Channel Pixel Research Articles

A New Payload Partition Strategy in Color Image Steganography

In traditional steganographic schemes, RGB three channels payloads are assigned equally in a true color image. In fact, the security of color image steganography relates not only to data-embedding algorithms but also to different payload partition. How to exploit inter-channel correlations to allocate payload for performance enhancement is still an open issue in color image steganography. In this paper, a novel channel-dependent payload partition strategy based on amplifying channel modification probabilities is proposed, so as to adaptively assign the embedding capacity among RGB channels. The modification probabilities of three corresponding pixels in RGB channels are simultaneously increased, and thus the embedding impacts could be clustered, in order to improve the empirical steganographic security against the channel co-occurrences detection. The experimental results show that the new color image steganographic schemes, incorporated with the proposed strategy, can effectively make the embedding changes concentrated mainly in textured regions, and achieve better performance on resisting the modern color image steganalysis.

Patch-Wise Blind Image Deblurring Via Michelson Channel Prior

Motion blur exists in many computer vision tasks, including faces, texts, and low-illumination images etc. It has been proved that Dark Channel Prior (DCP) and Bright Channel Prior (BCP) can both help the image deblurring by enhancing the dark or bright channel pixels. However, the pixels between the dark channel pixels and the bright channel pixels are not taken into consideration, which limits the deblurring performance. A novel image channel is proposed in combination with dark channel and bright channel in this paper to consider the effects of the all types of pixels, namely, Michelson channel pixels. Secondly, as the image channels are built based on the series of image patches with different blur kernels, a new method is developed to estimate the blur kernel and can measure the similarity between neighbored kernels. Meanwhile, to perform accurate kernel estimation, the L 0 regularization is applied into the algorithm framework. In the process of image deblurring, by enhancing the Michelson channels and retaining the other channels of the image, we can capture sharper image detail and eliminate the ringing artifacts of the recovered images. Massive experimental results demonstrate that the proposed method is more robust and outperforms the existing art-of-the-state of unsupervised image deblurring methods on both synthesized and natural images.

A Selective Cross-Substitution Technique for Encrypting Color Images Using Chaos, DNA Rules and SHA-512

An innovative approach of selective encryption for color images is proposed that utilizes SHA-512 hash of plain image to modify initial conditions and control parameters of 1-Dimensional (1D) chaotic maps. The three channels (red, green and blue) of a color image are combined into 1D array and permute using sorted index of a pseudo-random sequence. The 1D permuted array is split into three sub-arrays, DNA encoding is applied on every pixel of each channel chaotically and then separate each DNA encoded channel into its Least Significant Bits (LSB) and Most Significant Bits (MSB). The substitution is carried out in two phases using addition and exclusive-or operations on MSB of each channel only. In 1 st phase, the DNA addition operation is applied on chaotically selected MSB of a pixel of one channel to LSB part of a pixel of other channel in a twisted fashioned named cross-substitution. The translated DNA bases from pseudo-random numbers are exclusive-or with cross substituted output to surge the complexity. The second substitution phase is accomplished by combining MSB part of a channel with randomly selecting LSB at pixel level. The novel algorithm is highly suitable for real time applications as it requires single round of permutation/substitution which can resist all possible statistical and differential attacks. The simulation results and analysis show that the proposed technique has the best quality output and highly efficient.

A Dual Convolution Network Using Dark Channel Prior for Image Smoke Classification

Through a comparative analysis, we confirm that the value of the dark channel pixels of the smoke image is higher than the non-smoke image. It means that the dark channel of the smoke image has more elaborate information of the smoke, which is of great benefit to our detailed feature extraction of smoke. On this background, we propose a dual convolution network using dark channel prior for image smoke classification (DarkC-DCN) for the image smoke classification. In DarkC-DCN, basing on the AlexNet, and through continuous structural improvement and optimization, we improve a detailed CNN to extract the detailed features of dark channel images. Similarly, to extract the general features in the image, we further design another residual network based on the AlexNet, which is the main framework of the entire network. To ascertain the robustness of the network, the two channels are trained separately for various inputs. In addition, we perform feature fusion before the common fully connected layer. In the experiment, we also add some non-smoke data similar to smoke in the public smoke data set for data expansion. The experimental results indicate that the model has a good performance in general. The accuracy value reaches 98.56%.

