Single Color Image Research Articles

Single Channel Quantum Color Image Encryption Algorithm Based on HSI Model and Quantum Fourier Transform

In order to obtain high-quality color images, it is important to keep the hue component unchanged while emphasize the intensity or saturation component. As a public color model, Hue-Saturation Intensity (HSI) model is commonly used in image processing. A new single channel quantum color image encryption algorithm based on HSI model and quantum Fourier transform (QFT) is investigated, where the color components of the original color image are converted to HSI and the logistic map is employed to diffuse the relationship of pixels in color components. Subsequently, quantum Fourier transform is exploited to fulfill the encryption. The cipher-text is a combination of a gray image and a phase matrix. Simulations and theoretical analyses demonstrate that the proposed single channel quantum color image encryption scheme based on the HSI model and quantum Fourier transform is secure and effective.

Multi-color single-molecule tracking and subtrajectory analysis for quantification of spatiotemporal dynamics and kinetics upon T cell activation

The dynamic properties of molecules in living cells are attracting increasing interest. We propose a new method, moving subtrajectory analysis using single-molecule tracking, and demonstrate its utility in the spatiotemporal quantification of not only dynamics but also the kinetics of interactions using single-color images. Combining this technique with three-color simultaneous single-molecule imaging, we quantified the dynamics and kinetics of molecules in spatial relation to T cell receptor (TCR) microclusters, which trigger TCR signaling. CD3ε, a component of the TCR/CD3 complex, and CD45, a phosphatase positively and negatively regulating signaling, were each found in two mobility states: faster (associated) and slower (dissociated) states. Dynamics analysis suggests that the microclusters are loosely composed of heterogeneous nanoregions, possibly surrounded by a weak barrier. Kinetics analysis quantified the association and dissociation rates of interactions with the microclusters. The associations of both CD3ε and CD45 were single-step processes. In contrast, their dissociations were each composed of two components, indicating transient and stable associated states. Inside the microclusters, the association was accelerated, and the stable association was increased. Only CD45 showed acceleration of association at the microcluster boundary, suggesting specific affinity on the boundary. Thus, this method is an innovative and versatile tool for spatiotemporal quantification.

Example-Based Image Colorization Using Locality Consistent Sparse Representation.

Image colorization aims to produce a natural looking color image from a given gray-scale image, which remains a challenging problem. In this paper, we propose a novel example-based image colorization method exploiting a new locality consistent sparse representation. Given a single reference color image, our method automatically colorizes the target gray-scale image by sparse pursuit. For efficiency and robustness, our method operates at the superpixel level. We extract low-level intensity features, mid-level texture features, and high-level semantic features for each superpixel, which are then concatenated to form its descriptor. The collection of feature vectors for all the superpixels from the reference image composes the dictionary. We formulate colorization of target superpixels as a dictionary-based sparse reconstruction problem. Inspired by the observation that superpixels with similar spatial location and/or feature representation are likely to match spatially close regions from the reference image, we further introduce a locality promoting regularization term into the energy formulation, which substantially improves the matching consistency and subsequent colorization results. Target superpixels are colorized based on the chrominance information from the dominant reference superpixels. Finally, to further improve coherence while preserving sharpness, we develop a new edge-preserving filter for chrominance channels with the guidance from the target gray-scale image. To the best of our knowledge, this is the first work on sparse pursuit image colorization from single reference images. Experimental results demonstrate that our colorization method outperforms the state-of-the-art methods, both visually and quantitatively using a user study.

Perceptually accurate display of two greyscale images as a single colour image.

Life scientists often desire to display the signal from two different molecular probes as a single colour image, so as to convey information about the probes' relative concentrations as well as their spatial corelationship. Traditionally, such colour images are created through a merge display, where each greyscale signal is assigned to different channels of an RGB colour image. However, human perception of colour and greyscale intensity is not equivalent. Thus, a merged image display conveys to the typical viewer only a subset of the absolute and relative intensity information present in and between two greyscale images. The Commission Internationale de l'Eclairage L*a*b* colour space (CIELAB) has been designed to specify colours according to the perceptually defined quantities of hue (perceived colour) and luminosity (perceived brightness). Here, we use the CIELAB colour space to encode two dimensions of information about two greyscale images within these two perceptual dimensions of a single colour image. We term our method a Perceptually Uniform Projection display and show using biological image examples how these displays convey more information about two greyscale signals than comparable RGB colour space-based techniques.

