Red Green Blue Color Space Research Articles

Underwater image enhancement using contrast correction

AbstractLight‐induced degeneration of underwater images occurs by physical features of seawater. According to the wavelength of the colour spectrum, light reduces intensity significantly when it moves through water. The greatest wavelength of light that is visible gets absorbed first. Red and blue absorb the most and least, respectively. Because of the reducing consequences of the light spectrum, underwater images having poor contrast can be obtained. As a result, the crucial data contained inside these images will not be effectively retrieved for later analysis. The recent research suggests a novel approach to enhance the contrast while decreasing noise in underwater images. The recommended approach involves image histogram transformation using two significant colour spaces, Red‐Green‐Blue (RGB) and Hue‐Saturation‐Value (HSV). The histogram of the dominant colour channel (blue channel) in the RGB colour model is extended towards the lower level, containing a maximum limitation of 95%, while the inferior red colour channel has been extended towards the upper side, containing a minimum limitation of 5%. During the entire dynamic range, the green colour channel having the dominant and inferior colour channels expands in each direction. The Rayleigh distribution has been utilized for developing various stretching actions within the RGB colour space. The image has been converted to the HSV colour space, having the S and V elements adjusted within 1% of their minimum and maximum values. The suggested approach is examined in both qualitative and quantitative analysis. According to qualitative analysis, the recommended approach substantially boosts image contrast, lowers its blue and green effect, and minimizes over‐enhanced and under‐enhanced sections in the final resultant underwater image. The quantitative examination of 500 large scale underwater images dataset reveals that the suggested technique generates better results. The dataset images are grouped into small fish images, blue coral images, stone wall images, and coral branch images. The quantitative examination of all these four groups have been evaluated and shown. The average mean square error, peak signal to noise ratio, underwater image quality measurement, and underwater colour image quality evaluation values of dataset images are 76.69, 31.25, 3.85, and 0.64, respectively. These values of our proposed work outperform six other previous methods.

Evaluation of semi-quantitative colorimetric assays based on loop-mediated isothermal amplification indicators by using image analysis

Colorimetric assays are some of the most convenient detection methods, creating discoloration in solutions that is visible to the naked eye. However, colorimetric reactions have some limitations regarding the variability in the color perception of individuals caused by factors such as color blindness, experience, and gender. Semi-quantitative chromatic analysis has been used as an alternative method to differentiate between two colors and accurately interpret the results from a numerical value, with high confidence. Therefore, we developed and determined the optimal model between Red-Green-Blue (RGB) and Commission Internationale de l'Eclairage (CIE) Lab color spaces to establish a semi-quantitative colorimetric assay via image analysis by the ImageJ program for loop-mediated isothermal amplification (LAMP), using the dyes malachite green and phenol red. The semi-quantitative colorimetric assays using the color distance values of the CIELab color space (ΔEab) were more suitable than those using the RGB color space (ΔERGB) for chromatic differentiation between positive and negative reactions in both indicator dyes, demonstrating the feasibility of this assay to be applied in the detection of a wide range of pathogens and infectious diseases.

I2-FaçadeNet: An Illumination-invariant Façade Recognition Network Leveraging Sparsely Gated Mixture of Multi-color Space Experts for Aerial Oblique Imagery

