Gray Images Research Articles

Abnormal crop warning system based on OpenMV

In order to realize the early warning of abnormal crops, the photographing technology and image recognition technology of openMV and openCV are comprehensively applied to study the early warning of abnormal crops. The design take photos using openMV hardware platform and connects to the cloud through 5G module. Then it conducts in-depth processing such as gray processing, image denoising and boundary detection on the photos through the network server to obtain the location and size of abnormal areas, so as to help spray pesticides later and improve production efficiency.

A machine learning system to identify progress level of dry rot disease in potato tuber based on digital thermal image processing

This study proposed a quick and reliable thermography-based method for detection of healthy potato tubers from those with dry rot disease and also determination of the level of disease development. The dry rot development inside potato tubers was classified based on the Wiersema Criteria, grade 0 to 3. The tubers were heated at 60 and 90 °C, and then thermal images were taken 10, 25, 40, and 70 s after heating. The surface temperature of the tubers was measured to select the best treatment for thermography, and the treatment with the highest thermal difference in each class was selected. The results of variance analysis of tuber surface temperature showed that tuber surface temperature was significantly different due to the severity of disease development inside the tuber. Total of 25 thermal images were prepared for each class, and then Otsu’s threshold method was employed to remove the background. Their histograms were extracted from the red, green, and blue surfaces, and, finally, six features were extracted from each histogram. Moreover, the co-occurrence matrix was extracted at four angles from the gray level images and five features were extracted from each co-occurrence matrix. Totally, each thermograph was described by 38 features. These features were used to implement the artificial neural networks and the support vector machine in order to classify and diagnose the severity of the disease. The results showed that the sensitivity of the models in the diagnosis of healthy tubers was 96 and 100%, respectively. The overall accuracy of the models in detecting the severity of tuber tissue destruction was 93 and 97%, respectively. The proposed methodology as an accurate, nondestructive, fast, and applicable system reduces the potato loss by rapid detection of the disease of the tubers.

Texture-aware and color-consistent learning for underwater image enhancement

Texture and color are pivotal factors for evaluating the quality of underwater image enhancement. However, current methods for enhancing underwater images still exhibit deficiencies in the restoration of texture and color. Diverging from previous approaches, we conduct heuristic modeling specifically targeting color and texture, culminating in the proposal of a texture-aware and color-consistent network (TACC-Net). Specifically, it first obtains preliminary enhanced images of more global features to facilitate feature decoupling. Then, we designed a color feature decoupling (CFD) sub-module to decouple the preliminary enhanced images into color-consistent color histograms to generate textured gray images. Finally, we designed a color space adjustment embedding (CSAE) sub-module, which is used to evaluate the similarity between features when embedding color information, and adjust the embedding of color information on textures. The experimental results indicate that our method enhances images and exhibits strong competitiveness in improving visual quality.

Fast and accurate visual vibration measurement via derivative-enhanced phase-based optical flow

Visual vibration measurement using a digital camera is an emerging non-contact modality capable of enabling flexibility, high spatial resolution, and full field. However, the massive amounts of video data with vibration information (thousands of time-lapse images per measurement) to be handled lead to an actual bottleneck. This prevents the system from extracting the vibration information with high speed and fidelity. Here, a straightforward phase-based optical flow approach is proposed by introducing a derivative operation to perform vibration measurements in a more accurate and efficient manner, in contrast to the state-of-the-art phase-based visual method. This approach generates analytical signals by considering each raw gray image as the real part and its first-order derivative as the imaginary part. As a result, the physical vibration can be extracted from the phase variations of the analytical signals from one to the next. Numerical simulations and experiments are conducted to validate the effectiveness and accuracy of this simple but powerful approach. The results indicate that the proposed approach provides a significantly higher speed than current methods and a correlation coefficient of 99.2 % with the identification results.

