Image Color Research Articles

Enhancement method for non-uniform scattering images of turbid underwater based on neural network

Due to the influence of light absorption and scattering in the water medium, underwater images often appear with different degrees of blur, color distortion, and so on. The physical model-based method needs to establish image degradation processes and improve image quality by assuming and estimating complex parameters, which limits the generalization ability and practical application effect of these methods. To solve the above problems, this paper proposes an image enhancement method of non-uniform scattering in turbid underwater based on neural network. First, the high-frequency and low-frequency components of underwater images were obtained through the difference of gaussian (DOG) and bilateral filtering (BF), respectively. Then, for the high-frequency component, pulse coupled neural networks (PCNN) to improve the edge details of the image. For low-frequency components, it is carried out in two steps: an adaptive automatic white balance and polynomial regression (AAWBPR) algorithm is proposed, which can effectively eliminate the color bias under different color temperatures and improve the brightness of images; the adaptive color and contrast enhancement (ACCE) method is used to further enhance the color and contrast of images. Finally, Decom-Net and the improved U-Net method are used to reduce the errors caused by the non-uniform scattering effect, weaken the noise interference, and enhance the overall effect of images. Extensive experiments show that the proposed method can effectively improve the quality of turbid underwater non-uniform scattering images, enhance the contrast of images, reduce the complexity of parameters, and has good generalization performance and practical application effects.

Low illumination image enhancement based on attention mechanism and global illumination estimation

Aiming at the problems of insufficient or excessive local brightness enhancement, color distortion, and excessive noise in the existing low-light image enhancement algorithms, a low-light image enhancement method combining attention mechanism and global illumination estimation is proposed. First, the illumination distribution map of low illumination image is obtained through the illumination distribution estimation network coupled with the attention gate mechanism. Then, the weight of the light distribution map is learned in the feature attention module. Finally, the image details are fused by the detail reconstruction module to create improved image. According to the experimental results, this method may effectively improve image brightness, contrast, and color in subjective visual effects while also enhancing objective evaluation indicators like PSNR, SSIM, and MSE when compared to some conventional methods.

Matrix R-based Visual Response to Optimal Colors and Application to Image Color Gamut Expansion

Matrix R-based Visual Response to Optimal Colors and Application to Image Color Gamut Expansion

A Novel Approach to Optimize Memory Reconstruction Using Joint Multimodal Networks

Memories play a crucial role in the human life experience. Whether they are used to make decisions or understand current situations, memories influence the past, present, and future. However, some memories are unreliable; in the court of law, the false memories of victims can cause inaccurate verdicts. To reconstruct human memories directly from the brain, recent studies have used electroencephalographic (EEG) brain signals in machine learning frameworks like generative adversarial networks (GANs), which generates new data from old ones. However, traditional GANs tend to produce the same images, an issue called mode collapse. Therefore, a conditional variational autoencoder-generative adversarial network (CVAE-GAN), which had not been used before in memory reconstruction, was developed to address GAN failures by jointly-training (1) an encoder and (2) GAN. CVAE-GAN correlated the MindBigData dataset’s EEG brain signals with the corresponding ImageNet dataset images and produced new memories of participants, such as pandas, humans, and fish. CVAE-GAN’s Inception Score, which marked how diverse and distinct its generated images were, was 1.00 on average. The number of floating operations, or FLOPS, was 102 Gigaflops, which was less than the traditional GAN’s 198 Gigaflops. While limitations with time, computational memory, and mode collapse prevented the CVAE-GAN from recreating accurate memories, it still generated distinct image colors and general features. Future studies can build on existing network architectures and include more homogeneity in datasets. Ultimately, CVAE-GAN has the potential to advance new understandings of the brain and could elevate the memory reconstruction field.