Façade image recognition under complex illumination conditions is crucial for various applications, including urban three-dimensional modeling and building identification. Existing methods relying solely on Red-Green-Blue (RGB) images are prone to texture ambiguity in complex illumination environments. Furthermore, façades display varying orientations and camera viewing angles, resulting in performance issues within the RGB color space. In this study, we introduce an illumination-invariant façade recognition network (I2-FaçadeNet) that leverages sparsely gated multi-color space experts for enhanced façade image recognition in challenging illumination environments. First, RGB façade images are converted into multi-color spaces to eliminate the ambiguous texture in complex illumination. Second, we train expert networks using separate channels of multi-color spaces. Finally, a sparsely gated mechanism is introduced to manage the expert networks, enabling dynamic activation of expert networks and the merging of results. Experimental evaluations leveraging both the International Society for Photogrammetry and Remote Sensing benchmark data sets and the Shenzhen data sets reveal that our proposed I2 -FaçadeNet surpasses various depths of ResNet in façade recognition under complex illumination conditions. Specifically, the classification accuracy for poorly illuminated façades in Zurich improves by nearly 8%, while the accuracy for over-illuminated areas in Shenzhen increases by approximately 3%. Moreover, ablation studies conducted on façade images with complex illumination indicate that compared to traditional RGB-based ResNet, the proposed network achieves an accuracy improvement of 3% to 4% up to 100% for overexposed images and an accuracy improvement of 3% to 10% for underexposed images.

A New Remote Sensing Desert Vegetation Detection Index

Land desertification is a key environmental problem in China, especially in Northwest China, where it seriously affects the sustainable development of natural resources. In this paper, we combine high-resolution satellite remote sensing images and UAV (unmanned aerial vehicle) visible light images to extract desert vegetation data and quickly locate and accurately monitor land desertification in relevant areas according to changes in vegetation coverage. Due to the strong light and dry climate of deserts in Northwest China, which results in deeper vegetation shadow texture and mostly dry shrubs with fewer stems and leaves, the accuracy of the vegetation index commonly used in visible remote sensing image classification is not able to meet the requirements for monitoring and evaluating land desertification. For this reason, in this paper, we took the Hangjin Banner in Bayannur as an example and constructed a new vegetation index, the HSVGVI (hue–saturation–value green enhancement vegetation index), based on the HSV (hue–saturation–value) color space using channel enhancement that can improve the extraction accuracy of desert vegetation and reduce misclassification. In addition, in order to further test the extraction accuracy, samples of densely vegetated and multi-shaded areas were divided in the study area according to the accuracy-influencing factors. At the same time, the HSVGVI was compared with the vegetation indices EXG (excess green index), RGBVI (red–green–blue vegetation index), MGRVI (modified green–red vegetation index), NGBDI (normalized green–red discrepancy index), and VDVI (visible-band discrepancy vegetation index) constructed based on the RGB (red–green–blue) color space. The experimental results show that the extraction accuracy of the EXG and other vegetation indices constructed in RGB color space can only reach 70%, while the extraction accuracy of the HSVGVI can reach more than 95%. In summary, the HSVGVI proposed in this paper can better realize the extraction of desert vegetation data and can provide a reliable technical tool for monitoring and evaluating land desertification.

Supra-Quantum Dot Assemblies to Maximize Color-Based Multiplexed Fluorescence Detection with a Smartphone Camera.

Photoluminescence (PL) imaging and bioanalysis with smartphone-based devices are of growing interest for point-of-care/point-of-need diagnostics. Strategies for maximizing sensitivity have been explored in this context, but color multiplexing has been very limited, with its maximum level unexplored. Here, we evaluated color multiplexing with smartphone-based PL imaging by using supra-nanoparticle assemblies of quantum dots (supra-QDs). These materials were prepared as composite colors that were tailored to the red-green-blue (RGB) color space of smartphone cameras by coassembling different ratios of R-, G-, and B-emitting QDs on a silica nanoparticle scaffold. The supra-QDs were characterized and used to label cell-sized objects that were measured under flow with a smartphone-based device. Each color followed an approximately linear trajectory in the RGB space, and training of support vector machine models enabled color classification with overall accuracies ≥87% for 10-color multiplexing and better accuracies for fewer colors. Most misclassification occurred at low signal levels, such that establishing a nonclassifiable zone near the origin of RGB color space improved the overall 10-color classification accuracy to ≥94%. Similar improvements in accuracy with greater retention of data were possible with a probabilistic rather than a radial threshold. Simulations that were parameterized by experimental data suggested that ≥14-color multiplexing with accuracies ≥90% should be possible with an optimized supra-QD color set. This study is an important foundation for advancing RGB color-based multiplexing for imaging and analyses with smartphone cameras and related charge-coupled device and CMOS color image sensor technologies.

