely available to the common masses. Such forged images can be used with various malicious intentions, such as to harm the social reputation of renowned personalities, to perform identity fraud resulting in financial disasters, and many more illegitimate activities. Image splicing is a form of image forgery where an adversary intelligently combines portions from multiple source images to generate a natural-looking artificial image. Detection of image splicing attacks poses an open challenge in the forensic domain, and in recent literature, several significant research findings on image splicing detection have been described. However, the number of features documented in such works is significantly huge. Our aim in this work is to address the issue of feature set optimization while modeling image splicing detection as a classification problem and preserving the forgery detection efficiency reported in the state-of-the-art. This paper proposes an image-splicing detection scheme based on textural features and Haralick features computed from the input image’s Gray Level Co-occurrence Matrix (GLCM) and also localizes the spliced regions in a detected spliced image. We have explored the well-known Columbia Image Splicing Detection Evaluation Dataset and the DSO-1 dataset, which is more challenging because of its constituent post-processed color images. Experimental results prove that our proposed model obtains 95% accuracy in image splicing detection with an AUC score of 0.99, with an optimized feature set of dimensionality of 15 only.

Multifrequency topological interface modes in a nonlocal acoustic system with large winding numbers

Topological interface states have wide applications for their robustness and energy localization. Multiple topological interface states in 1D acoustic systems are preferred for signal enhancement at specific frequencies. However, previous 1D topological systems with local interactions can realize only one topological interface state in a bandgap. Here, we find that connected acoustic Su–Schrieffer–Heeger (SSH) chains with nonlocal interactions can achieve two or more topological interface states at different frequencies in a single bandgap, even in the subwavelength region. The difference in frequency can be modulated by merely adjusting the structural design at the interface. A mechanical metamaterial with a large winding number is designed to realize multiple nondegenerate topological interface states. This metamaterial can enhance weak acoustic signals at multiple specific frequencies simultaneously and has applications in the fields of acoustic detecting and color imaging.

Color Image Compression Using Wavelet Compression and Zero-MeanCoding

Thousands pictures require a very large amount of storage. So differenttechniques of digital image compression is used to reduce the storage requirementsfor these images. In this paper an adaptive compression method applied on colorimage. Firstly the color image is transformed to the RGB system, and then convertsthe color data to a luminance/chrominance color space (YIQ).The adaptivecompression method is applied on Y. The image may contain a uniform region andedge region; the uniform regions compress depending on zero-mean coding and theedge region compress depending on Daubechies wavelet transform, then returnedthe final image to RGB system. The proposed algorithms has many advantageswhich make it very efficient, these are, low bit rate, low computational complexity,fast processing and edge preservation with good reconstructed image quality.

Enhancing images for vision measurement in hazy tunnel construction

To address the negative impacts of image degradation on vision measurement in hazy tunnel construction, this paper presents a unified enhancement method of color and grayscale images in tunnel haze environments. Firstly, a tunnel haze environment light scattering model is introduced utilizing geometric optics principles and the conventional atmospheric scattering model. Secondly, a model for perceiving image haze is established within the image feature space to estimate ambient light and transmission. The efficacy of the method is assessed by constructing a tunnel haze simulation chamber and a platform for measurement of tunnel boring machine (TBM) cutter wear. Comparative experiments demonstrate the method reduces the error in 3D point cloud reconstruction by 77% and improves the measurement precision of TBM cutter wear by 62%. The results demonstrate the potential application of this method in automated tunnel construction. However, further research is needed to enhance its efficacy in dense haze images.

Meta-learning with hypernetworks: Cost-effective fault detection in Parabolic Trough plants

Detecting receivers with broken glass envelopes in Parabolic Trough plants presents a crucial challenge in Concentrated Solar Power systems. While existing research has addressed this problem, some limitations persist, particularly in the context of data collection and the accuracy of the proposed methods. To overcome these challenges, we propose a fully automated and cost-efficient solution. Leveraging thermal camera data, we develop a novel cropping algorithm that achieves a 91% success rate in detecting the location of the receiver tubes without the need for labeled samples. Additionally, we introduce a luminescence-based filter for color images in Red–Green–Blue format to assess biases in the training and test sets. To solve our target problem we rely on Convolutional Neural Network architectures augmented with Hypernetworks as meta-learners. Our proposal is inspired by traditional voting methods but it is trainable end-to-end. In our test set, the image-based meta-learners obtain the best performance over all evaluation metrics. Furthermore, we provide experimentally driven insights to the weaknesses and strengths of our approach. Combining the experimental results with the luminescence-based filter, we achieve a robust method with a 95% F1-Score for detecting broken glass samples, including edge cases. Our approach surpasses the best individual learner by 2.1%–5.8%, depending on brightness conditions.

Color image encryption algorithm based on novel dynamic DNA encoding and chaotic system* *This work was supported by the Open Fund of Advanced Cryptography and System Security Key Laboratory of Sichuan Province (Grant No. SKLACSS–202208), the Science and Technology Research Program of Chongqing Municipal Education Commission (Grant no. KJZD-M202000501), Sponsored by Natural Science Foundation of Chongqing (CSTB2023NSCQ-LZX0139) and National Natural Science Foundation of China (No.61772295).