A New Efficient Forgery Detection Method using Scaling, Binning, Noise Measuring Techniques and Artificial Intelligence (Ai)

In the market new updated editing tools and technologies are available to edit images and with help of these tools images are easily forged. In this research paper we proposed new forgery detection technique with estimation of noise on various scale of input image affect of noise in input image, frequency of images are also changed due to noise, noise signal correlated with original input images and in compressed images quantization level frequency also changed due to noise.We partition input image into M X N blocks, resized blocks are proceed further, image colors are also taken into consideration, each block noise value is evaluated at local level and global level. For each color channel of input image estimate local and global noise levels are estimated and compared using binning method. Also measured heat map of each block and each color channel of input image and all these values are stored in bins. Finally from all noise values calculate average mean value of noise, with these values decide whether input image is forgery or not, and performance of proposed method is compared with existing methods.

Artificial Intelligence in Tongue Image Recognition

Tongue image recognition is a traditional Chinese medicine diagnosis method, which uses the shape, color, and texture of the tongue to judge the health of the human body. With the rapid development of artificial intelligence technology, the application of artificial intelligence in the field of tongue recognition has been widely considered. Based on the intelligent analysis of tongue diagnosis in traditional Chinese medicine, this paper reviews the application progress of artificial intelligence in tongue image recognition in recent years and analyzes its potential and challenges in this field. Firstly, this paper introduces three steps of tongue image recognition, including tongue image acquisition, tongue image preprocessing, and tongue image feature analysis. The application of traditional methods and artificial intelligence methods in the whole process of tongue image recognition is reviewed, especially the tongue body segmentation, and the advantages and disadvantages of convolutional neural networks are analyzed and compared. Artificial intelligence can use technologies such as deep learning and computer vision to automatically analyze and extract features from tongue images. By constructing a tongue image recognition model, tongue shape, color, texture, and other features can be accurately recognized and quantitatively analyzed. Finally, this paper summarizes the problems existing in artificial intelligence in tongue image recognition and looks forward to the future developmental direction of this field. It can promote the modernization of TCM diagnostic methods, achieve early disease screening and prevention, personalized medicine and treatment optimization, and support medical research and knowledge accumulation. However, there is still a need for further validation and practice, with a focus on patient privacy and data security.

Pengaruh Desain Kemasan terhadap Keputusan Pembelian Produk Biskuit dan Makanan Ringan PT Siantar Top Tbk Pada Siswa Siswi SMA Santa Lusia Bekasi

This study aims to obtain empirical evidence regarding the effect of packaging products with indicators packaging brand, packaging images, packaging shapes and packaging colors on the buyer's decision of PT Siantar Top Tbk products on students of SMA Santa Lusia Bekasi. The data used are primary data and the student population of SMA Santa Lusia Bekasi. The sample selection method used is the probability sampling method. The total population in this study was 101 high school students from Santa Lusia Bekasi and the sample used was 50 students from SMA Santa Lusia Bekasi. Buyer's decision as the dependent variable, while the packaging brand, packaging image, packaging shape and packaging color are used as independent variables. The method used is multiple regression analysis using the SPSS program. The results of the research that have been carried out show that the packaging brand variable has a significant number of 0.030, the shape of the packaging has a significant number of 0.025 and the color of the packaging has a significant number of 0.000 which means that it partially affects the buyer's decision because it gets a number below 0.05, while the variable the packaging image has a significant number of 0.108 which means that it partially has no effect on purchasing decisions because it gets a number above 0.05, and the sour brand variable, packaging image, packaging shape and packaging color have a significant number of0.000 which means it has a simultaneous effect on the decision. buyers for getting numbers below 0.05.