Non-contact Real-time Heart Rate Measurement Algorithm Based on PPG-standard Deviation

Heart rate is an important physiological parameter for clinical diagnosis, it can infer the health of the human body. Thus, efficient and accurate heart rate measurement is important for disease diagnosis and health monitoring. There are two ways to measure heart rate. One is contact type and the other is non-contact. Contact measurement methods include pulse cutting, electrocardiogram, etc. Because of the inconvenience of this method, a non-contact heart rate method has been proposed. Traditional non-contact measurement method based on image is collecting RGB three-channel signals in continuous video and selecting the average value of the green channel pixels as the heart rate signal for processing. However, this method is not accurate when the pixel values are changing greatly. To overcome this problem, non-contact real-time heart rate measurement method is proposed in this paper based on pixel standard deviation. Because of the changes in skin color caused by heart rate, the standard deviation signal of the green channel pixels in the region of interest (ROI) is filtered and extracted by the forward and inverse Fourier transform respectively, measuring the heart rate. The experimental results show that the improved algorithm can measure heart rate with faster speed and higher accuracy comparing to traditional methods. And we hope that our algorithm can apply in intelligent elder caring.

Smartphone imaging of subcutaneous veins

The identification of veins by medical personnel is a critical skill that is necessary to draw blood or administer intravenous fluids and medications. Because a normal consumer camera can act as a multispectral imaging apparatus, operating with three broadband detectors, we hypothesized that a standard smartphone camera might be employed for enhanced visualization of veins in human skin. Video and images of subcutaneous veins were acquired using the rear-facing iSight camera from an iPhone 6, with a fixed aperture of f/2.2, and Sony Exmor RS back-illuminated CMOS image sensor with pixel generation of 1.5 microns. A custom program was written for the iOS operating system that performs a scaled matrix subtraction of different spectral channels and displays results as a grayscale image. A scaled subtraction of green channel pixel values from red channel pixel values enabled greatly improved identification of subcutaneous veins. Wavelengths of light at which the green detector is most sensitive (520-580 nm) correspond to local absorption maxima of both oxyhemoglobin (542 and 576 nm) and deoxyhemoglobin (556 nm); consequently, the algorithm obtained images of light transport weighted toward deeper skin layers. We identified and developed a simple algorithm by which a standard smartphone camera can be employed for enhanced video-rate visualization of veins in human skin. Lasers Surg. Med. 50:1034-1039, 2018. © 2018 Wiley Periodicals, Inc.

Color fidelity and visibility enhancement of underwater image de-hazing by enhanced fuzzy intensification operator

This paper presents an optimization based algorithm for underwater image de-hazing problem. Underwater image de-hazing is the most prominent area in research. Underwater images are corrupted due to absorption and scattering. With the effect of that, underwater images have the limitation of low visibility, low color and poor natural appearance. To avoid the mentioned problems, Enhanced fuzzy intensification method is proposed. For each color channel, enhanced fuzzy membership function is derived. Second, the correction of fuzzy based pixel intensification is carried out for each channel to remove haze and to enhance visibility and color. The post processing of fuzzy histogram equalization is implemented for red channel alone when the captured image is having highest value of red channel pixel values. The proposed method provides better results in terms maximum entropy and PSNR with minimum MSE with very minimum computational time compared to existing methodologies.

Deblurring Images via Dark Channel Prior.

We present an effective blind image deblurring algorithm based on the dark channel prior. The motivation of this work is an interesting observation that the dark channel of blurred images is less sparse. While most patches in a clean image contain some dark pixels, this is not the case when they are averaged with neighboring ones by motion blur. This change in sparsity of the dark channel pixels is an inherent property of the motion blur process, which we prove mathematically and validate using image data. Enforcing sparsity of the dark channel thus helps blind deblurring in various scenarios such as natural, face, text, and low-illumination images. However, imposing sparsity of the dark channel introduces a non-convex non-linear optimization problem. In this work, we introduce a linear approximation to address this issue. Extensive experiments demonstrate that the proposed deblurring algorithm achieves the state-of-the-art results on natural images and performs favorably against methods designed for specific scenarios. In addition, we show that the proposed method can be applied to image dehazing.

A protocol for registration and correction of multicolour STED superresolution images.