A Hierarchical Approach for Rain or Snow Removing in a Single Color Image.

In this paper, we propose an efficient algorithm to remove rain or snow from a single color image. Our algorithm takes advantage of two popular techniques employed in image processing, namely, image decomposition and dictionary learning. At first, a combination of rain/snow detection and a guided filter is used to decompose the input image into a complementary pair: 1) the low-frequency part that is free of rain or snow almost completely and 2) the high-frequency part that contains not only the rain/snow component but also some or even many details of the image. Then, we focus on the extraction of image's details from the high-frequency part. To this end, we design a 3-layer hierarchical scheme. In the first layer, an overcomplete dictionary is trained and three classifications are carried out to classify the high-frequency part into rain/snow and non-rain/snow components in which some common characteristics of rain/snow have been utilized. In the second layer, another combination of rain/snow detection and guided filtering is performed on the rain/snow component obtained in the first layer. In the third layer, the sensitivity of variance across color channels is computed to enhance the visual quality of rain/snow-removed image. The effectiveness of our algorithm is verified through both subjective (the visual quality) and objective (through rendering rain/snow on some ground-truth images) approaches, which shows a superiority over several state-of-the-art works.

Single-shot quantitative phase microscopy with color-multiplexed differential phase contrast (cDPC).

We present a new technique for quantitative phase and amplitude microscopy from a single color image with coded illumination. Our system consists of a commercial brightfield microscope with one hardware modification—an inexpensive 3D printed condenser insert. The method, color-multiplexed Differential Phase Contrast (cDPC), is a single-shot variant of Differential Phase Contrast (DPC), which recovers the phase of a sample from images with asymmetric illumination. We employ partially coherent illumination to achieve resolution corresponding to 2× the objective NA. Quantitative phase can then be used to synthesize DIC and phase contrast images or extract shape and density. We demonstrate amplitude and phase recovery at camera-limited frame rates (50 fps) for various in vitro cell samples and c. elegans in a micro-fluidic channel.

Multi-spectral Vehicle Detection based on Convolutional Neural Network

This paper presents a unified framework for joint Convolutional Neural Network (CNN) based vehicle detection by leveraging multi-spectral image pairs. With the observation that under challenging environments such as night vision and limited light source, vehicle detection in a single color image can be more tractable by using additional far-infrared (FIR) image, we design joint CNN architecture for both RGB and FIR image pairs. We assume that a score map from joint CNN applied to overall image can be considered as confidence of vehicle existence. To deal with various scale ratios of vehicle candidates, multi-scale images are first generated scaling an image according to possible scale ratio of vehicles. The vehicle candidates are then detected on local maximal on each score maps. The generation of overlapped candidates is prevented with non-maximal suppression on multi-scale score maps. The experimental results show that our framework have superior performance than conventional methods with a joint framework of multi-spectral image pairs reducing false positive generated by conventional vehicle detection framework using only single color image.

A novel 2D to 3D video conversion system based on a machine learning approach

There has been recently a significant increase in the number of available 3D displays and players. Nevertheless, the amount of 3D content has not increased in the same magnitude, creating a gap between 3D offer and demand. To reduce this difference, many algorithms have appeared that perform 2D-to-3D image and video conversion. These algorithms usually require several images from the same scene to perform the conversion. In this paper, an automatic algorithm for estimating the 3D structure of a scene from a single color image is proposed. It is based on the key assumption that color images with similar structure will likely present similar depth structures. The conversion algorithm is split into an offline and an online module to be easily deployable into consumer devices, such as smartphones or TVs. The offline module pre-processes a color+depth image database to speed up the subsequent depth estimation. The online module infers a depth prior from a color query image using the previous database as training data. Then, it is refined through a segmentation-guided filtering. The conversion algorithm has been evaluated in three publicly available databases, and compared with several state-of-theart algorithms to prove its efficiency.