BAWGNet: Boundary aware wavelet guided network for the nuclei segmentation in histopathology images

Precise cell nucleus segmentation is very critical in many biologically related analyses and disease diagnoses. However, the variability in nuclei structure, color, and modalities of histopathology images make the automatic computer-aided nuclei segmentation task very difficult. Traditional encoder–decoder based deep learning schemes mainly utilize the spatial domain information that may limit the performance of recognizing small nuclei regions in subsequent downsampling operations. In this paper, a boundary aware wavelet guided network (BAWGNet) is proposed by incorporating a boundary aware unit along with an attention mechanism based on a wavelet domain guidance in each stage of the encoder–decoder output. Here the high-frequency 2 Dimensional discrete wavelet transform (2D-DWT) coefficients are utilized in the attention mechanism to guide the spatial information obtained from the encoder–decoder output stages to leverage the nuclei segmentation task. On the other hand, the boundary aware unit (BAU) captures the nuclei’s boundary information, ensuring accurate prediction of the nuclei pixels in the edge region. Furthermore, the preprocessing steps used in our methodology confirm the data’s uniformity by converting it to similar color statistics. Extensive experimentations conducted on three benchmark histopathology datasets (DSB, MoNuSeg and TNBC) exhibit the outstanding segmentation performance of the proposed method (with dice scores 90.82%, 85.74%, and 78.57%, respectively). Implementation of the proposed architecture is available at https://github.com/tamjidimtiaz/BAWGNet.

Digital image colour analysis to evaluate the moisture sensitivity of hot-mix asphalt mixes

Moisture in hot-mix asphalt (HMA) can cause serious damage such as cracks and ruts if left untreated. This is due to the separation of binder and aggregates caused by moisture-induced stripping. Existing approaches for assessing moisture damage in asphalt are time consuming, destructive and expensive to implement. In contrast, digital image analysis provides a simple, inexpensive and non-destructive method for identifying moisture-sensitive mixes. Colour, as an essential characteristic of an image, effectively conveys information about its content. This study seeks to explore the potential of analysing digital image colour in the L*a*b* colour space to assess the moisture sensitivity of HMA after moisture-induced stress tester conditioning. Laboratory-compacted specimens of loose asphalt mixtures from the field were studied by image processing followed by statistical tests of significance. It is found that two parameters – namely, the ‘average colour value for the L* channel’ and the ‘standard deviation value for the b* channel’ – can be used to distinguish between moisture-sensitive and non-sensitive mixes. The findings of this study suggest that future research may find colour to be a helpful criterion for characterising asphalt materials using digital image processing.

Multi-Feature Fusion-Guided Low-Visibility Image Enhancement for Maritime Surveillance

Low-visibility maritime image enhancement is essential for maritime surveillance in extreme weathers. However, traditional methods merely optimize contrast while ignoring image features and color recovery, which leads to subpar enhancement outcomes. The majority of learning-based methods attempt to improve low-visibility images by only using local features extracted from convolutional layers, which significantly improves performance but still falls short of fully resolving these issues. Furthermore, the computational complexity is always sacrificed for larger receptive fields and better enhancement in CNN-based methods. In this paper, we propose a multiple-feature fusion-guided low-visibility enhancement network (MFF-Net) for real-time maritime surveillance, which extracts global and local features simultaneously to guide the reconstruction of the low-visibility image. The quantitative and visual experiments on both standard and maritime-related datasets demonstrate that our MFF-Net provides superior enhancement with noise reduction and color restoration, and has a fast computational speed. Furthermore, the object detection experiment indicates practical benefits for maritime surveillance.

LPS-Net: Lightweight Parallel Strategy Network for Underwater Image Enhancement

Underwater images are frequently subject to color distortion and loss of details. However, previous enhancement methods did not tackle these mixed degradations by dividing them into sub-problems that could be effectively addressed. Moreover, the parameters and computations required for these methods are usually costly for underwater equipment, which has limited power supply, processing capabilities, and memory capacity. To address these challenges, this work proposes a Lightweight Parallel Strategy Network (LPS-Net). Firstly, a Dual-Attention Enhancement Block and a Mirror Large Receptiveness Block are introduced to, respectively, enhance the color and restore details in degraded images. Secondly, we employed these blocks on parallel branches at each stage of LPS-Net, with the goal of achieving effective image color and detail rendering simultaneously. Thirdly, a Gated Fusion Unit is proposed to merge features from different branches at each stage. Finally, the network utilizes four stages of parallel enhancement, achieving a balanced trade-off between performance and parameters. Extensive experiments demonstrated that LPS-Net achieves optimal color enhancement and superior detail restoration in terms of visual quality. Furthermore, it attains state-of-the-art underwater image enhancement performance on the evaluation metrics, while using only 80.12 k parameters.