Multicolour fluorescence imaging by STimulated Emission Depletion (STED) superresolution microscopy with doughnut-shaped STED laser beams based on different wavelengths for each colour channel requires precise image registration. This is especially important when STED imaging is used for co-localisation studies of two or more native proteins in biological specimens to analyse nanometric subcellular spatial arrangements. We developed a robust postprocessing image registration protocol, with the aim to verify and ultimately optimise multicolour STED image quality. Importantly, this protocol will support any subsequent quantitative localisation analysis at nanometric scales. Henceforth, using an approach that registers each colour channel present during STED imaging individually, this protocol reliably corrects for optical aberrations and inadvertent sample drift. To achieve the latter goal, the protocol combines the experimental sample information, from corresponding STED and confocal images using the same optical beam path and setup, with that of an independent calibration sample. As a result, image registration is based on a strategy that maximises the cross-correlation between sequentially acquired images of the experimental sample, which are strategically combined by the protocol. We demonstrate the general applicability of the image registration protocol by co-staining of the ryanodine receptor calcium release channel in primary mouse cardiomyocytes. To validate this new approach, we identify user-friendly criteria, which - if fulfilled - support optimal image registration. In summary, we introduce a new method for image registration and rationally based postprocessing steps through a highly standardised protocol for multicolour STED imaging, which directly supports the reproducibility of protein co-localisation analyses. Although the reference protocol is discussed exemplarily for two-colour STED imaging, it can be readily expanded to three or more colours and STED channels.

Determining Image Processing Features Describing the Appearance of Challenging Mitotic Figures and Miscounted Nonmitotic Objects

Context: Previous studies showed that the agreement among pathologists in recognition of mitoses in breast slides is fairly modest. Aims: Determining the significantly different quantitative features among easily identifiable mitoses, challenging mitoses, and miscounted nonmitoses within breast slides and identifying which color spaces capture the difference among groups better than others. Materials and Methods: The dataset contained 453 mitoses and 265 miscounted objects in breast slides. The mitoses were grouped into three categories based on the confidence degree of three pathologists who annotated them. The mitoses annotated as “probably a mitosis” by the majority of pathologists were considered as the challenging category. The miscounted objects were recognized as a mitosis or probably a mitosis by only one of the pathologists. The mitoses were segmented using fc-means clustering, followed by morphological operations. Morphological, intensity-based, and textural features were extracted from the segmented area and also the image patch of 63 × 63 pixels in different channels of eight color spaces. Holistic features describing the mitoses’ surrounding cells of each image were also extracted. Statistical Analysis Used: The Kruskal–Wallis H-test followed by the Tukey-Kramer test was used to identify significantly different features. Results: The results indicated that challenging mitoses were smaller and rounder compared to other mitoses. Among different features, the Gabor textural features differed more than others between challenging mitoses and the easily identifiable ones. Sizes of the non-mitoses were similar to easily identifiable mitoses, but nonmitoses were rounder. The intensity-based features from chromatin channels were the most discriminative features between the easily identifiable mitoses and the miscounted objects. Conclusions: Quantitative features can be used to describe the characteristics of challenging mitoses and miscounted nonmitotic objects.

A population-based approach to point-sampling spatial color algorithms.

Inspired by the behavior of the human visual system, spatial color algorithms perform image enhancement by correcting the pixel channel lightness based on the spatial distribution of the intensities in the surrounding area. The two visual contrast enhancement algorithms RSR and STRESS belong to this family of models: they rescale the input based on local reference values, which are determined by exploring the image by means of random point samples, called sprays. Due to the use of sampling, they may yield a noisy output. In this paper, we introduce a probabilistic formulation of the two models: our algorithms (RSR-P and STRESS-P) rely implicitly on the whole population of possible sprays. For processing larger images, we also provide two approximated algorithms that exploit a suitable target-dependent space quantization. Those spray population-based formulations outperform RSR and STRESS in terms of the processing time required for the production of noiseless outputs. We argue that this population-based approach, which can be extended to other members of the family, complements the sampling-based approach, in that it offers not only a better control in the design of approximated algorithms, but also additional insight into individual models and their relationships. We illustrate the latter point by providing a model of halo artifact formation.

