Showing papers on "Grayscale published in 2020"

A New Payload Partition Strategy in Color Image Steganography

Abstract: 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.

Instance-Aware Image Colorization

Abstract: Image colorization is inherently an ill-posed problem with multi-modal uncertainty. Previous methods leverage the deep neural network to map input grayscale images to plausible color outputs directly. Although these learning-based methods have shown impressive performance, they usually fail on the input images that contain multiple objects. The leading cause is that existing models perform learning and colorization on the entire image. In the absence of a clear figure-ground separation, these models cannot effectively locate and learn meaningful object-level semantics. In this paper, we propose a method for achieving instance-aware colorization. Our network architecture leverages an off-the-shelf object detector to obtain cropped object images and uses an instance colorization network to extract object-level features. We use a similar network to extract the full-image features and apply a fusion module to full object-level and image-level features to predict the final colors. Both colorization networks and fusion modules are learned from a large-scale dataset. Experimental results show that our work outperforms existing methods on different quality metrics and achieves state-of-the-art performance on image colorization.

Automated Colorization of a Grayscale Image With Seed Points Propagation

Abstract: In this paper, we propose a fully automatic image colorization method for grayscale images using neural network and optimization. For a determined training set including the gray images and its corresponding color images, our method segments grayscale images into superpixels and then extracts features of particular points of interest in each superpixel. The obtained features and their RGB values are given as input for, the training colorization neural network of each pixel. To achieve a better image colorization effect in shorter running time, our method further propagates the resulting color points to neighboring pixels for improved colorization results. In the propagation of color, we present a cost function to formalize the premise that neighboring pixels should have the maximum positive similarity of intensities and colors; we then propose our solution to solving the optimization problem. At last, a guided image filter is employed to refine the colorized image. Experiments on a wide variety of images show that the proposed algorithms can achieve superior performance over the state-of-the-art algorithms.

ChromaGAN: Adversarial Picture Colorization with Semantic Class Distribution

Abstract: The colorization of grayscale images is an ill-posed problem, with multiple correct solutions. In this paper, we propose an adversarial learning colorization approach coupled with semantic information. A generative network is used to infer the chromaticity of a given grayscale image conditioned to semantic clues. This network is framed in an adversarial model that learns to colorize by incorporating perceptual and semantic understanding of color and class distributions. The model is trained via a fully self-supervised strategy. Qualitative and quantitative results show the capacity of the proposed method to colorize images in a realistic way achieving state-of-the-art results.

Low-Rank Quaternion Approximation for Color Image Processing

Abstract: Low-rank matrix approximation (LRMA)-based methods have made a great success for grayscale image processing. When handling color images, LRMA either restores each color channel independently using the monochromatic model or processes the concatenation of three color channels using the concatenation model. However, these two schemes may not make full use of the high correlation among RGB channels. To address this issue, we propose a novel low-rank quaternion approximation (LRQA) model. It contains two major components: first, instead of modeling a color image pixel as a scalar in conventional sparse representation and LRMA-based methods, the color image is encoded as a pure quaternion matrix, such that the cross-channel correlation of color channels can be well exploited; second, LRQA imposes the low-rank constraint on the constructed quaternion matrix. To better estimate the singular values of the underlying low-rank quaternion matrix from its noisy observation, a general model for LRQA is proposed based on several nonconvex functions. Extensive evaluations for color image denoising and inpainting tasks verify that LRQA achieves better performance over several state-of-the-art sparse representation and LRMA-based methods in terms of both quantitative metrics and visual quality.

A novel equilibrium optimization algorithm for multi-thresholding image segmentation problems

