Monochrome Images Research Articles

An Efficient Noise Separation Technique for Removal of Gaussian and Mixed Noises in Monochrome and Color Images

Images are often affected by different kinds of noise while acquiring, storing and transmitting it. Even the datasets gathered by the various image acquiring devices would be contaminated by noise. Hence, there is a need for noise reduction in the image, often called Image De-noising and thereby it becomes the significant concerns and fundamental step in the area of image processing. During image de-noising, the big challenge before the researchers is removing noise from the original image in such a way that most significant properties like edges, lines, etc., of the image, should be preserved. There were various published algorithms and techniques to de-noise the image and every single approach has its own limitations, benefits, and assumptions. This paper reviews the noise models and presents a comparative analysis of various de-noising filters that works for color images with single and mixed noises. It also suggests the best filter for color that involve in producing a high-quality color image. The metrics like PSNR, Entropy, SSIM, MSE, FSIM, and EPI are considered as image quality assessment metric

Hyperspectral Imaging Bioinspired by Chromatic Blur Vision in Color Blind Animals

Hyperspectral imaging remote sensing is mutually restricted in terms of spatial and spectral resolutions, signal-to-noise ratio and exposure time. To deal with this trade-off properly, it is beneficial for imaging systems to have high light flux. In this paper, we put forward a novel hyperspectral imaging method with high light flux bioinspired by chromatic blur vision in color blind animals. We designed a camera lens with high degree of longitudinal chromatic aberration, a monochrome image sensor captured the chromatic blur images at different focal lengths. Finally, by using the known point spread functions of the chromatic blur imaging system, we process these chromatically blurred images by deconvolution based on singular value decomposition inverse filtering, and the spectral images of a target were restored. We constructed three different targets for validating image restoration based on a typical octopus eyeball imaging system. The results show that the proposed imaging method can effectively extract spectral images from the chromatically blurred images. This study can facilitate development of a novel bionic hyperspectral imaging, which may benefit from the high light flux of a large aperture and provide higher detection sensitivity.

Auto-generation of centerline graphs from geometrically complex roadmaps of real-world traffic systems using hierarchical quadtrees for cellular automata simulations

This paper proposes a method for auto-generating centerline graphs from geometrically complex roadmaps of real-world traffic systems for cellular automata simulations, using hierarchical quadtrees. Our method is summarized as follows: First, we store the binary values of the monochrome image of the target roadmap (where one and zero represent the road and other areas, respectively) in a two-dimensional square map. Second, we recursively divide the square map into sub-leaves using a quadtree, until the sum of the values of pixels included inside each leaf becomes less than or equal to one. Third, we successively remove the distal leaves for which adjacent leaves have shallower depths. Fourth, we trace the remaining distal leaves of the tree using Morton’s space-filling curve, while selecting the leaves among those selected previously that preserve a certain distance as the nodes of the graph. Finally, each selected node searches the neighboring nodes, and these are stored as the edges of the graph. We demonstrate our method by generating a centerline graph from a complex roadmap of a real-world airport, and by performing a typical network analysis using Dijkstra’s method.

Multi-threshold object selection in images of remote sensing systems

The aim of the work is to increase the efficiency of selection of objects of different nature in digital monochrome images formed in remote sensing systems. For this purpose, algorithms for the formation of features of objects with respect to which boundary values are specified are introduced into the structure of multi-threshold processing. New schemes of multi-threshold processing and selection of objects of interest with threshold setting based on selection results are proposed. Algorithms of multi-threshold selection of objects by area and other scale-invariant geometric features, such as the elongation coefficient of the perimeter of the object and the elongation coefficient of the main axis of the describing ellipse, are obtained and tested. The binarization threshold is set for each of the selected objects based on the extremum of the applied geometric criterion. The new invariant geometric features used are different for round and elongated objects and provide independence of characteristics with changes in the image scale. Results of processing of typical models of images, and also results of selection of objects on the real television and infrared images showing efficiency of the proposed selection method are presented.