Brain activity characteristics of RGB stimulus: an EEG study

The perception of color is a fundamental cognitive feature of our psychological experience, with an essential role in many aspects of human behavior. Several studies used magnetoencephalography, functional magnetic resonance imaging, and electroencephalography (EEG) approaches to investigate color perception. Their methods includes the event-related potential and spectral power activity of different color spaces, such as Derrington-Krauskopf-Lennie and red-green-blue (RGB), in addition to exploring the psychological and emotional effects of colors. However, we found insufficient studies in RGB space that considered combining all aspects of EEG signals. Thus, in the present study, focusing on RGB stimuli and using a data-driven approach, we investigated significant differences in the perception of colors. Our findings show that beta oscillation of green compared to red and blue colors occurs in early sensory periods with a latency shifting in the occipital region. Furthermore, in the occipital region, the theta power of the blue color decreases noticeably compared to the other colors. Concurrently, in the prefrontal area, we observed an increase in phase consistency in response to the green color, while the blue color showed a decrease. Therefore, our results can be used to interpret the brain activity mechanism of color perception in RGB color space and to choose suitable colors for more efficient performance in cognitive activities.

A Hybrid Method for Enhancement of Both Contrast Distorted and Low-Light Images

Many different histogram equalization (HE)-based image enhancement methods have been developed to overcome the problems of low or high image brightness, contrast sensitivity, and difficulty in revealing details of dark areas under low-light environments. In this paper, a novel image enhancement method based on HE and adaptive gamma correction with weight distribution (AGCWD) is proposed for natural and effective image enhancement. In this method, histogram stretching is performed on Red–Green–Blue (RGB) color components of image, and then the color space of RGB image is converted to Hue–Saturation–Intensity (HSI) color space. The histograms of S and I components are divided into sub-histograms according to the exposure threshold. The underexposure regions are enhanced with a new AGCWD. Then, the color space of HSI image is converted to RGB color space. Finally, the HE is applied to the input image with the obtained image histogram map. Thus, the method has not only effectively prevented the over-enhancement of the contrast but also obtained the quality and natural enhanced image. The proposed method is compared with the most known contrast enhancement methods and low-light enhancement methods. Experimental results have supported that the proposed method outperforms other methods in terms of both visual perception and objective evaluation.

A dichotomy color quantization algorithm for the HSI color space

Color quantization is used to obtain an image with the same number of pixels as the original but represented using fewer colors. Most existing color quantization algorithms are based on the Red Green Blue (RGB) color space, and there are few color quantization algorithms for the Hue Saturation Intensity (HSI) color space with a simple uniform quantization algorithm. In this paper, we propose a dichotomy color quantization algorithm for the HSI color space. The proposed color quantization algorithm can display images with a smaller number of colors than other quantization methods of RGB color space. The proposed algorithm has three main steps as follows: first, a single-valued monotonic function of the Hue (H) component in the from RGB color space to HSI color space (RGB-HSI) color space conversion is constructed, which can avoid the partition calculation of the H component in the RGB-HSI color space; second, an iterative quantization algorithm based on the single-valued monotonic function is proposed; and third, a dichotomy quantization algorithm is proposed to improve the iterative quantization algorithm. Both visual and numerical evaluations reveal that the proposed method presents promising quantization results.