Abstract: Image segmentation is considered a crucial step required for image analysis and research. Many techniques have been proposed to resolve the existing problems and improve the quality of research, such as region-based, threshold-based, edge-based, and feature-based clustering in the literature. The researchers have moved toward using the threshold technique due to the ease of use for image segmentation. To find the optimal threshold value for a grayscale image, we improved and used a novel meta-heuristic equilibrium algorithm to resolve this scientific problem. Additionally, our improved algorithm has the ability to enhance the accuracy of the segmented image for research analysis with a significant threshold level. The performance of our algorithm is compared with seven other algorithms like whale optimization algorithm, bat algorithm, sine–cosine algorithm, salp swarm algorithm, Harris hawks algorithm, crow search algorithm, and particle swarm optimization. Based on a set of well-known test images taken from Berkeley Segmentation Dataset, the performance evaluation of our algorithm and well-known algorithms described above has been conducted and compared. According to the independent results and analysis of each algorithm, our algorithm can outperform all other algorithms in fitness values, peak signal-to-noise ratio metric, structured similarity index metric, maximum absolute error, and signal-to-noise ratio. However, our algorithm cannot outperform some algorithms in standard deviation values and central processing unit time with the large threshold levels observed.

Blind Universal Bayesian Image Denoising With Gaussian Noise Level Learning

Abstract: Blind and universal image denoising consists of using a unique model that denoises images with any level of noise. It is especially practical as noise levels do not need to be known when the model is developed or at test time. We propose a theoretically-grounded blind and universal deep learning image denoiser for additive Gaussian noise removal. Our network is based on an optimal denoising solution, which we call fusion denoising. It is derived theoretically with a Gaussian image prior assumption. Synthetic experiments show our network’s generalization strength to unseen additive noise levels. We also adapt the fusion denoising network architecture for image denoising on real images. Our approach improves real-world grayscale additive image denoising PSNR results for training noise levels and further on noise levels not seen during training. It also improves state-of-the-art color image denoising performance on every single noise level, by an average of $0.1dB$ , whether trained on or not.

Implicit Dual-Domain Convolutional Network for Robust Color Image Compression Artifact Reduction

Abstract: Several dual-domain convolutional neural network-based methods show outstanding performance in reducing image compression artifacts. However, they are unable to handle color images as the compression processes for gray scale and color images are different. Moreover, these methods train a specific model for each compression quality, and they require multiple models to achieve different compression qualities. To address these problems, we proposed an implicit dual-domain convolutional network (IDCN) with a pixel position labeling map and quantization tables as inputs. We proposed an extractor-corrector framework-based dual-domain correction unit (DCU) as the basic component to formulate the IDCN; the implicit dual-domain translation allows the IDCN to handle color images with discrete cosine transform (DCT)-domain priors. A flexible version of IDCN (IDCN-f) was also developed to handle a wide range of compression qualities. Experiments for both objective and subjective evaluations on benchmark datasets show that IDCN is superior to the state-of-the-art methods and IDCN-f exhibits excellent abilities to handle a wide range of compression qualities with a little trade-off against performance; further, it demonstrates great potential for practical applications.

Hybrid Watermarking Algorithm Using Clifford Algebra With Arnold Scrambling and Chaotic Encryption

Abstract: With the widespread use of color images, the copyright protection of those images using watermarks is one of the latest research topics. The use of color images as watermarks has advantages over binary and irreplaceable grayscale images. Color images are intuitive, rich, and lively; they have large amounts of copyright protection information and more easily recognized by human vision. To improve the security of watermark information and embedding positions and improve the algorithm’s robustness against various attacks, a Quaternion Fourier transform (QFT) based algorithm, based on Arnold transform and chaotic encryption, is proposed in this paper. Geometric algebra (GA) can deal with color images in vector form with each component of RGB handled individually. We used Quaternion, which is a sub-algebra of GA, and effectively handled color image processing by using Fourier transformation. After deriving the calculation process of the QFT with strong security by Arnold scrambling and chaotic encryption, this paper proposes a digital watermarking algorithm that resists geometric attacks by using color images as carriers. The robustness and quality of the proposed watermarking algorithm is tested with different with many statistical measures. Experimental outcomes show that the proposed approach is the best to solve conflict problems between quality and robustness. Also, the proposed approach exhibits worthy robustness against many attacks, such as, conventional attacks, and geometrical attacks.