Robust Acoustic Event Classification using Fusion Fisher Vector features

In this paper, a novel Fusion Fisher Vector (FFV) features are proposed for Acoustic Event Classification (AEC) in the meeting room environments. The monochrome images of a pseudo-color spectrogram of an acoustic event are represented as Fisher vectors. First, irrelevant feature dimensions of each Fisher vector are discarded using Principal Component Analysis (PCA) and then, resulting Fisher vectors are fused to get FFV features. Performance of the FFV features is evaluated on acoustic events of UPC-TALP dataset in clean and different noisy conditions. Results show that proposed FFV features are robust to noise and achieve overall 94.32% recognition accuracy in clean and different noisy conditions.

RGB laser speckles based 3D profilometry

Laser speckle is becoming popular for encoding objects for 3D profile reconstructions. However, the measurement accuracy of laser speckles based stereophotogrammetry is often not high due to the subjective speckles formed on the sensor plane of the camera. In this letter, we propose a method to enhance the measurement accuracy by encoding the surface of the object using red, green, and blue (RGB) laser speckles which are generated when a combined RGB laser beam passes through a ground glass diffuser. A 3CCD color camera is utilized to capture the object from two different perspectives. Interestingly, there is no crosstalk between different channels. Hence, the proposed system is practically equivalent to three synchronized monochrome vision systems having separate red, green, and blue laser speckle projections. Correspondences located by the RGB speckle image correlation are more accurate, because the structured information in the color image is three times that in the monochrome image. Experimental results validate the higher accuracy of the proposed method against the conventional method using monochrome laser speckles. Moreover, the motion robustness of the proposed method is verified on the measurement of the moving objects.

Narrowband-autofluorescence imaging for bone analysis.

We present a new autofluorescence-imaging method for bone analysis. This method, based on the autofluorescence of bone, provides color images in microscopic scale. The color images are created from three monochrome images acquired with optimal excitation- and emission-wavelengths combinations. The choice of these combinations were determined from the study of two-dimensional distributions of bone-features-bispectral autofluorescence in the visible- and ultraviolet-spectral range. We demonstrate that main-bone features visualized with MG-staining method can also be visualized in the autofluorescence-color image. Furthermore, the autofluorescence-color image presents features hardly distinguished in a histological-bone section.

Development of the interscutularis model as an outcome measure for facial nerve surgery

IntroductionAnimal models for the study of facial paralysis have been well developed, but concern has arisen regarding the accuracy of eye closure and whisker movement as outcome measures due to new data regarding interconnectivity between facial nerve branches and autonomic innervation. The posterior auricular nerve (PAN) is an isolated branch of the facial nerve which has been confirmed as the sole motor innervat or of the interscutularis muscle. This study was designed to develop a model for facial nerve palsy utilizing the PAN and interscutularis muscle. MethodsA custom-made automated video capture system was built into a poly methyl methacrylate cage using a high definition monochrome digital camera and image sensor to record the animal as it drank from a water feeder. A copper floor pad and copper collar around the water feeder were connected to an electrical circuit for automatic saving of the video recording 10 s prior to and 30 s following the drinking event. A pre-operative baseline recording of ear movement during drinking was captured. Female YFP-16 mice at 6 weeks were assigned to sham (Sh, n = 5), nerve excision (Ex, n = 10), or nerve crush (Cr, n = 10) groups with all interventions performed on the right PAN. Sh mice were irrigated with 10 ml normal saline as were the Ex and Cr mice following operative intervention. In Ex mice, a 3 mm section of the PAN was sharply excised and nerve gap was confirmed with fluorescent microscopy. In Cr mice, the PAN was crushed 3 mm from the origin of the facial nerve trunk with size 5 jeweler’s forceps for two periods of 20 s. Post-operative video recordings were collected on post-operative days (POD) 1, 10, 20, and 30. To determine the change in ear movement, the right ear was graphically compared to the left control side. ResultsSh animals exhibited a statistically significant reduction in ear movement at POD01 compared to other POD recordings (p < 0.05), but no significant change in right ear movement following POD05. Ex animals had a significant reduction in right ear movement at all PODs in comparison to the left ear (p < 0.05) with no significant change in right ear movement during the study period (p = 0.94). Cr animals showed a significant reduction in right ear movement compared to the left at POD01, POD10, and POD20 (p < 0.05). At POD30, there was no significant difference between ear movement on either side (p = 0.35). There was a significant change in right ear movement during the data collection period (p < 0.05). ConclusionThe results show that significant differences were demonstrated between the experimental groups and that significant changes within the crush group were identifiable making this an acceptable model to develop as an accurate outcome measure following rodent facial nerve surgery.