Research of soil surface image occlusion removal and inpainting based on GAN used for estimation of farmland soil moisture content

In smart agriculture, soil images are frequently used to estimate soil moisture content (SMC). The soil image can’t fully reflect the real soil surface condition due to the interference of weeds and other obstructions. To solve the occlusion problem, a new occlusion removal idea is proposed: identifying the occluded object, generating corresponding masks to cover it, and restoring the area. Therefore, it involves three parts: image inpainting network research, occlusion recognition and removal system, and practical application in farmland. Soil Surface Occlusion Image Inpainting Network (SOI NET) was proposed for image inpainting based on Generative Adversarial Networks (GAN). A two-stage generation network was designed: Rough Network and Refinement Network which uses the coherent semantic attention (CSA) module to improve the network's context information utilization capability. A soil surface image occlusion removal system was designed, including vehicle-mounted detection system, occlusion target recognition, mask generation and occlusion removal. Indoor and field models for estimating SMC using image color parameters were established. To verify the performance of SOI NET, experiments were carried out to compare with the traditional method and other deep learning methods. The actual farmland occlusion removal experiment was carried out with weeds as the removal object which are the most common occlusion. The results revealed that the system can effectively remove the occlusion from image surface images. In order to verify the improvement effect of occlusion removal image on the detection accuracy of vehicle-mounted terminals, further farmland experiments were conducted to estimate SMC. R2 increased from 0.637 to 0.667 and Root Mean Square Error (RMSE) decreased from 1.916 % to 1.822 % after occlusion removal. SOI NET and soil occlusion removal system can make the processed images reflect the real soil conditions and provide help for soil image analysis.

Digital inpainting of mural images based on DC-CycleGAN

Located in Dunhuang, northwest China, the Mogao Grottoes are a cultural treasure of China and the world. However, after more than 2000 years of weathering and destruction, many murals faded and were damaged. This treasure of human art is in danger. Mural inpainting through deep learning can permanently preserve mural information. Therefore, a digital restoration method combining the Deformable Convolution (DCN), ECANet, ResNet and Cycle Generative Adversarial Network (CycleGAN) is proposed. We name it DC-CycleGAN. Compared with other image digital inpainting methods, the proposed DC-CycleGAN based mural image color inpainting method has better inpainting effects and higher model performance. Compared with the current repair network, the Frechet Inception Distance (FID) value and the two-image structural similarity metric (SSIM) value are increased by 52.61% and 7.08%, respectively. Image color inpainting of Dunhuang murals can not only protect and inherit Chinese culture, but also promote academic research and development in related fields.

Retracted: Image Color Recognition and Optimization Based on Deep Learning

Retracted: Image Color Recognition and Optimization Based on Deep Learning

From photographic images to hierarchical networks―Color associations of a traditional Chinese garden

AbstractThe complexity of the environment and the richness of landscape color make it difficult to objectively measure color in gardens. However, with the use of machine vision and image processing algorithms, it is now possible to accurately quantify the colors of the built environment through photo recognition, classification, enhancement, and segmentation. This approach can produce correlation intensity charts and hierarchical networks that illustrate theme colors and their associations. To explore this methodology, we have selected Beijing Xiangshan Temple―a traditional Chinese garden and temple with rich landscape colors―as the research object. Using a combination of machine vision image processing technology and color clustering algorithms, we aim to establish a landscape color analysis model based on the Munsell color system and color harmony theory. Ultimately, this will provide a quantifiable and objective reference basis for the design and repair of temple garden colors, with a view to achieving the inheritance and development of classical gardens.