Monitoring Postharvest Color Changes and Damage Progression of Cucumbers Using Machine Vision

To monitor cucumbers' external quality, such as color changes or the presence of any damage during storage, a machine vision system was used. Red, Green, Blue (RGB) images were acquired in a "soft box," which provided a highly diffused lighting scene for observing visual changes such as color and appearance in the skin of cucumber. The RGB images were transformed into L*, a*,b*, and HSV spaces. Histograms for each channel in each color space were evaluated for image segmentation, and the blue (B) channel in the RGB color space was found superior in terms of measuring damage progression. Damage progression plots (DPP) were made from accumulated grayscale images in each of the color channels and to observe variation over time, absolute differential damage progression (ADDP) plots were generated. Overall, the order of channel utility was [B], [R, G, V], and [H, S, L*, a*, b*]. To assess which channel, in which colorspace, was most sensitive, i.e., could capture most of the information regarding day-to-day color changes, a principal component analysis (PCA) was performed. The PCA showed that all individual components in the RGB color space were suitable for obtaining information about the external changes of cucumber. Based on the results, the machine vision approach is recommended as a non-destructive technique for monitoring the external quality of stored fresh produce.

An evidential combination method with multi-color spaces for remote sensing image scene classification

Remote sensing image scene classification aims to commit the semantic labels according to the content of images. Convolutional Neural Network (CNN) is often used here to extract deep discriminative feature of remote sensing images for classification. In practice, CNN is usually trained by images in the Red Green Blue (RGB) color space. Whereas, CNN also can be trained by images in some other color spaces, e.g., Hue Saturation Value. The CNN models trained by images in diverse color spaces will perform differently because different color spaces often emphasize diverse color information. Thus, we present an Evidential Combination method with Multi-color Spaces (ECMS) to integrate the complementary information of different color spaces for classification performance improvement. In ECMS, labeled remote sensing images in the RGB color space are first converted into other color spaces, and then they are used to train CNN models, respectively. The soft classification results (of query images) yielded by these CNN models are combined by evidence theory. During fusion, the reliabilities/weights of these outputs of different CNN models are usually different, so they should not be equally treated for combination. In our approach, the weights are learnt by minimizing the mean squared error between the combination results and ground truth on labeled images. By doing this, weighted evidence combination of soft classification results is employed to make scene class decision. We conducted experiments on several datasets to verify the effectiveness of ECMS, and the results show ECMS can significantly improve classification accuracy compared with many existing methods.

A multimodal molecular image fusion method based on relative total variation and co‐saliency detection

Image fusion can integrate complementary information from multimodal molecular images to provide an informative single result image. In order to obtain a better fusion effect, this article proposes a novel method based on relative total variation and co-saliency detection (RTVCSD). First, only the gray-scale anatomical image is decomposed into a base layer and a texture layer according to the relative total variation; then, the three-channel color functional image is transformed into the luminance and chroma (YUV) color space, and the luminance component Y is directly fused with the base layer of the anatomical image by comparing the co-saliency information; next, the fused base layer is linearly combined with the texture layer, and the obtained fused result is combined with the chroma information U and V of the functional image. Finally, the fused image is obtained by transforming back to the red–green–blue color space. The dataset consists of magnetic resonance imaging (MRI)/positron emission tomography images, MRI/single photon emission computed tomography (SPECT) images, computed tomography/SPECT images, and green fluorescent protein/phase contrast images, each category with 20 image pairs. Experimental results demonstrate that the proposed method RTVCSD outperforms the nine comparison algorithms in terms of visual effects and objective evaluation. RTVCSD well preserves the texture information of the anatomical image and the metabolism or protein distribution information of the functional image.

Soft sensor of iron tailings grade based on froth image features for reverse flotation

In the iron reverse flotation production process, the amount of flotation agent and the quality of flotation products are usually judged according to the grade of tailings, so it is essential to measure the grade of tailings froth. This research applies computer vision and image feature extraction technology to the soft sensor of tailings froth grade. An adaptive selection method for the image target region is proposed. The relationship between RGB (Red, Green, Blue), HSI (Hue, Saturation, Intensity), and Lab color space and tailings grade of reverse flotation in iron mine has been analyzed. A new image feature is proposed to characterize the degree of froth mineralization. The RGB and HSI dual color space feature values and froth mineralization degree values are determined as input, and the tailing grade soft sensor model is established by the multilayer feedforward perceptrons and VGG-19 neural network. A tailings grade soft sensor system has been developed and applied in a flotation workshop. The results of industrial tests show that this method is efficient and reliable.