To enhance the security of image data, prevent unauthorized access, tampering, and leakage, maintain personal privacy, protect intellectual property rights, and ensure the integrity of images during transmission and storage. This study introduces an innovative color image encryption scheme based on dynamic DNA encoding operations and chaotic systems. By simulating a quantum random walk, a random key is generated to enhance the security of the confidential system. In addition, we integrated the enhanced Josephus problem into DNA coding rules to create dynamic DNA coding rules. At the same time, we proposed a dynamic double-loop DNA XOR operation, which fully utilizes the random sequence generated by the generalized Hamiltonian chaos system to precisely control the loop direction, starting point, and number of operations. This enhances the complexity of the encryption algorithm. After sufficient experimental verification and in-depth research and analysis, our innovative design not only enhances the difficulty of cracking while ensuring image quality but also provides reliable protection for the security of image data.

Detection of Skin Disease by using Image Processing

There are many types and forms of skin diseases and have many causes, including internal ones related to hormones and body glands such as acne, or external related to air pollution or sensitivity to sunlight such as rashes. The skin disease may be contagious, such as scabies and lice, or it may not be contagious, such as drug allergy and rosacea, or it may be chronic, such as psoriasis and atopic eczema, or it may be rare, such as sweet syndrome and ofuji disease. The outlook of society, in general, reduces the importance of skin disease when it appears and it is preferable not to visit the doctor. Skin diseases are a major and worrying problem in societies due to their physical and psychological effects on patients. Detecting skin diseases at an early stage has an important role in treatment. The process of diagnosing and treating skin injury is related to the skill and experience of the specialist doctor. skin disease prediction by using a neural network based on image analysis. Diagnosis and prediction of skin disease take a very long process because it requires a patient’s history, physical examination, and proper laboratory diagnostic tests. st of such diagnosis is still limited and very expensive. So, image processing techniques help to build automated screening system for dermatology at an initial stage. The extraction of features plays a key role in helping to classify skin disease. proach works on the inputs of a color image. Then resize the of the image to extract features using pre trained convolutional neural network. After that classified feature using Multiclass SVM. Finally, the results are shown to the user, including the type.

Digital Image Analysis to Evaluate Sensory Attributes of Protein-Enriched Whole-Wheat Bread

New food products or reformulated food products require intensive sensory assessment using a group of panelists before launching in the market. Sometimes, the sensory results obtained by the panelists are inconclusive due to their subjective scores. An indirect and accurate method to evaluate the sensory attributes using images is highly beneficial to conduct preliminary screening during product development stages. Therefore, the objective of this study was to determine the potential of red-green-blue (RGB) color images to evaluate the sensory qualities of whole wheat bread reformulated with pea and soy protein isolates as model food. In this study, reformulated whole wheat (WW) bread was used as model food to determine the potential of digital color images in assessing the selected sensory attributes. Seven types of WW bread was evaluated by ten untrained panelists. Four features (edge detection, pore numbers, pore area and Hu-moment similarity) were extracted from the images of the bread slices and compared with measured sensory scores. In general, the polynomial regression models yielded higher R2 values than linear regression models. The R2 values in polynomial regression models ranged 0.82-0.97, 0.60-0.92, 0.55-0.96, 0.77-0.99, 0.67-0.97, and 0.50-0.87 for chewiness, graininess, moistness, taste, desired aroma and overall acceptability, respectively. Hu-moment similarity provided the highest R2 values for the sensory attributes in polynomial regression models. In conclusion, although image-based sensory assessment may not substitute the current human sensory, it can provide valuable information to supplement the decision making process.

An efficient lightweight network for image denoising using progressive residual and convolutional attention feature fusion

While deep learning has become the go-to method for image denoising due to its impressive noise removal capabilities, excessive network depth often plagues existing approaches, leading to significant computational burdens. To address this critical bottleneck, we propose a novel lightweight progressive residual and attention mechanism fusion network that effectively alleviates these limitations. This architecture tackles both Gaussian and real-world image noise with exceptional efficacy. Initiated through dense blocks (DB) tasked with discerning the noise distribution, this approach substantially reduces network parameters while comprehensively extracting local image features. The network then adopts a progressive strategy, whereby shallow convolutional features are incrementally integrated with deeper features, establishing a residual fusion framework adept at extracting encompassing global features relevant to noise characteristics. The process concludes by integrating the output feature maps from each DB and the robust edge features from the convolutional attention feature fusion module (CAFFM). These combined elements are then directed to the reconstruction layer, ultimately producing the final denoised image. Empirical analyses conducted in environments characterized by Gaussian white noise and natural noise, spanning noise levels 15–50, indicate a marked enhancement in performance. This assertion is quantitatively corroborated by increased average values in metrics such as Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Feature Similarity Index for Color images (FSIMc), outperforming the outcomes of more than 20 existing methods across six varied datasets. Collectively, the network delineated in this research exhibits exceptional adeptness in image denoising. Simultaneously, it adeptly preserves essential image features such as edges and textures, thereby signifying a notable progression in the domain of image processing. The proposed model finds applicability in a range of image-centric domains, encompassing image processing, computer vision, video analysis, and pattern recognition.