Flow-guided warping for image-based shape manipulation

We present an interactive method that manipulates perceived object shape from a single input color image thanks to a warping technique implemented on the GPU. The key idea is to give the illusion of shape sharpening or rounding by exaggerating orientation patterns in the image that are strongly correlated to surface curvature. We build on a growing literature in both human and computer vision showing the importance of orientation patterns in the communication of shape, which we complement with mathematical relationships and a statistical image analysis revealing that structure tensors are indeed strongly correlated to surface shape features. We then rely on these correlations to introduce a flow-guided image warping algorithm, which in effect exaggerates orientation patterns involved in shape perception. We evaluate our technique by 1) comparing it to ground truth shape deformations, and 2) performing two perceptual experiments to assess its effects. Our algorithm produces convincing shape manipulation results on synthetic images and photographs, for various materials and lighting environments.

REMOTE SENSING IMAGE FUSION USING ICA AND OPTIMIZED WAVELET TRANSFORM

Abstract. In remote-sensing image processing, fusion (pan-sharpening) is a process of merging high-resolution panchromatic and lower resolution multispectral (MS) imagery to create a single high-resolution color image. Many methods exist to produce data fusion results with the best possible spatial and spectral characteristics, and a number have been commercially implemented. However, the pan-sharpening image produced by these methods gets the high color distortion of spectral information. In this paper, to minimize the spectral distortion we propose a remote sensing image fusion method which combines the Independent Component Analysis (ICA) and optimization wavelet transform. The proposed method is based on selection of multiscale components obtained after the ICA of images on the base of their wavelet decomposition and formation of linear forms detailing coefficients of the wavelet decomposition of images brightness distributions by spectral channels with iteratively adjusted weights. These coefficients are determined as a result of solving an optimization problem for the criterion of maximization of information entropy of the synthesized images formed by means of wavelet reconstruction. Further, reconstruction of the images of spectral channels is done by the reverse wavelet transform and formation of the resulting image by superposition of the obtained images. To verify the validity, the new proposed method is compared with several techniques using WorldView-2 satellite data in subjective and objective aspects. In experiments we demonstrated that our scheme provides good spectral quality and efficiency. Spectral and spatial quality metrics in terms of RASE, RMSE, CC, ERGAS and SSIM are used in our experiments. These synthesized MS images differ by showing a better contrast and clarity on the boundaries of the "object of interest - the background". The results show that the proposed approach performs better than some compared methods according to the performance metrics.

EFFECT OF PANSHARPENED IMAGE ON SOME OF PIXEL BASED AND OBJECT BASED CLASSIFICATION ACCURACY

Classification is the most important method to determine type of crop contained in a region for agricultural planning. There are two types of the classification. First is pixel based and the other is object based classification method. While pixel based classification methods are based on the information in each pixel, object based classification method is based on objects or image objects that formed by the combination of information from a set of similar pixels. Multispectral image contains a higher degree of spectral resolution than a panchromatic image. Panchromatic image have a higher spatial resolution than a multispectral image. Pan sharpening is a process of merging high spatial resolution panchromatic and high spectral resolution multispectral imagery to create a single high resolution color image. The aim of the study was to compare the potential classification accuracy provided by pan sharpened image. In this study, SPOT 5 image was used dated April 2013. 5m panchromatic image and 10m multispectral image are pan sharpened. Four different classification methods were investigated: maximum likelihood, decision tree, support vector machine at the pixel level and object based classification methods. SPOT 5 pan sharpened image was used to classification sun flowers and corn in a study site located at Kadirli region on Osmaniye in Turkey. The effects of pan sharpened image on classification results were also examined. Accuracy assessment showed that the object based classification resulted in the better overall accuracy values than the others. The results that indicate that these classification methods can be used for identifying sun flower and corn and estimating crop areas.