Firedec: a two-channel finite-resolution image deconvolution algorithm

We present a two-channel deconvolution method that decomposes images into a parametric point-source channel and a pixelized extended-source channel. Based on the central idea of the deconvolution algorithm proposed by Magain, Courbin & Sohy (1998), the method aims at improving the resolution of the data rather than at completely removing the point spread function (PSF). Improvements over the original method include a better regularization of the pixel channel of the image, based on wavelet filtering and multiscale analysis, and a better controlled separation of the point source vs. the extended source. In addition, the method is able to simultaneously deconvolve many individual frames of the same object taken with different instruments under different PSF conditions. For this purpose, we introduce a general geometric transformation between individual images. This transformation allows the combination of the images without having to interpolate them. We illustrate the capability of our algorithm using real and simulated images with complex diffraction-limited PSF.

Studies of the possibility to use Gas Pixel Detector as a fast trigger tracking device

Gas Pixel Detector (GPD) technology offers new possibilities, which make them very attractive for application in existing and future accelerator experiments and beyond. GPDs combine advantages of silicon and gaseous detectors. They can be produced radiation hard and with low power consumption using relatively cheap technology. Low capacitance of the individual pixel channel allows us to obtain a large signal to noise ratio. Using a time projection method for GPD readout one obtains 3D track image with precise coordinate (31 µm) and angular information (0.40°). This feature would allow us to achieve performance of one GPD layer equal to a few layers of silicon detectors. Implementation of a fast readout and data processing at the front-end level allows one to reconstruct a track segment in less than 1 μs, and to use this information for the first level trigger generation. The relevant algorithms of data acquisition and analysis are described and the results of simulations are presented in this paper.

A LOSSLESS COLOR IMAGE COMPRESSION USING AN IMPROVED REVERSIBLE COLOR TRANSFORM WITH HIERARCHICAL PREDICTION AND MODIFIED HUFFMAN CODING

In case of the conventional lossless color image compression methods, the pixels are interleaved from each color component, and they are predicted and finally encoded. In this paper, we propose a lossless color image compression method using hierarchical prediction of chrominance channel pixels and encoded with modified Huffman coding. An input image is chosen and the R, G and B color channel is transform into YCuCv color space using an improved reversible color transform. After that a conventional lossless image coder like CALIC is used to compress the luminance channel Y. The chrominance channel Cu and Cv are encoded with hierarchical decomposition and directional prediction. The effective context modeling for prediction residual is adopted finally. It is seen from the experimental result the proposed method improves the compression performance than the existing method.

Multiscale curvatures for identifying channel locations from DEMs

Curvature based methods are suitable for channel identification in digital elevation models. One obstacle in using these methods is the fact that channels generally occur at multiple scales in the landscape, from small creeks to large rivers. In this paper, we show how likely channel pixels can be identified simultaneously at a range of scales using multiscale curvature operators applied to digital elevation models. Our proposed Hyperscale Channel Extraction (HCE) method localizes channels at the smallest scale while simultaneously tracking the shape of the channel at a full interval of scales (the hyperscale). We test the method using two different types of curvature, and apply and validate it to a catchment representing terrain with a high slope sampled by airborne laser altimetry. The test results demonstrate that by explicitly employing the extra dimension of scale to localize channels, (a) we are able to robustly identify channel pixels, as possible channel locations are tracked through a full interval of scales, (b) no more a priori determination of the relevant scale is necessary, and (c) only one parameter remains to be set: a threshold on the curvature value that has a clear physical interpretation.

Enhancement Algorithm of Color Fog Image Based on the Adaptive Scale and S-Cosine Curve

To deal with the image contrast and color fidelity details problem in the traditional Center around the Retinex image enhancement algorithms, Enhancement algorithm of color fog image based on the adaptive scale and s-cosine curve is proposed. Firstly, the image is transformed into the RGB color space. Then the each channel pixel values can be stretched the grayscale range by S-cosine curve and introduces the local correction function. It can calculate the scale of the Gaussian kernel, and then proceeds to do the Gamma correction for the estimates of the reflection component, obtains the multi-scale image by the weighted average. Afterwards, the obtained image is used to global nonlinear correction, image sharpening and smoothing, and being superimposed reflection components, achieving the image enhancement. At last, it can carry on the intensity adjustment and grayscale adjustment for the obtained image. Through the subjective observation and objective evaluation, this algorithm is better than the traditional center around Retinex algorithm and MSRCR algorithm in processing effect.