Salient Object Detection by LTP Texture Characterization on Opposing Color Pairs under SLICO Superpixel Constraint.

The effortless detection of salient objects by humans has been the subject of research in several fields, including computer vision, as it has many applications. However, salient object detection remains a challenge for many computer models dealing with color and textured images. Most of them process color and texture separately and therefore implicitly consider them as independent features which is not the case in reality. Herein, we propose a novel and efficient strategy, through a simple model, almost without internal parameters, which generates a robust saliency map for a natural image. This strategy consists of integrating color information into local textural patterns to characterize a color micro-texture. It is the simple, yet powerful LTP (Local Ternary Patterns) texture descriptor applied to opposing color pairs of a color space that allows us to achieve this end. Each color micro-texture is represented by a vector whose components are from a superpixel obtained by the SLICO (Simple Linear Iterative Clustering with zero parameter) algorithm, which is simple, fast and exhibits state-of-the-art boundary adherence. The degree of dissimilarity between each pair of color micro-textures is computed by the FastMap method, a fast version of MDS (Multi-dimensional Scaling) that considers the color micro-textures’ non-linearity while preserving their distances. These degrees of dissimilarity give us an intermediate saliency map for each RGB (Red–Green–Blue), HSL (Hue–Saturation–Luminance), LUV (L for luminance, U and V represent chromaticity values) and CMY (Cyan–Magenta–Yellow) color space. The final saliency map is their combination to take advantage of the strength of each of them. The MAE (Mean Absolute Error), MSE (Mean Squared Error) and F measures of our saliency maps, on the five most used datasets show that our model outperformed several state-of-the-art models. Being simple and efficient, our model could be combined with classic models using color contrast for a better performance.

Gain Bandwidth Engineering in Polymer Blends for Full‐Color‐Tunable Lasers

AbstractDeveloping semiconductors with broad gain bandwidth has always been at the forefront of laser technologies. The variation in feedback resonators can provide a useful tool for producing a relatively wide range of discrete lasing wavelengths. However, the lasing wavelength range is limited by the fundamental gain bandwidth of the semiconductor itself. Gain bandwidth engineering for full‐color range lasing though remains a daunting challenge. Considering the abundant energy levels of organic semiconductors, the authors stride over the barrier of the gain bandwidth limitation and demonstrate the dynamically tunable amplification/lasing across the entire emission range by leveraging on Förster resonance energy transfer (FRET)‐assisted guest–host blends. The unprecedented tunability in amplification and lasing is governed by energy transfer process, which enables them to achieve wavelength‐tunable semiconductor lasers spanning the full visible region of the electromagnetic spectrum. Their distributed feedback (DFB) lasers using these guest–host blends as gain media cover almost all CIE color gamut (94%), which is 170% more perceptible colors than standard Red Green Blue color space. These insights can guide the versatile and convenient design of organic semiconductor materials transcending the current gain bandwidth limitation, paving the way for next generation of optoelectronic devices.