EFFECT OF PANSHARPENED IMAGE ON SOME OF PIXEL BASED AND OBJECT BASED CLASSIFICATION ACCURACY

Abstract. Classification is the most important method to determine type of crop contained in a region for agricultural planning. There are two types of the classification. First is pixel based and the other is object based classification method. While pixel based classification methods are based on the information in each pixel, object based classification method is based on objects or image objects that formed by the combination of information from a set of similar pixels. Multispectral image contains a higher degree of spectral resolution than a panchromatic image. Panchromatic image have a higher spatial resolution than a multispectral image. Pan sharpening is a process of merging high spatial resolution panchromatic and high spectral resolution multispectral imagery to create a single high resolution color image. The aim of the study was to compare the potential classification accuracy provided by pan sharpened image. In this study, SPOT 5 image was used dated April 2013. 5m panchromatic image and 10m multispectral image are pan sharpened. Four different classification methods were investigated: maximum likelihood, decision tree, support vector machine at the pixel level and object based classification methods. SPOT 5 pan sharpened image was used to classification sun flowers and corn in a study site located at Kadirli region on Osmaniye in Turkey. The effects of pan sharpened image on classification results were also examined. Accuracy assessment showed that the object based classification resulted in the better overall accuracy values than the others. The results that indicate that these classification methods can be used for identifying sun flower and corn and estimating crop areas.

REMOTE SENSING IMAGE FUSION USING ICA AND OPTIMIZED WAVELET TRANSFORM

In remote-sensing image processing, fusion (pan-sharpening) is a process of merging high-resolution panchromatic and lower resolution multispectral (MS) imagery to create a single high-resolution color image. Many methods exist to produce data fusion results with the best possible spatial and spectral characteristics, and a number have been commercially implemented. However, the pan-sharpening image produced by these methods gets the high color distortion of spectral information. In this paper, to minimize the spectral distortion we propose a remote sensing image fusion method which combines the Independent Component Analysis (ICA) and optimization wavelet transform. The proposed method is based on selection of multiscale components obtained after the ICA of images on the base of their wavelet decomposition and formation of linear forms detailing coefficients of the wavelet decomposition of images brightness distributions by spectral channels with iteratively adjusted weights. These coefficients are determined as a result of solving an optimization problem for the criterion of maximization of information entropy of the synthesized images formed by means of wavelet reconstruction. Further, reconstruction of the images of spectral channels is done by the reverse wavelet transform and formation of the resulting image by superposition of the obtained images. To verify the validity, the new proposed method is compared with several techniques using WorldView-2 satellite data in subjective and objective aspects. In experiments we demonstrated that our scheme provides good spectral quality and efficiency. Spectral and spatial quality metrics in terms of RASE, RMSE, CC, ERGAS and SSIM are used in our experiments. These synthesized MS images differ by showing a better contrast and clarity on the boundaries of the "object of interest - the background". The results show that the proposed approach performs better than some compared methods according to the performance metrics.

A divide-and-conquer hole-filling method for handling disocclusion in single-view rendering

Large holes are unavoidably generated in depth image based rendering (DIBR) using a single color image and its associated depth map. Such holes are mainly caused by disocclusion, which occurs around the sharp depth discontinuities in the depth map. We propose a divide-and-conquer hole-filling method which refines the background depth pixels around the sharp depth discontinuities to address the disocclusion problem. Firstly, the disocclusion region is detected according to the degree of depth discontinuity, and the target area is marked as a binary mask. Then, the depth pixels located in the target area are modified by a linear interpolation process, whose pixel values decrease from the foreground depth value to the background depth value. Finally, in order to remove the isolated depth pixels, median filtering is adopted to refine the depth map. In these ways, disocclusion regions in the synthesized view are divided into several small holes after DIBR, and are easily filled by image inpainting. Experimental results demonstrate that the proposed method can effectively improve the quality of the synthesized view subjectively and objectively.

Orange Sorting by Applying Pattern Recognition on Colour Image

Manual sorting/grading of oranges is done at wholesale markets/ food processing factories based upon its maturity, size, quality and breeds. With an aim to replace the manual sorting system, this paper proposes the research work for automated grading of Oranges using pattern recognition techniques applied on a single color image of the fruit. This research is carried out on 160 Orange fruits collected from varied geographical locations in Vidarbha Region of Maharashtra. System designed can automatically classify an Orange fruit from this region, given its single color image of 640 × 480 pixel resolution, taken inside a special box designed with 430 lux intensity light inside it, by a digital camera. Only 4 features are used to classify oranges into 4 different classes according to the maturity level and 3 different classes as per size of oranges. In this paper two novel techniques based on Pattern Recognition are proposed – Edited Multi Seed Nearest Neighbor Technique and Linear Regression based technique; although Nearest Neighbor Prototype technique is also deployed. Linear Regression based technique can explicitly predict the maturity of the unknown orange fruit, enabling classification into multiple classes with desired lifespan. Experimental results indicate success rate up to 90% and 98%.