Анализ эффективности нового способа выявления ахроматопсии

Aim: to assess the efficacy of a novel method for diagnosing achromatopsia. Patients and Methods: twenty patients with congenital achromatopsia established by genetic testing were enrolled. Sixteen (80%) patients were diagnosed with mutations in the CNGB3 gene and 4 (20%) patients were diagnosed with mutations in the CNGA3 gene. The control group 1 (with eye diseases) included 37 patients with congenital optic atrophy and 5 patients with oculocutaneous albinism. The control group 2 (without eye diseases) included 40 individuals. At the initial stage of the study, we compared the perception of brightness of chromatic and achromatic images in patients with achromatopsia. To this end, we employed our own images with specific characteristics of color tone, lightness (brightness), and saturation. The results of the first phase have demonstrated that for most patients with achromatop-sia, the lightest and least distinguishable images on a white background are blue. In light of this, we developed tests to detect achromatopsia (tests #1 and #2). Each test comprises four gray silhouette images (with a lightness of 80% for test #1 and 50% for test #2) and four blue images (R0/ G255/B255). For the blue images, saturation is maximal (S 100%) in both tests, while luminosity is L 80% for test #1 and L 50% for test #2. Results: all individuals in the control group 2, patients with optic atrophy and oculocutaneous albinism, were able to see all blue and gray images of both tests and easily identified the blue images corresponding to the gray (achromatic) ones. In the first test, 95% of patients with achromatopsia (n=19) were unable to see blue images or distinguish their shape from gray images. In contrast, all patients were able to distinguish the shape of gray images of the same lightness and specify them. In test #2 (L 50%), the majority of patients (n=13, 65%) exhibited difficulties with the shape of blue images. The remaining seven patients (35%) demonstrated the ability to name blue images and match them to gray images. Two of them were diagnosed with mutations in the CNGA3 gene, while five of them were diagnosed with mutations in the CNGB3 gene. Therefore, the cone system disorder in these patients was less severe than in the others. Conclusion: the achromatopsia test, developed on the basis of comparative analysis of perceived luminosity of chromatic and achromatic images, is simple and accessible. This test provides preliminary differential diagnosis with other eye diseases and determines the most appropriate strategy of further examinations to establish the diagnosis. KEYWORDS: achromatopsia, color vision, method to diagnose achromatopsia, color per-ception abnormality, monochromacy, color vision deficiency, color disfunction. FOR CITATION: Sukhanova N.V., Rychkova S.I., Likhvantseva V.G., Sandimirov R.I., Kadyshev V.V., Zinchenko R.A. Analyzing the efficacy of a novel method for detecting achromatopsia. Russian Journal of Clinical Ophthalmology. 2024;24(2):49–54 (in Russ.). DOI: 10.32364/2311- 7729-2024-24-2-1.

Comparison of Signal to Noise Ratio of Colored and Gray Scale Image in Clustered Condition From the Contours of the Images With the Help of Different Image Filtering Method

Comparison of Signal to Noise Ratio of Colored and Gray Scale Image in Clustered Condition From the Contours of the Images With the Help of Different Image Filtering Method

Research on the Application of Image Feature Extraction in Mechanical Structure Recognition and Fault Diagnosis

Abstract With the rapid development of modern industry and science and technology, in recent years the fault diagnosis method based on image processing has become a research hotspot in the field of mechanical fault diagnosis. In this paper, image characteristics are extracted from multiple aspects such as image texture, color, shape, etc. A grayscale symbiotic matrix image feature extraction method is proposed. On this basis, the algorithm for extracting gray symbiotic matrix time-frequency image features is designed. At the same time, the algorithm and parameters of mechanical structure identification are optimized to identify and diagnose mechanical faults. The results show that the grayscale symbiotic matrix time-frequency image feature extraction algorithm is able to accurately diagnose the wear-type faults, overwork-type faults, and short-circuit-type fault behavior of the mechanical equipment. All of them are able to obtain more than 80% accuracy, and all of them are able to reach 99.99% accurate detection of mechanical faults, which proves the effectiveness of the method of this research.

Constacyclic codes over the non-chain finite commutative ring 4 2 2 [ , ] , , u v 〈u − u v uv〉

This paper considers (1+2u) -constacyclic and (1+2u) -skew constacyclic codes over the ring R = Z4[u,v]/<u2 – u, v2, uv>, and some Gray maps over this ring are presented. The Gray images of these codes are shown to be cyclic, quasi-cyclic, and permutation equivalent to quasi-cyclic codes. In addition, (1+2u) constacyclic codes are given with images that are new Z4 codes according to the online database of linear Z4 codes [3].