A Variational Approach for Image Decolorization by Variance Maximization

Color-to-grayscale conversion is the process used to convert a color image to a grayscale one, which is a basic tool in digital printing, photograph rendering, and single-channel image processing. The main aim of this paper is to propose a variational approach for image decolorization by variance maximization. Our idea is to use an energy functional to determine local transformations for combining red, green, and blue channel pixel values together by maximizing the local variance of the output grayscale image and preserving the brightness of the input color image. In order to minimize the differences among the local transformations at nearby pixel locations, the total variation regularization of the transformation is incorporated into the functional for the decolorization process. The existence and uniqueness of the minimizer of the variational model can be shown. We also present an effective algorithm for solving the variational model numerically, and show the convergence of the algorithm. Experimental results are reported to demonstrate the effectiveness of the proposed method, and its performance is better than those of the other testing methods for a set of benchmark color images.

Speckle reducing in PolSAR images for topographic feature extraction

This article presents a new approach that reduces the speckle in PolSAR (Polarimetric Synthetic Aperture Radar) image, and then improves the visualisation and facilitates operators to extract topographic planimetric features. The proposed filter takes into consideration all channels simultaneously (polarisation modes: HH, HV, VH and VV) for each channel despeckling process. So, for a given pixel in an image channel, the adjustment degree of its radiometric value will be decided by its adjacent pixels in the same channel and their equivalent pixels in other channels. Moreover, to avoid error propagation between channels, the filter runs in several iterations modifying the less ambiguous pixels first and leaves the rest of the pixels for a possible adjustment in next iterations. To modulate the uncertainty and combine inter and intra channel information and make decision, the proposed filter calls rules and techniques of Dempster–Shafer theory. The experimentation is based on Radarsat-2 images and our study areas were in Quebec City and the Arctic in Canada. The results of our experimentation show clearly the performance and the accuracy of our approach in spite of its simplicity and processing speed compared to other approaches.

SU-E-T-24: A New Method Correcting the Parabola Effect of Flat-Bed Scanners Used in Radiochromic Film Dosimetry

Purpose: In flatbed scanners used to digitize radiochromic films, a system of mirrors provides a reduced optical image of the scanned film in the entrance plane of a small CCD array. In this optical system, artifacts of the optical density arise from anisotropic scattering and polarization of the scanner light. We quantified this perturbation effect and eliminated it by a software correction. Methods: In order to measure the nonuniformity of an Epson 10000XL scanner and an Epson V700 scanner, seven EBT3 films of 4x4 cm2, exposed to doses ranging from 0 to 3 Gy were scanned at different positions × on the axis parallel to the light source. An area of interest of 30×30 pixels at the center of each film was chosen for analysis. The corresponding red channel pixel values were averaged and converted to optical density OD. These optical density values were then fitted by a parabolic function OD(×), which fully characterizes the nonuniformity of the scanner. The correction algorithm transforms the OD value measured at position × into the corrected OD value, i.e. the value that would be measured at the apex position. The calibration films are scanned at the apex position and the calibration curve is related to the apex OD value. Results: To test the correction algorithm a 20× 20 cm2 6 MV photon field was measured by film and ionization chamber and the dose difference after correction was less than 2%. This Result can be achieved all over the scanner bed, so it is possible to correct wide dose distributions measured by radiochromic films. Conclusion: This new parabola correction algorithm reduces the scanner artifacts over the whole width of the scanner bed and presents an alternative approach or an extension to the three‐channel analysis [Micke et al Med Phys 38, 2012].

TDICCD拼接相机的像元响应非均匀性校正方法

To improve the Pixel Response Non-uniformity(PRNU) in a TDICCD mosaic camera,an algorithm was proposed to correct the PRNU between the channels and among the pixels in channels at different gains,deviations and integrated time.Then,a design method was given to implement the algorithm on a Field Programmable Gate Array(FPGA) platform.The experimental results indicate that the problems of PRNU could be solved at different gains,deviations and integrated time in the TDICCD mosaic camera.The PRNU of a single TDICCD could be decreased from 4.72% to 0.27%.In bad circumstance,the PRNU of the single TDICCD could be decreased from 4.68% to 2.13%.The actual image also proves that the pixel correction algorithm has high real time performance,high reliability and stability,and a satisfied result to the requirements of space-projects.It solves the real-time pixel correction problem of the PRNU in TDICCD mosaic cameras.