Deep Learning Local Descriptor for Image Splicing Detection and Localization

Abstract: In this paper, a novel image splicing detection and localization scheme is proposed based on the local feature descriptor which is learned by deep convolutional neural network (CNN). A two-branch CNN, which serves as an expressive local descriptor is presented and applied to automatically learn hierarchical representations from the input RGB color or grayscale test images. The first layer of the proposed CNN model is used to suppress the effects of image contents and extract the diverse and expressive residual features, which is deliberately designed for image splicing detection applications. In specific, the kernels of the first convolutional layer are initialized with an optimized combination of the 30 linear high-pass filters used in calculation of residual maps in spatial rich model (SRM), and is fine-tuned through a constrained learning strategy to retain the high-pass filtering properties for the learned kernels. Both the contrastive loss and cross entropy loss are utilized to jointly improve the generalization ability of the proposed CNN model. With the block-wise dense features for a test image extracted by the pre-trained CNN-based local descriptor, an effective feature fusion strategy, known as block pooling, is adopted to obtain the final discriminative features for image splicing detection with SVM. Based on the pre-trained CNN model, an image splicing localization scheme is further developed by incorporating the fully connected conditional random field (CRF). Extensive experimental results on several public datasets show that the proposed CNN based scheme outperforms some state-of-the-art methods not only in image splicing detection and localization performance, but also in robustness against JPEG compression.

Reconstruction of 3D Microstructures from 2D Images via Transfer Learning

Abstract: Computational analysis, modeling, and prediction of many phenomena in materials require a three-dimensional (3D) microstructure sample that embodies the salient features of the material system under study Since acquiring 3D microstructural images is expensive and time-consuming, an alternative approach is to extrapolate a 2D image (aka exemplar) into a virtual 3D sample and thereafter use the 3D image in the analyses and design In this paper, we introduce an efficient and novel approach based on transfer learning to accomplish this extrapolation-based reconstruction for a wide range of microstructures including alloys, porous media, and polycrystalline We cast the reconstruction task as an optimization problem where a random 3D image is iteratively refined to match its microstructural features to those of the exemplar VGG19, a pre-trained deep convolutional neural network, constitutes the backbone of this optimization where it is used to obtain the microstructural features and construct the objective function By augmenting the architecture of VGG19 with a permutation operator, we enable it to take 3D images as inputs and generate a collection of 2D features that approximate an underlying 3D feature map We demonstrate the applications of our approach with nine examples on various microstructure samples and image types (grayscale, binary, and RGB) As measured by independent statistical metrics, our approach ensures the statistical equivalency between the 3D reconstructed samples and the corresponding 2D exemplar quite well

YOLOv3 and YOLOv4: Multiple Object Detection for Surveillance Applications

Abstract: Object detection algorithm such as You Only Look Once (YOLOv3 and YOLOv4) is implemented for traffic and surveillance applications. A neural network consists of input with minimum one hidden and output layer. Multiple object dataset (KITTI image and video), which consists of classes of images such as Car, truck, person, and two-wheeler captured during RGB and grayscale images. The dataset is composed (image and video) of varying illumination. YOLO model variants such as YOLOv3 is implemented for image and YOLOv4 for video dataset. Obtained results show that the algorithm effectively detects the objects approximately with an accuracy of 98% for image dataset and 99% for video dataset.

Saliency Detection via the Improved Hierarchical Principal Component Analysis Method

Abstract: Aiming at the problems of intensive background noise, low accuracy, and high computational complexity of the current significant object detection methods, the visual saliency detection algorithm based on Hierarchical Principal Component Analysis (HPCA) has been proposed in the paper. Firstly, the original RGB image has been converted to a grayscale image, and the original grayscale image has been divided into eight layers by the bit surface stratification technique. Each image layer contains significant object information matching the layer image features. Secondly, taking the color structure of the original image as the reference image, the grayscale image is reassigned by the grayscale color conversion method, so that the layered image not only reflects the original structural features but also effectively preserves the color feature of the original image. Thirdly, the Principal Component Analysis (PCA) has been performed on the layered image to obtain the structural difference characteristics and color difference characteristics of each layer of the image in the principal component direction. Fourthly, two features are integrated to get the saliency map with high robustness and to further refine our results; the known priors have been incorporated on image organization, which can place the subject of the photograph near the center of the image. Finally, the entropy calculation has been used to determine the optimal image from the layered saliency map; the optimal map has the least background information and most prominently saliency objects than others. The object detection results of the proposed model are closer to the ground truth and take advantages of performance parameters including precision rate (PRE), recall rate (REC), and - measure (FME). The HPCA model’s conclusion can obviously reduce the interference of redundant information and effectively separate the saliency object from the background. At the same time, it had more improved detection accuracy than others.