Image Analysis for Rapid Assessment and Quality-Based Sorting of Corn Stover

Imaging in the visible spectrum is a low-cost tool that can be readily deployed for in-field or over-belt monitoring of biomass quality for bio-refining operations. Rapid image analysis coupled with innovative preprocessing may reduce the impacts of feedstock variability through identification of contaminants or other material attributes to guide selective sorting and quality management. Image analysis was employed to evaluate the quality of corn stover in red-green-blue (RGB) chromatic space. This study used controlled, bench-scale imaging as a proof-of-concept for rapid quality assessment of corn stover based on variations in material attributes, including chemical and physical attributes, that relate to biological degradation and soil contamination. Logistic regression-based classification algorithms were used to develop a method for biomass screening as a function of biological degradation or soil contamination. This study demonstrated the use of image analysis to extract features from RGB color space to investigate variations in critical material attributes from chemical composition of corn stover. Fourier transform infrared (FT-IR) suggested a correlation between red band intensity and biological degradation, while detailed surface texture analysis was found to distinguish among variations in ash. These insights offer promise for development of a rapid screening tool that could be deployed by farmers for in-field assessment of biomass quality or biorefinery operators for in-line sorting and process optimization.

Identification of the Yield of Camellia oleifera Based on Color Space by the Optimized Mean Shift Clustering Algorithm Using Terrestrial Laser Scanning

Oil tea (Camellia oleifera) is one of the world’s major woody edible oil plants and is vital in providing food and raw materials and ensuring water conservation. The yield of oil tea can directly reflect the growth condition of oil tea forests, and rapid and accurate yield measurement is directly beneficial to efficient oil tea forest management. Light detection and ranging (LiDAR), which can penetrate the canopy to acquire the geometric attributes of targets, has become an effective and popular method of yield identification for agricultural products. However, the common geometric attribute information obtained by LiDAR systems is always limited in terms of the accuracy of yield identification. In this study, to improve yield identification efficiency and accuracy, the red-green-blue (RGB) and luminance-bandwidth-chrominance (i.e., YUV color spaces) were used to identify the point clouds of oil tea fruits. An optimized mean shift clustering algorithm was constructed for oil tea fruit point cloud extraction and product identification. The point cloud data of oil tea trees were obtained using terrestrial laser scanning (TLS), and field measurements were conducted in Changsha County, central China. In addition, the common mean shift, density-based spatial clustering of applications with noise (DBSCAN), and maximum–minimum distance clustering were established for comparison and validation. The results showed that the optimized mean shift clustering algorithm achieved the best identification in both the RGB and YUV color spaces, with detection ratios that were 9.02%, 54.53%, and 3.91% and 7.05%, 62.35%, and 10.78% higher than those of the common mean shift clustering, DBSCAN clustering, and maximum-minimum distance clustering algorithms, respectively. In addition, the improved mean shift clustering algorithm achieved a higher recognition rate in the YUV color space, with an average detection rate of 81.73%, which was 2.4% higher than the average detection rate in the RGB color space. Therefore, this method can perform efficient yield identification of oil tea and provide a new reference for agricultural product management.

A Combined Immunofluorescence and Fluorescent Viability Cocktail Staining Procedure for Rapid Microscopic Detection and Enumeration of Live Legionella pneumophila.

This report describes a combined immunofluorescence and fluorescence viability stain applied as one staining solution for rapid detection oflive Legionella pneumophila in mixed bacterial populations. Instead of sequential viability staining with the Invitrogen BacLight LIVE/DEAD staining kit followed by antibody-Alexa Fluor (AF) 647 conjugate staining to identify liveL. pneumophila, a combined single cocktail solution staining protocol was developed to simplify and accelerate the time to detection of viable L. pneumophila serogroup-1 (SG-1) in mixed species populations on a filter membrane. The stain cocktail will aid in accelerating fluorescence microscopic analysis of cooling tower, air conditioner and water fountain or other liquid samples for the presence of L. pneumophila and its viability status. Visibly red stained cells were identified as dead non-L. pneumophila SG-1 cells, while green fluorescing cells represented viable non-L. pneumophila SG-1 cells. Due to also staining red with antibody-AF 647, L. pneumophila SG-1 cells were pseudocolorized as blue to distinguish them from other dead cells. Fluorescence color emission mixing from the viability dyes (SYTO 9 and propidium iodide) with antibody-AF 647 stained L. pneumophila led to other fluorescent colors. For example, green plus pseudocolorized blue AF 647-antibody- labeled cells were identified as live cyan-colored L. pneumophila SG-1 cells. Magenta-colored cells resulted from dead L. pneumophila cells that combined red propidium iodide with blue pseudocolorized AF 647-antibody emissions. Analysis of measured RGB (red, green, blue) color values in microscopic images of mixed bacterial populations suggests the possibility of facile automated discrimination of subpopulations of live and dead L. pneumophila and non-L. pneumophila species by computers in 3-dimensional RGB color space after staining in the combined cocktail which will save time for more rapid microscopic detection of potential sources of Legionnaire's disease.