Using kernel functions to estimate the probability density function for segmentation images

This paper used nonparametric kernel functions to segment images using the thresholding method. This was done by transforming the image to grey and then estimating the probability density function from the grey image data and taking the highest value of the core function vector as the threshold limit. The study also found that the bandwidth parameter affects the segmentation process because it affects the estimate of the probability density function. The bandwidth parameter smooths the curve and brings it closer to the true curve because of its significant effect on bias and variance. The beam width parameter is known to depend on the size of the sample. Thus, it was found that when the parameter is used to determine the image size into equal rows and columns, it gives good, satisfactory results for segmented images that contain the most important areas of interest with removing unhelpful or important areas. Finally, the uniform Gaussian, cosine, triangular, and logistic Silverman kernel functions proved their efficiency in extracting all image features.

An Efficient Multiscale Spatial Rearrangement MLP Architecture for Image Restoration.

The effective use of long-range information can yield improved network performance, which is very important for image restoration. Although local window-based models have linear complexity and can be feasibly applied to process high-resolution images, a single-scale window has a limited receptive field and is less efficient for encoding long-range context information. To address this issue, this paper presents a single-stage multiscale spatial rearrangement multilayer perceptron (MSSR-MLP) architecture that can obtain information at different scales within a local window. Specifically, we propose a simple and efficient spatial rearrangement module (SRM) that moves information outside the local window to the inside of the local window so that long-range dependencies can be modeled using only a window-based fully connected (FC) layer. The SRM can extend the local receptive field of a window-based FC layer without introducing additional parameters and FLOPs. Utilizing several spatial rearrangement modules with different step sizes, we design an efficient multiscale spatial rearrangement MLP architecture for image restoration. This design aggregates multiscale information to achieve improved restoration quality while maintaining a low computational cost. Extensive experiments conducted on several image restoration tasks demonstrate the efficiency and effectiveness of our method. For example, it requires only ~4.3% of the FLOPs needed by SwinIR for Gaussian gray image denoising, ~13.9% of the FLOPs needed by C2 PNet for single-image dehazing and ~18.9% of the FLOPs needed by MAXIM for single-image motion deblurring but achieves better performance on each of these restoration tasks.

Research on High-Performance Fourier Transform Algorithms Based on the NPU

Backpack computers require powerful, intelligent computing capabilities for field wearables while taking energy consumption into careful consideration. A recommended solution for this demand is the CPU + NPU-based SoC. In many wearable intelligence applications, the Fourier Transform is an essential, computationally intensive preprocessing task. However, due to the unique structure of the NPU, the conventional Fourier Transform algorithms cannot be applied directly to it. This paper proposes two NPU-accelerated Fourier Transform algorithms that leverage the unique hardware structure of the NPU and provides three implementations of those algorithms, namely MM-2DFT, MV-2FFTm, and MV-2FFTv. Then, we benchmarked the speed and energy efficiency of our algorithms for the gray image edge filtering task on the Huawei Atlas200I-DK-A2 development kits against the Cooley-Tukey algorithm running on CPU and GPU platforms. The experiment results reveal MM-2DFT outperforms OpenCL-based FFT on NVIDIA Tegra X2 GPU for small input sizes, with a 4- to 8-time speedup. As the input image resolution exceeds 2048, MV-2FFTv approaches GPU computation speed. Additionally, two scenarios were tested and analyzed for energy efficiency, revealing that cube units of the NPU are more energy efficient. The vector and CPU units are better suited for sparse matrix multiplication and small-scale inputs, respectively.

Improved dark channel priori single image defogging technique using image segmentation and joint filtering.

Foggy images affect image analysis and measurement because of their low definition and blurred details. Despite numerous studies on haze in natural images in hazy environments, the recovery effect is not ideal for processing hazy images in sky areas. A dark channel priori technique for processing haze images with sky areas where atmospheric light values are misestimated and halo artefacts are produced, as well as an improved dark channel priori single-image defogging technique based on image segmentation and joint filtering, are proposed. First, an estimation method of the atmospheric illumination value using image segmentation is proposed to obtain the atmospheric illumination value. The probability density distribution function of the haze-grey image was constructed during image segmentation. The probability density distribution function of the grey image value, the K-means clustering technique, and the method for estimating atmospheric illumination values are combined to improve image segmentation techniques and achieve the segmentation of sky and non-sky areas in hazy images. Based on the segmentation threshold, the number of pixels in the sky and non-sky areas, as well as the normalisation results, were counted to calculate the atmospheric illumination values. Second, to address the halo artefact phenomenon, a method for optimising the image transmittance map using joint filtering is proposed. The image transmittance map was optimised by combining fast-guided filtering and weighted least-squares filtering to retain the edge information and smooth the gradient change of the internal region. Finally, gamma correction and automatic level optimisation are used to improve the brightness and contrast of the defogged images. The experimental results show that the proposed technique can effectively achieve sky segmentation. Compared to the traditional dark-channel prior technique, the proposed technique suppress halo artefacts and improve image detail recovery. Compared to other techniques, the proposed technique exhibited excellent performance in subjective and objective evaluations.