Infrared Moving Small-Target Detection Using Spatial–Temporal Local Difference Measure

Abstract: Effectiveness and false alarm (FA) suppression are key issues in infrared (IR) moving small-target detection. In this letter, we propose a novel spatial–temporal local difference measure (STLDM) algorithm to detect a moving IR small target. The method we propose involves three steps. First, we block off three frames in a certain range of time (temporal) domain. Next, in a 3-D spatial–temporal domain, we analyze the local grayscale intensity difference of the small targets moving between the three frames and calculate the difference between the grayscale intensity in the center area and the grayscale intensity in the eight directions in the area surrounding the target. Finally, we segment the small target in a detection result map. The results of our experiment demonstrate that our proposed STLDM method has a higher rate of detection of IR moving small targets, as well as fewer FAs than other existing methods.

Rethinking CNN-Based Pansharpening: Guided Colorization of Panchromatic Images via GANs

Abstract: Convolutional Neural Networks (CNN)-based approaches have shown promising results in pansharpening of satellite images in recent years. However, they still exhibit limitations in producing high-quality pansharpening outputs. To that end, we propose a new self-supervised learning framework, where we treat pansharpening as a colorization problem, which brings an entirely novel perspective and solution to the problem compared to existing methods that base their solution solely on producing a super-resolution version of the multispectral image. Whereas CNN-based methods provide a reduced resolution panchromatic image as input to their model along with reduced resolution multispectral images, hence learn to increase their resolution together, we instead provide the grayscale transformed multispectral image as input, and train our model to learn the colorization of the grayscale input. We further address the fixed downscale ratio assumption during training, which does not generalize well to the full-resolution scenario. We introduce a noise injection into the training by randomly varying the downsampling ratios. Those two critical changes, along with the addition of adversarial training in the proposed PanColorization Generative Adversarial Networks (PanColorGAN) framework, help overcome the spatial detail loss and blur problems that are observed in CNN-based pansharpening. The proposed approach outperforms the previous CNN-based and traditional methods as demonstrated in our experiments.

Ring oscillator as confusion – diffusion agent: a complete TRNG drove image security

Abstract: The utility of true random number generators (TRNGs) is not only restricted to session key generation, nonce generation, OTP generation etc. in cryptography. In the proposed work, two ring oscillator (RO) based TRNG structures adopting identical and non-identical ring of inverters have alone been employed for confusion (scrambling) and diffusion (intensity variation) processes for encrypting the greyscale and RGB images. Cyclone IVE EP4CE115F29C7 FPGA was utilised to generate a couple of random synthetic images using the two RO architectures which took a maximum of 520 combinational units and 543 logic registers. The suggested scheme of image encryption was tested on 100 test greyscale images of size 256 × 256. This non-chaos influenced image ciphering has resulted in an approximate average entropy of 7.99 and near-zero correlation figures for the greyscale & RGB cipher images. The attack resistance capability was checked by performing various occlusion and noise attacks on encrypted images.

A Superpixel-Based Variational Model for Image Colorization

Abstract: Image colorization refers to a computer-assisted process that adds colors to grayscale images. It is a challenging task since there is usually no one-to-one correspondence between color and local texture. In this paper, we tackle this issue by exploiting weighted nonlocal self-similarity and local consistency constraints at the resolution of superpixels. Given a grayscale target image, we first select a color source image containing similar segments to target image and extract multi-level features of each superpixel in both images after superpixel segmentation. Then a set of color candidates for each target superpixel is selected by adopting a top-down feature matching scheme with confidence assignment. Finally, we propose a variational approach to determine the most appropriate color for each target superpixel from color candidates. Experiments demonstrate the effectiveness of the proposed method and show its superiority to other state-of-the-art methods. Furthermore, our method can be easily extended to color transfer between two color images.