Introducing new methods based on the standard ISO/IEC 24790 to evaluate graininess for coloured printed images

AbstractPrinting quality control has been one of the challenges in the printing industry. Therefore, it is indispensable to find evaluation methods with high correlations with the human visual system to assess the quality of the printed images. Standard ISO/IEC 24790 is one of the recommended standards to achieve this goal. An evaluation procedure for graininess, a common defect in printing, has been presented in this standard; however, this procedure is suggested only for monochrome printed images. Hence, in this research, the graininess of coloured samples is evaluated based on the proposed method in this standard with considering the effect of the paper. For this purpose, fictitious graininess defects are designed and applied on coloured samples and are printed on different papers. A visual assessment is performed to examine the effect of colour and paper differences on graininess. Six different methods, including the method suggested in the standard and the proposed modified procedures, are used to evaluate graininess, and their results are compared to subjective results. Moreover, three other wavelets are studied besides the suggested one in the standard. To eliminate the effect of paper texture, a Gaussian filter is utilised. The results show that applying the Gaussian filter to images increases the correlation between computational procedure results and visual assessment results. Also, the results indicate that utilising haar wavelet slightly increases the correlation. The results demonstrate that besides assessing graininess in greyscale mode, evaluating graininess in RGB (red, green, blue) colour space results in a high correlation with the visual assessment results.

Retinex-Based Fast Algorithm for Low-Light Image Enhancement.

We proposed the Retinex-based fast algorithm (RBFA) to achieve low-light image enhancement in this paper, which can restore information that is covered by low illuminance. The proposed algorithm consists of the following parts. Firstly, we convert the low-light image from the RGB (red, green, blue) color space to the HSV (hue, saturation, value) color space and use the linear function to stretch the original gray level dynamic range of the V component. Then, we estimate the illumination image via adaptive gamma correction and use the Retinex model to achieve the brightness enhancement. After that, we further stretch the gray level dynamic range to avoid low image contrast. Finally, we design another mapping function to achieve color saturation correction and convert the enhanced image from the HSV color space to the RGB color space after which we can obtain the clear image. The experimental results show that the enhanced images with the proposed method have better qualitative and quantitative evaluations and lower computational complexity than other state-of-the-art methods.

A Method of Segmenting Apples Based on Gray-Centered RGB Color Space

In recent years, many agriculture-related problems have been evaluated with the integration of artificial intelligence techniques and remote sensing systems. The rapid and accurate identification of apple targets in an illuminated and unstructured natural orchard is still a key challenge for the picking robot’s vision system. In this paper, by combining local image features and color information, we propose a pixel patch segmentation method based on gray-centered red–green–blue (RGB) color space to address this issue. Different from the existing methods, this method presents a novel color feature selection method that accounts for the influence of illumination and shadow in apple images. By exploring both color features and local variation in apple images, the proposed method could effectively distinguish the apple fruit pixels from other pixels. Compared with the classical segmentation methods and conventional clustering algorithms as well as the popular deep-learning segmentation algorithms, the proposed method can segment apple images more accurately and effectively. The proposed method was tested on 180 apple images. It offered an average accuracy rate of 99.26%, recall rate of 98.69%, false positive rate of 0.06%, and false negative rate of 1.44%. Experimental results demonstrate the outstanding performance of the proposed method.