A simple and reproducible method for production of protein nanoparticles at biological pH using egg white

Protein nanoparticles have been found to be of great interest as a carrier in a drug delivery system due to its biodegradability and non toxic nature. The purpose of the present investigation is to establish a simple and fast method for the preparation of stable egg white protein (EWP) nanoparticles. Desolvation process was adopted and the resulting nanoparticles were stabilized by a crosslinker, glutaraldehyde. To get the suitable and stable nanoparticles several process parameters such as pH, agitation speed, concentration of egg white, rate of addition of desolvating agent, gluteraldehyde concentration and addition of salt and buffer were examined. The minimum size of 112nm has been obtained at pH 9.0 when ethanol addition rate was 1ml/min at an agitation speed of 550 rpm. The size of nanoparticles is affected largely by pH of egg white while it is not significantly affected by the agitation speed and concentration of egg white and crosslinker. The SEM monochrome image of eggwhite nanoparticles displays the spherical shape with around 100nm size.

Orientation-Aware Saliency Guided JND Model for Robust Image Watermarking

The visual saliency-based just noticeable distortion (VS-JND) model has been widely used in the quantization-based watermarking framework, but it remains a challenge that how to reflect the characteristics of the human visual system (HVS) to the greatest extent in the processing of detecting salient objects. In this paper, given that the HVS has a different perception sensitivity in the different orientation of the image, a compound orientation feature map, which contains the vertical, horizontal, and diagonal information for directional patch extraction, is computed to improve the visual saliency map within just noticeable distortion (JND) profile. First, the DC and three low-frequency coefficients of each block are used to calculate the luminance and texture feature map, respectively. Then, the feature map based on the complex directional feature includes the horizontal, vertical, and diagonal information contained in the image is calculated by three low-frequency coefficients. Finally, we will be able to make a linear fusion of the same three different visual features to get the final VS map, and the JND model is improved according to the new VS model. Later, the new proposed VS-JND model is employed in the quantization watermarking (Q-watermarking) framework. The simulation results show that the proposed watermarking scheme is effective and produces superior robustness compared with existing monochrome image watermarking schemes.

Anchor Box Parameters and Bounding Box Overlap Ratios for the Faster R-CNN Detector in Detecting a Single Object by the Masking Background

Anchor box parameters and bounding box overlap ratios are studied in order to set them appropriately for the Faster R-CNN detector. The benchmark detection is based on monochrome images whose background may mask a small dark object. Three object detection tasks are generated, where every image either contains a small black square/rectangle or does not contain the object, representing thus class “background”. The ratios are recommended to be tried at 0.7 if this class is represented. The ratio for positive training samples is tried at a less value but greater than 0.4 for the task every image of which contains an object. The minimum anchor box size is better to try at a lesser value from a range of object sizes. The anchor box pyramid scale factor and the number of levels are better to try at 2 and 8, respectively. Subsequently, these parameters may be corrected as their influence is fuzzier than that of the ratios.

Super-resolution of single multi-color image with guided filter

Super-resolution reconstruction is a method that can transcend the limitations of optical imaging systems through the use of image processing algorithms. Recent techniques of super-resolution for single monochrome images develop rapidly, but for single multi-color images, to efficiently apply the monochrome super-resolution algorithms to all channels is still under exploration. In most of the recent research, the chromatic channels are simply upscaled by interpolation, which leads to the quality of the chromatic channels downgraded. This application may not be noticed by the visual systems of humans, but can affect other algorithms when super-resolution plays roles at image pre-processing. In this paper, we present a novel approach for multi-color super-resolution reconstruction. Using the super-resolution reconstructed luminance channel as the guide image, we adopt guided filters to manage the interpolated chromatic channels. Guided filters retain the sharp edges and fine details from the guided image and carry them to the output images. Meanwhile the whole process is quite computationally economic. Extensive experiments on natural images show that our method achieves better results than the method that is used in most of the algorithm in the literature in both statistic and visual aspects.