Multi-dimensional particle swarm optimization for robust blind image watermarking using intertwining logistic map and hybrid domain

Abstract: Robust blind watermarking has become a vital means of copyright protection, and this paper presents a new optimal robust and blind watermarking method of grayscale images based on intertwining logistic map and a variant of particle swarm optimization (PSO) in a hybrid domain. In the proposed approach, firstly a host image is decomposed by discrete wavelet transform, and discrete cosine transform (DCT) is applied to insensitive LH and HL subbands according to human visual model. Then, optimum frequency spectra in the DCT domain are chosen to form a feature matrix for improving the robustness and transparency of watermark. Finally, a shuffled watermark image using the chaotic logistic map is inserted by modifying the largest singular values of a feature matrix pair in the singular value decomposition domain. An improved version of PSO is employed to perform multi-dimensional optimization for selection of the most qualified DCT coefficients and estimation of watermark embedding strength in terms of their significant influence on imperceptibility and robustness. The security of the proposed method is provided by intertwining logistic map. Experimental results on standard test images indicate that PSO searches efficiently optimal values of watermark embedding strength and the most suitable DCT subbands, and the proposed watermarking algorithm performs much better than the other compared schemes in imperceptibility and robustness objectives.

A Pixel-Based Digital Medical Images Protection Using Genetic Algorithm with LSB Watermark Technique

Abstract: In recent years, especially when these images are transmitted via a network, digital image security has attracted attention. The 2D barcode is designed to encrypt patient information. In this paper, we proposed a grayscale medical image encryption technology based on the characteristics of genetic algorithm (GAS) using LSB technology. The patient information will be converting into 2D barcode and after that, the original image is embedded with 2D barcode using LSB technique. The LSB technique is more suitable for medical imaging. The resulting image is subjected to a genetic algorithm. In this purpose design, we combined the genetic with LSB technique to make the patient image and its information more secured.

DeepOrganNet: On-the-Fly Reconstruction and Visualization of 3D / 4D Lung Models from Single-View Projections by Deep Deformation Network

Abstract: This paper introduces a deep neural network based method, i.e., DeepOrganNet, to generate and visualize fully high-fidelity 3D / 4D organ geometric models from single-view medical images with complicated background in real time. Traditional 3D / 4D medical image reconstruction requires near hundreds of projections, which cost insufferable computational time and deliver undesirable high imaging / radiation dose to human subjects. Moreover, it always needs further notorious processes to segment or extract the accurate 3D organ models subsequently. The computational time and imaging dose can be reduced by decreasing the number of projections, but the reconstructed image quality is degraded accordingly. To our knowledge, there is no method directly and explicitly reconstructing multiple 3D organ meshes from a single 2D medical grayscale image on the fly. Given single-view 2D medical images, e.g., 3D / 4D-CT projections or X-ray images, our end-to-end DeepOrganNet framework can efficiently and effectively reconstruct 3D / 4D lung models with a variety of geometric shapes by learning the smooth deformation fields from multiple templates based on a trivariate tensor-product deformation technique, leveraging an informative latent descriptor extracted from input 2D images. The proposed method can guarantee to generate high-quality and high-fidelity manifold meshes for 3D / 4D lung models; while, all current deep learning based approaches on the shape reconstruction from a single image cannot. The major contributions of this work are to accurately reconstruct the 3D organ shapes from 2D single-view projection, significantly improve the procedure time to allow on-the-fly visualization, and dramatically reduce the imaging dose for human subjects. Experimental results are evaluated and compared with the traditional reconstruction method and the state-of-the-art in deep learning, by using extensive 3D and 4D examples, including both synthetic phantom and real patient datasets. The efficiency of the proposed method shows that it only needs several milliseconds to generate organ meshes with 10K vertices, which has great potential to be used in real-time image guided radiation therapy (IGRT).

Display device and driving method thereof

Abstract: A display device and driving method thereof are disclosed. The display device includes a pixel emitting light at a luminance corresponding to an output grayscale value and a color shifter for converting an input grayscale value into the output grayscale value based on output color gamut information. The color shifter includes an offset storage unit storing reference color gamut information and offset information; and a color gamut determination unit that determines the output color gamut information using the reference color gamut information and the offset information when the color shift level corresponds to a value between the reference level and the shift levels, and determines tire output color gamut information using second offset information in which the offset information is inverted and the reference color gamut information when the color shift level is not between the reference level and the shift levels.