Correlation of the Grapevine (Vitis vinifera L.) Leaf Chlorophyll Concentration with RGB Color Indices

Spectral investigation of the canopy has an increasing importance in precision viticulture to monitor the effect of biotic and abiotic stress factors. In this study, RGB (color model, red, green, blue)-based vegetation indices were evaluated to find a correlation with grapevine leaf chlorophyll concentration. ‘Hárslevelű’ (Vitis vinifera L.) leaf samples were obtained from a commercial vineyard and digitalized. The chlorophyll concentration of the samples was determined with a portable chlorophyll meter. Image processing and color analyses were performed to determine the RGB average values of the digitized samples. According to the RGB values, 31 vegetation indices were calculated and evaluated with a correlation test and multivariate regression. The Pearson correlation between the chlorophyll concentration and most of the indices was significant (p < 0.01), with some exceptions. The same results were obtained with the Spearman correlation as the relationship had high significance (p < 0.01) for most of the indices. The highest Pearson correlation was obtained with the index PCA2 (Principal Component Analysis 2), while Spearman correlation was the highest for RMB (difference between red and blue) and GMB (difference between green and blue). The multivariate regression model also showed a high correlation with the pigmentation. We consider that our results would be applicable in the future to receive information about the canopy physiological status monitored with on-the-go sensors.

Caster: Cartoon style transfer via dynamic cartoon style casting

The cartoon style transfer has attracted widespread attention. Although many researchers have proposed various methods to develop this field, there are still two areas for improvement: (1) existing image-to-image cartoon style transfer methods can only perform domain-to-domain cartoon style transfer, neglecting the specific color and texture of the cartoon images, and (2) arbitrary style transfer methods only transfer the style of the style image onto the content image, neglecting the style information of the style domain. To address these issues, we observe that artists often refer to specific paintings to fine-tune the color of their artworks. This behavior inspires us to propose a method that can dynamically encode the style information of a specific cartoon image based on Variational Autoencoders, allowing the style feature to be cast onto the content feature dynamically. We also introduce a cartoon contrastive learning loss to push the cartoon stylized image closer to the same cartoon stylized image and otherwise pull away. Extensive experiments demonstrate that our proposed method, Caster, can generate a high-quality stylized image with specific and domain cartoon style information than state-of-the-art cartoon style transfer methods.

Image analysis quantification of sperm acrosome associated 3 protein expression in domesticated and free‐roaming equine ovaries

SummaryBackgroundIn the United States, the carrying capacity of the land has been overwhelmed with the wild horse and burro populations. Identification of a long‐term contraception method is needed to control growing populations as current methods cannot quell these growing numbers. Sperm acrosome‐associated 3 protein (SPACA3) is a protein expressed in both male and female reproductive systems. SPACA3 may prove to be a viable contraception target in horses in immunosterilant research.ObjectivesThe objective of the current study was to evaluate and compare ovarian immunoexpression of SPACA3 between domesticated and free‐roaming mares. This information may lead to the development of a SPACA3 immunosterilant to reduce free‐roaming horse and burro populations.Study designNon‐randomised comparative study design.MethodsRoutine immunohistochemistry was performed on ovarian sections from domesticated (n = 8) and free‐roaming mares (n = 8). At least three representative images of each follicle stage (primordial, primary, secondary, tertiary) from each ovary were digitally captured and analysed using Image J software. Expression of SPACA3 was quantified in cellular locations (granulosa or theca) specific to each follicle stage: primordial, primary, secondary, and tertiary.ResultsDomesticated horses had higher SPACA3 expression compared to free‐roaming mares in granulosa cells of primordial (p = 0.0022), primary (p < 0.001), secondary (p = 0.025), and tertiary follicles (p = 0.0078). In theca cells, domesticated horses had higher SPACA3 expression in tertiary follicles (p = 0.022), but not secondary follicles (p = 0.089). The highest SPACA3 expression was observed in the granulosa cells of tertiary follicles (p = 0.022).Main limitationsVarying background colours of representative images taken of ovaries, leading to slight technical issues in Image J software.ConclusionsThis is the first report quantifying SPACA3 immunoexpression during folliculogenesis and describing the spatial and temporal expression of SPACA3 in the equine ovary. This information may lead to the development of a SPACA3 immunosterilant to reduce free‐roaming horse and burro populations.