New set of multi-channel orthogonal moments for color image representation and recognition

Orthogonal moments and their invariants to similarity transformations for monochrome and gray-scale images are widely used in many pattern recognition and image processing applications. Quaternion orthogonal moments are used with color images. Recently, the multi-channel framework is proposed as a successful alternative of the quaternion orthogonal moments in representation and recognition of the color images. In this paper, a new set of multi-channel orthogonal moments and their invariants to rotation, scaling and translation (RST) is proposed for color image representation and recognition. The proposed multi-channel moments are based on the orthogonal radial substituted Chebyshev functions. The multi-channel orthogonal radial substituted Chebyshev moments (MORSCMs) are defined in polar coordinates over a unit circle. An accurate kernel-based method is utilized for accurate computation of the MORSCMs. A series of experiments is performed to validate this new set of multi-channel moments and compare its performance with the existing quaternion and multi-channel orthogonal moments. The obtained results ensure the superiority of the proposed MORSCMs over all existing moments in representation and recognition of the color images.

Efficient Multispectral Facial Capture With Monochrome Cameras

We propose a variant to polarized gradient illumination facial scanning which uses monochrome instead of color cameras to achieve more efficient and higher-resolution results. In typical polarized gradient facial scanning, sub-millimeter geometric detail is acquired by photographing the subject in eight or more polarized spherical gradient lighting conditions made with white LEDs, and RGB cameras are used to acquire color texture maps of the subject's appearance. In our approach, we replace the color cameras and white LEDs with monochrome cameras and multispectral, colored LEDs, leveraging that color images can be formed from successive monochrome images recorded under different illumination colors. While a naive extension of the scanning process to this setup would require multiplying the number of images by number of color channels, we show that the surface detail maps can be estimated directly from monochrome imagery, so that only an additional n photographs are required, where n is the number of added spectral channels. We also introduce a new multispectral optical flow approach to align images across spectral channels in the presence of slight subject motion. Lastly, for the case where a capture system's white light sources are polarized and its multispectral colored LEDs are not, we introduce the technique of multispectral polarization promotion, where we estimate the cross- and parallel-polarized monochrome images for each spectral channel from their corresponding images under a full sphere of even, unpolarized illumination. We demonstrate that this technique allows us to efficiently acquire a full color (or even multispectral) facial scan using monochrome cameras, unpolarized multispectral colored LEDs, and polarized white LEDs.

Dual Optimization of Monochrome Images Tone Approximation using Parallel Evolutionarily Genetic Search

The paper considers the optimization problem of tone approximation for monochrome (for example: in grayscale palette) images. The procedure of tone approximation implies the reduction of approximated image’s number of tones, which are used in image displaying, compared to number of tones in the original image. The point of the procedure optimization consists of minimization of visual quality loses that estimated according to total or mean deviation between the same pixels of original image and approximated one. As a tool of the optimization the hybrid algorithm is used. It was developed and investigated by authors. The hybrid algorithm combines heuristic and deterministic algorithms of searching the best structure of approximating palette according to criterion of deviations minimization. The heuristic algorithm is based on evolutionarily-genetic paradigm. The main goal of heuristic stage is the reduction of search area of approximating palette’s structures that are the closest to optimum. Such role for heuristic stage was defined according to its fast computational time. The goal of deterministic algorithm of directed exhaustive search is to find the nearest extreme for the result that was obtained by previous algorithm. The developed hybrid algorithm allows to provide dual optimization of tone approximation. It means that the algorithm provides a result, in which two different criteria become optimal relative to each other. The current investigation is devoted to consideration of possibility to increase the effectiveness of hybrid algorithm on the level of heuristic stage. The possibility of implementation the parallel model of evolutionarily-genetic algorithm with different settings is considered. The results of initial experiments are discussed and compared with known algorithm of tone approximation.

Open-Source High-Throughput Phenomics of Bacterial Promoter-Reporter Strains.