An Efficient Framework for Visible-Infrared Cross Modality Person Re-Identification

Abstract: Visible–infrared cross-modality person re-identification (VI-ReId) is an essential task for video surveillance in poorly illuminated or dark environments. Despite many recent studies on person re-identification in the visible domain (ReId), there are few studies dealing specifically with VI-ReId. Besides challenges that are common for both ReId and VI-ReId such as pose/illumination variations, background clutter and occlusion, VI-ReId has additional challenges as color information is not available in infrared images. As a result, the performance of VI-ReId systems is typically lower than that of ReId systems. In this work, we propose a four-stream framework to improve VI-ReId performance. We train a separate deep convolutional neural network in each stream using different representations of input images. We expect that different and complementary features can be learned from each stream. In our framework, grayscale and infrared input images are used to train the ResNet in the first stream. In the second stream, RGB and three-channel infrared images (created by repeating the infrared channel) are used. In the remaining two streams, we use local pattern maps as input images. These maps are generated utilizing local Zernike moments transformation. Local pattern maps are obtained from grayscale and infrared images in the third stream and from RGB and three-channel infrared images in the last stream. We improve the performance of the proposed framework by employing a re-ranking algorithm for post-processing. Our results indicate that the proposed framework outperforms current state-of-the-art with a large margin by improving Rank-1/mAP by 29 . 79 % ∕ 30 . 91 % on SYSU-MM01 dataset, and by 9 . 73 % ∕ 16 . 36 % on RegDB dataset.

Multi-Channel and Multi-Model-Based Autoencoding Prior for Grayscale Image Restoration

Abstract: Image restoration (IR) is a long-standing challenging problem in low-level image processing. It is of utmost importance to learn good image priors for pursuing visually pleasing results. In this paper, we develop a multi-channel and multi-model-based denoising autoencoder network as image prior for solving IR problem. Specifically, the network that trained on RGB-channel images is used to construct a prior at first, and then the learned prior is incorporated into single-channel grayscale IR tasks. To achieve the goal, we employ the auxiliary variable technique to integrate the higher-dimensional network-driven prior information into the iterative restoration procedure. In addition, according to the weighted aggregation idea, a multi-model strategy is put forward to enhance the network stability that favors to avoid getting trapped in local optima. Extensive experiments on image deblurring and deblocking tasks show that the proposed algorithm is efficient, robust, and yields state-of-the-art restoration quality on grayscale images.

Two-Stage Sketch Colorization With Color Parsing

Abstract: We implement high-quality sketch colorization using two-stage conditional generative adversarial network (GAN) training based on different intermediate features. The intermediate features used in autonomous colorization are the grayscale parsing and interval pixel-level color parsing. The autonomous colorization based on grayscale parsing feature can learn the spacial topology of pixels in the first stage to guide the colorization in the second stage. The autonomous colorization based on pixel-level color parsing feature can learn the color information of few feature points in the first stage to guide the colorization of all pixels in the second stage. Additionally, we use the intermediate feature of sampling points as constraint and achieve the color reconstruction using Laplacian mesh editing as a special second stage. Furthermore, the interactive colorization uses the superpixel color parsing as the intermediate feature. Specifically, we use the simple linear iterative cluster (SLIC) to obtain a palette that maintains the edges in the first stage to guide the colorization in the second stage. As for evaluation metrics, we propose a color-coded local binary pattern (CCLBP) score based on color distances from the first-order 8 pixels to the central pixel, to measure the degrees of color blurring and mess. We also propose a light-sensitivity (LS) score based on the reversed grayscale map, to measure the degrees of auto painting and overfitting of the color hint. According to the L1 distances between the original and generated color images based on these scores, compared with state-of-the-art methods including one stage approaches such as pix2pix and PaintsChainer and two-stage approaches such as Style2Paints and DeepColor, our model can achieve the highest-quality autonomous colorization. Moreover, compared with pix2pix, PaintsChainer and Style2Paints with color hints, according to the proposed objective evaluation as well as the user visual study, our model can achieve the highest-quality interactive colorization as well.