Evaluation of different median filters for image processing tasks

Impulsive noise is captured at the acquisition stage of digital image processing. It is an important step in the pre-processing process which is used to improve the image quality and make it more informative. There have been several proposed image-denoising algorithms, each with advantages and disadvantages. This work gives a thorough investigation of the median filter and its various variations for the reduction or elimination of impulsive noise in grey scale images. In terms of usefulness and comparative performance, Standard Median Filter, Adaptive Median Filter and Modified Median Filter are contrasted. On a set of pictures, thorough MATLAB-based simulations have been run to assess the performance of the current algorithms. Peak Signal to Noise Ratio was employed to compare performance in this case (PSNR). In terms of relative statistical performance, the Modified Median Filter (MMF) is the best.

Chaotic Multiple-Image Encryption Algorithm Based on Block Scrambling and Dynamic DNA Coding

In this paper, a chaotic multiple-image encryption algorithm using block scrambling and dynamic DNA coding is designed. The algorithm can achieve simultaneous encryption of many different types of images. Firstly, a new block scrambling algorithm is proposed, which is divided into two scrambling modes according to the size of the initial block. And it combines with the cyclic shift operation to achieve scrambling. Secondly, dynamic DNA coding is utilized to diffuse images, which enhances the complexity and security of the proposed multiple-image encryption algorithm. Through the analysis of key space, correlation, information entropy, histogram, differential attacks and robustness, it is verified that the algorithm is safe and effective. Experimental results show that the new multiple-image encryption algorithm is suitable for multiple color and gray images encryption. The algorithm has excellent encryption performance, and can be applied in secure communication.

Mixed gray-color images encryption algorithm based on a memristor chaotic system and 2D compression sensing

Multi-image encryption has attracted great interest in the era of secret communication, however, the mixed encryption of gray and color images has been reported rarely. Meanwhile, the security of image encryption based on chaos system depends heavily on the performance of pseudo randomness and sensibility. Thus, a four-dimensional chaotic system is improved by introducing a memristor, which has good nonlinear characteristic and can enhance chaotic property. It is found that this new system possesses larger chaotic scope and exhibits a coexistence phenomenon, all of which reveal the intricate nature and underlying application. After the verification of spectral entropy and circuit simulations of the new system, a mixed-image encryption algorithm based on two-dimension compression sensing (2DCS) is designed innovatively. To achieve the target of compression and encryption, the image set is first preprocessed and varied utilizing DWT, and then is sampled by measurement matrix in two-dimension. Next, the pixels of compressed image set are diffused by employing DNA coding. The accuracy of reconstruction image is closely related to the randomness and sparsity of the measurement matrix. Chaotic system with pseudo-randomness can be fully exploited to fabric a measurement matrix. Hence the Toeplitz cyclic chaos matrix is conceived and the reconstruction accuracy does have better results. In addition, this method can not only encrypt multiple mixed images with grey and color simultaneously, but also compress them with different sizes and less storage space. At last, the reliability of the novel algorithm is assessed through a series of reconstruction and security performance tests. The results indicate that the encryption scheme has a better effect than encrypting one kind of image, and prove that the algorithm has high reconstruction accuracy and security.

Tchebichef moments and bat optimization algorithm-based information hiding and authentication of medical gray level images

Tchebichef moments and bat optimization algorithm-based information hiding and authentication of medical gray level images

NODE-ImgNet: A PDE-informed effective and robust model for image denoising

Inspired by the traditional partial differential equation (PDE) approach for image denoising, we propose a novel neural network architecture, referred as NODE-ImgNet, that combines neural ordinary differential equations (NODEs) with convolutional neural network (CNN) blocks. NODE-ImgNet is intrinsically a PDE model, where the dynamic system is learned implicitly without the explicit specification of the PDE. This naturally circumvents the typical issues associated with introducing artifacts during the learning process. By invoking such a NODE structure, which can also be viewed as a continuous variant of a residual network (ResNet) and inherits its advantage in image denoising, our model achieves enhanced accuracy and parameter efficiency. In particular, our model exhibits consistent effectiveness in different scenarios, including denoising gray and color images perturbed by Gaussian noise, as well as real-noisy images, and demonstrates superiority in learning from small image datasets.