Color-coded LED microscopy for multi-contrast and quantitative phase-gradient imaging.

We present a multi-contrast microscope based on color-coded illumination and computation. A programmable three-color light-emitting diode (LED) array illuminates a specimen, in which each color corresponds to a different illumination angle. A single color image sensor records light transmitted through the specimen, and images at each color channel are then separated and utilized to obtain bright-field, dark-field, and differential phase contrast (DPC) images simultaneously. Quantitative phase imaging is also achieved based on DPC images acquired with two different LED illumination patterns. The multi-contrast and quantitative phase imaging capabilities of our method are demonstrated by presenting images of various transparent biological samples.

Machine-Learning-Based Analysis in Genome-Edited Cells Reveals the Efficiency of Clathrin-Mediated Endocytosis.

Cells internalize various molecules through clathrin-mediated endocytosis (CME). Previous live-cell imaging studies suggested that CME is inefficient, with about half of the events terminated. These CME efficiency estimates may have been confounded by overexpression of fluorescently tagged proteins and inability to filter out false CME sites. Here, we employed genome editing and machine learning to identify and analyze authentic CME sites. We examined CME dynamics in cells that express fluorescent fusions of two defining CME proteins, AP2 and clathrin. Support vector machine classifiers were built to identify and analyze authentic CME sites. From inception until disappearance, authentic CME sites contain both AP2 and clathrin, have the same degree of limited mobility, continue to accumulate AP2 and clathrin over lifetimes >∼20 s, and almost always form vesicles as assessed by dynamin2 recruitment. Sites that contain only clathrin or AP2 show distinct dynamics, suggesting they are not part of the CME pathway.

Red-green bicolor photoelasticity using a single-color image

Conventional RGB or tricolor photoelasticity processes the intensity information of the red, green, and blue color planes to demodulate the photoelastic fringe orders (or isochromatics). We explore the possibility of isochromatic demodulation using the intensity data of only the red and green color planes drawn from a single-color image. This would help in avoiding the uncertainties caused due to the blue color plane in RGB photoelasticity. Moreover, a new procedure is proposed for isochromatic demodulation using a synthetically generated look-up table (LUT) to address the tint and intensity variations between the calibration and analysis images. The proposed method has been validated and extended to analyze live models and stress frozen slices. The results show that the proposed bicolor technique has the potential for demodulating higher fringe orders using generic white light sources with acceptable accuracy. Moreover, the bicolor algorithm consumes 13% less computational time than the tricolor algorithm.

Improved Depth Image-Based Rendering Using an Adaptive Compensation Method on an Autostereoscopic 3-D Display for a Kinect Sensor

Depth image-based rendering (DIBR) is a 2-D-3-D view conversion technology in a space domain that corresponds to depth map. DIBR system requires only a single color image and an associated depth map to generate synthesized autostereoscopic 3-D views, but the quality of the result is depreciated due to the disoccluded areas in the depth map. Because of this problem, an improved DIBR method that uses a depth map adaptive compensation method is proposed. Using a fast parallax lookup table, the position of holes is allocated and depth map is split into blocks for statistical analysis and classification in order to estimate the quantity of holes. The result of the classification is used for adaptive multimode filtering to optimize the depth map. The experimental results show that the proposed method not only enhances the quality of depth map by compensating for the bigger and smaller areas of holes referring to analysis results, and by determining the filling method using the type of holes, but also reduces the number of holes that retain better depth map and reduces computation time.

A Novel DWT Based Image Securing Method Using Steganography

Steganography is a data hiding technique that is widely used in various information securing applications. Steganography transmits data by hiding the existence of the message so that a viewer cannot identify the transmission of message and hence not able to decrypt it. This work proposes a data securing technique that is used for hiding multiple color images into a single color image using the Discrete Wavelet Transform. The cover image is split up into R, G and B planes. Secret images are embedded into these planes. An N-level decomposition of the cover image and the secret images are done and some frequency components of the same are combined. Secret images are then extracted from the stego image. Here, the stego image obtained has a less perceptible changes compared to the original image with high overall security.