Open-source electronics are becoming more prevalent in biological sciences, enabling novel and unique means of data acquisition. Here, we present 3D-printed, open-source tools to acquire fluorescence phenotypes with high temporal resolution. Printed fluorescence imaging boxes (PFIboxes) cost approximately 200US dollars to assemble, can be placed in incubators or hypoxic chambers, and accurately read high-density colony arrays of microorganisms. We demonstrate the utility of PFIboxes using a timecourse gene expression approach, examining globalEscherichia coli promoter activity using a fluorescent reporter library across a diverse panel of 15 antibiotics, each at several concentrations. Many secondary and indirect effects were observed when E.coli was challenged with various drugs, including increased gene expression in carbon metabolism processes. Further, kinetic data acquisition enabled non-destructive time course gene expression, clustering of which revealed patterns of co-expression. In all, PFIboxes provide an open solution to gene expression, for about 2US dollars per treatment condition, including technical replicates.

Исследование модицифицированного метода кодирования в субполосы монохромного изображения

Исследование модицифицированного метода кодирования в субполосы монохромного изображения

Multi-channel versus quaternion orthogonal rotation invariant moments for color image representation

Orthogonal rotation invariant moments (ORIMs) have been used in many pattern recognition and image processing applications in the last three decades. Most of the applications relate to monochrome and gray-scale images. Recently, the theory of image moments for gray-scale images has been extended to color images using quaternion moments to explore the benefit of color information while representing the color images by moments. In this paper, we propose multi-channel ORIMs (MORIMs) invariants for color images and compare their performance with the existing quaternion moments, called quaternion orthogonal rotation invariant moments (QORIMs). The theoretical and experimental analysis demonstrates the superiority of the proposed MORIMs over the QORIMs invariants in the color image recognition task. The experiments are conducted by considering Zernike moments (ZMs) and quaternion ZMs (QZMs) as the representatives of MORIMs and QORIMs, respectively.

ANALISIS TEKSTUR PHOTO LAMA MENGGUNAKAN FITUR TEKSTUR GRAY LEVEL CO-OCCURRENCE MATRIKS PADA PEWARNAAN CITRA OTOMATIS

&lt;p&gt; Image processing is important in a process of introduction, classification or segmentation or other processes. One thing that can be done is an analysis of the texture features related to old photos in this case grayscale photos. The object of the research can be an old photo (image) and use a statistical method based on Gray Level Counseling Matrix (GLCM). GLCM is one of the methods used for extracting texture features, some of which are analyzed using glcm by comparing the GLCM texture feature in the old photo with the original photo The coloring process is to provide more visualization of an object, it can be a monochrome image or video with the aim of providing details and clarity of the colored image or video. The study discusses grayscale images to be colored, then searches for GLCM texture feature values. The size of the features obtained from the calculation is used to find out how much the error value indirectly shows how much the image is similar. The measurement of the success of the small scale using the method of Mean Square Error (MSE) and Mean Absolute Error (MAE).&lt;/p&gt;&lt;p&gt;Keyword: Texture, Glcm, MAE, MSE &lt;/p&gt;

Effects of Resolution, Range, and Image Contrast on Target Acquisition Performance.

We sought to determine the joint influence of resolution, target range, and image contrast on the detection and identification of targets in simulated naturalistic scenes. Resolution requirements for target acquisition have been developed based on threshold values obtained using imaging systems, when target range was fixed, and image characteristics were determined by the system. Subsequent work has examined the influence of factors like target range and image contrast on target acquisition. We varied the resolution and contrast of static images in two experiments. Participants (soldiers) decided whether a human target was located in the scene (detection task) or whether a target was friendly or hostile (identification task). Target range was also varied (50-400 m). In Experiment 1, 30 participants saw color images with a single target exemplar. In Experiment 2, another 30 participants saw monochrome images containing different target exemplars. The effects of target range and image contrast were qualitatively different above and below 6 pixels per meter of target for both tasks in both experiments. Target detection and identification performance were a joint function of image resolution, range, and contrast for both color and monochrome images. The beneficial effects of increasing resolution for target acquisition performance are greater for closer (larger) targets.