Colorization of gray scale images based on convolutional block attention and Pix2Pix network

Image colorization is a key research direction in the field of computer vision, which aims to predict the color information of grayscale images and convert them into color images. Traditional image colorization methods need to provide a large number of color points to mark each object in the image. As a result of the rapid development of convolutional neural networks, image colorization methods have also improved, with deep learning methods benefiting the most. However, due to the lack of feature quality, there are still many challenges in practical applications, such as boundary blurring. In this paper, we propose a gray image colorization method based on the Convolutional Block Attention Module (CBAM) and Pix2Pix network. Specifically, we add the CBAM module to the U-net generator network, which helps adaptively capture sensitive features, thereby enhancing the quality of features and image coloring. The effectiveness of the proposed model is verified by experiments.

Imaging genetics based dementia risk prediction using deep survival neural network

AbstractBackgroundDementia has a large impact on people’s lives and is one of the leading causes of death. Therefore, accurate risk assessment is essential for improving early diagnostics and intervention. Previous studies have proposed various models for dementia risk prediction, based on imaging biomarkers and genetic factors. However, most of them have linear, unimodality or low‐dimensional nature due to model limitations and computational challenges of integrating such heterogeneous and high‐dimensional domains as images and genotype data. Recent development of Artificial Intelligence, specifically neural networks, enabled such integration, yet focused on classification problems, not risk prediction. We present a deep survival neural network to meet the purpose.MethodData were obtained from the Rotterdam Study. Individuals with complete data and available brain MRI before dementia diagnosis were included. MRI scans were pre‐processed using voxel‐based morphometry according to Good et al. into gray matter images. We included ApoE‐ε4 and 76 top‐risk SNPs based on most recently published GWAS (Bellenguez et al.). We developed the model combining Convolutional Neural Network and Survival Analysis, with the maximum‐likelihood estimator and C‐index of cox proportional hazard (Cox) model as the loss function and metric. To evaluate model performances, we used Cox model as comparisons: one baseline model with gender, age, genetic inputs and one extended model with additional extracted brain measurements. We performed 10 cross‐validation by stratified sampling to reveal the true differences.ResultTable1 shows little performance differences among the models, as expected age is already the strongest predictor. However, we see that raw gray matter and genetics inputs contribute to prediction additionally in all models. Furthermore, using the method from Tsang et al. we show strong interactions among image, age and genetic features (Fig.1). Fig.2 visualizes the most important brain regions for prediction on 3D image using GRAD‐CAM.ConclusionWe developed a deep survival model for dementia risk prediction which do not outperform classical model with brain measurements at present. However, our model is able to integrate high‐dimensional MRI and genetic data, detect and visualize the importance and interactions among input data, which may give additional insight for dementia prediction and etiologic research.

Technical Note: Novel imaging method to obtain gray matter-attenuated inversion recovery image using low-field magnetic resonance imaging systems

IntroductionThe double inversion recovery (DIR) technique suppresses two types of tissue signals with different T1 values by applying two inversion recovery (IR) pulses with different inversion times (TI). In contrast, the double tissue suppression with multi-echo acquisition and single TI combining HIRE (DOMUST-HIRE) method, is a technique enabling the white-matter-attenuated inversion recovery (WAIR) images by setting one inversion time (TI) in a sequence based on the multi-echo method and subtracting the second echo image from the first echo image. Here, we propose a new sequence that can provide the gray-matter-attenuated inversion recovery image based on the DOMUST-HIRE method. MethodsIn this small clinical study, we performed determination of optimal TI and physical evaluation by imaging a subject’s head with T1WI and our proposed method for GAIR images. ResultsOur proposed method could increase the contrast ratio and the contrast-to-noise ratio between white matter (WM) and gray matter (GM), whereas the signal-to-noise ratio WM and GM decreased than with T1WI method. ConclusionsOur proposed method can be used to suppress GM and CSF signals. Implications for practiceThe use of our proposed method in low-field MRI systems could provide GAIR image.