Unsupervised anomaly appraisal of cleft faces using a StyleGAN2-based model adaptation technique.

A novel machine learning framework that is able to consistently detect, localize, and measure the severity of human congenital cleft lip anomalies is introduced. The ultimate goal is to fill an important clinical void: to provide an objective and clinically feasible method of gauging baseline facial deformity and the change obtained through reconstructive surgical intervention. The proposed method first employs the StyleGAN2 generative adversarial network with model adaptation to produce a normalized transformation of 125 faces, and then uses a pixel-wise subtraction approach to assess the difference between all baseline images and their normalized counterparts (a proxy for severity of deformity). The pipeline of the proposed framework consists of the following steps: image preprocessing, face normalization, color transformation, heat-map generation, morphological erosion, and abnormality scoring. Heatmaps that finely discern anatomic anomalies visually corroborate the generated scores. The proposed framework is validated through computer simulations as well as by comparison of machine-generated versus human ratings of facial images. The anomaly scores yielded by the proposed computer model correlate closely with human ratings, with a calculated Pearson's r score of 0.89. The proposed pixel-wise measurement technique is shown to more closely mirror human ratings of cleft faces than two other existing, state-of-the-art image quality metrics (Learned Perceptual Image Patch Similarity and Structural Similarity Index). The proposed model may represent a new standard for objective, automated, and real-time clinical measurement of faces affected by congenital cleft deformity.

Insights into surface chemistry down to nanoscale: An accessible colour hyperspectral imaging approach for scanning electron microscopy

Chemical imaging (CI) is the spatial identification of molecular chemical composition and is critical to characterising the (in-) homogeneity of functional material surfaces. Nanoscale CI on bulk functional material surfaces is a longstanding challenge in materials science and is addressed here.We demonstrate the feasibility of surface sensitive CI in the scanning electron microscope (SEM) using colour enriched secondary electron hyperspectral imaging (CSEHI). CSEHI is a new concept in the SEM, where secondary electron emissions in up to three energy ranges are assigned to RGB (red, green, blue) image colour channels. The energy ranges are applied to a hyperspectral image volume which is collected in as little as 50 s. The energy ranges can be defined manually or automatically.Manual application requires additional information from the user as first explained and demonstrated for a lithium metal anode (LMA) material, followed by manual CSEHI for a range of materials from art history to zoology.We introduce automated CSEHI, eliminating the need for additional user information, by finding energy ranges using a non-negative matrix factorization (NNMF) based method. Automated CSEHI is evaluated threefold: (1) benchmarking to manual CSEHI on LMA; (2) tracking controlled changes to LMA surfaces; (3) comparing automated CSEHI and manual CI results published in the past to reveal nanostructures in peacock feather and spider silk. Based on the evaluation, CSEHI is well placed to differentiate/track several lithium compounds formed through a solution reaction mechanism on a LMA surface (eg. lithium carbonate, lithium hydroxide and lithium nitride). CSEHI was used to provide insights into the surface chemical distribution on the surface of samples from art history (mineral phases) to zoology (di-sulphide bridge localisation) that are hidden from existing surface analysis techniques. Hence, the CSEHI approach has the potential to impact the way materials are analysed for scientific and industrial purposes.