Showing papers on "Grayscale published in 2021"
23 Aug 2021
TL;DR: Wang et al. as discussed by the authors proposed a strong baseline model SwinIR for image restoration based on the Swin Transformer, which consists of three parts: shallow feature extraction, deep feature extraction and high-quality image reconstruction.
Abstract: Image restoration is a long-standing low-level vision problem that aims to restore high-quality images from low-quality images (e.g., downscaled, noisy and compressed images). While state-of-the-art image restoration methods are based on convolutional neural networks, few attempts have been made with Transformers which show impressive performance on high-level vision tasks. In this paper, we propose a strong baseline model SwinIR for image restoration based on the Swin Transformer. SwinIR consists of three parts: shallow feature extraction, deep feature extraction and high-quality image reconstruction. In particular, the deep feature extraction module is composed of several residual Swin Transformer blocks (RSTB), each of which has several Swin Transformer layers together with a residual connection. We conduct experiments on three representative tasks: image super-resolution (including classical, lightweight and real-world image super-resolution), image denoising (including grayscale and color image denoising) and JPEG compression artifact reduction. Experimental results demonstrate that SwinIR outperforms state-of-the-art methods on different tasks by $\textbf{up to 0.14$\sim$0.45dB}$, while the total number of parameters can be reduced by $\textbf{up to 67%}$.
1,064 citations
TL;DR: This paper proposes a Homogeneous Augmented Tri-Modal (HAT) learning method for VI-ReID, where an auxiliary grayscale modality is generated from their homogeneous visible images, without additional training process, and significantly outperforms the current state-of-the-art by a large margin.
Abstract: Matching person images between the daytime visible modality and night-time infrared modality (VI-ReID) is a challenging cross-modality pedestrian retrieval problem. Existing methods usually learn the multi-modality features in raw image, ignoring the image-level discrepancy. Some methods apply GAN technique to generate the cross-modality images, but it destroys the local structure and introduces unavoidable noise. In this paper, we propose a Homogeneous Augmented Tri-Modal (HAT) learning method for VI-ReID, where an auxiliary grayscale modality is generated from their homogeneous visible images, without additional training process. It preserves the structure information of visible images and approximates the image style of infrared modality. Learning with the grayscale visible images enforces the network to mine structure relations across multiple modalities, making it robust to color variations. Specifically, we solve the tri-modal feature learning from both multi-modal classification and multi-view retrieval perspectives. For multi-modal classification, we learn a multi-modality sharing identity classifier with a parameter-sharing network, trained with a homogeneous and heterogeneous identification loss. For multi-view retrieval, we develop a weighted tri-directional ranking loss to optimize the relative distance across multiple modalities. Incorporated with two invariant regularizers, HAT simultaneously minimizes multiple modality variations. In-depth analysis demonstrates the homogeneous grayscale augmentation significantly outperforms the current state-of-the-art by a large margin.
139 citations
TL;DR: This paper proposes a new color image encryption algorithm (CIEA) that sufficiently considers the properties of the color image and Latin square and designs a two-dimensional chaotic system called 2D-LSM to address the weaknesses of existing chaotic systems.
Abstract: Recently, many image encryption schemes have been developed using Latin squares. When encrypting a color image, these algorithms treat the color image as three greyscale images and encrypt these greyscale images one by one using the Latin squares. Obviously, these algorithms do not sufficiently consider the inner connections between the color image and Latin square and thus result in many redundant operations and low efficiency. To address this issue, in this paper, we propose a new color image encryption algorithm (CIEA) that sufficiently considers the properties of the color image and Latin square. First, we propose a two-dimensional chaotic system called 2D-LSM to address the weaknesses of existing chaotic systems. Then, we design a new CIEA using orthogonal Latin squares and 2D-LSM. The proposed CIEA can make full use of the inherent connections of the orthogonal Latin squares and color image and executes the encryption process in the pixel level. Simulation and security analysis results show that the proposed CIEA has a high level of security and can outperform some representative image encryption algorithms.
112 citations
TL;DR: A novel triple-image encryption and hiding algorithm is proposed by combining a 2D chaotic system, compressive sensing (CS) and the 3D discrete cosine transform (DCT) to obtain a visually meaningful cipher image.
106 citations
TL;DR: Simulation and analysis results proved that the proposed chaotic color/grayscale image encryption algorithm has a promising security performance and has a high ability to resist statistical and differential attacks.
Abstract: Image encryption has become the essential way to secure image information with the high frequency of multimedia information exchange on the Internet. In this paper, an effective chaotic color/grayscale image encryption algorithm is proposed. The algorithm uses a hybrid 2D composite chaotic map combined with a sine–cosine cross-chaotic map for the transformation required to scramble the image as a confusion phase. As for the diffusion phase, a 1D combined Logistic-Tent chaotic map is used to generate a chaotic self-diffusion matrix that is bitwise XORed with the scrambled image to produce the final cipher image. The proposed algorithm combines the merits of both 1D and 2D chaotic maps; it has a simple structure, easy implementation, and excellent chaotic features making its chaotic orbits more unpredictable for introducing more security. The simulation and analysis results proved that the algorithm has a promising security performance and has a high ability to resist statistical and differential attacks.
74 citations
TL;DR: In this article, a residual dense neural network (RDUNet) was proposed for image denoising based on the densely connected hierarchical network, where the encoding and decoding layers consist of densely connected convolutional layers to reuse the feature maps and local residual learning to avoid the vanishing gradient problem and speed up the learning process.
Abstract: In recent years, convolutional neural networks have achieved considerable success in different computer vision tasks, including image denoising. In this work, we present a residual dense neural network (RDUNet) for image denoising based on the densely connected hierarchical network. The encoding and decoding layers of the RDUNet consist of densely connected convolutional layers to reuse the feature maps and local residual learning to avoid the vanishing gradient problem and speed up the learning process. Moreover, global residual learning is adopted such that, instead of directly predicting the denoised image, the model predicts the residual noise of the corrupted image. The algorithm was trained for the case of additive white Gaussian noise and using a wide range of noise levels. Hence, one advantage of the proposal is that the denoising process does not require prior knowledge about the noise level. In order to evaluate the model, we conducted several experiments with natural image databases available online, achieving competitive results compared with state-of-the-art networks for image denoising. For comparison purpose, we use additive Gaussian noise with levels 10, 30, 50. In the case of grayscale images, we achieved PSNR of 34.39, 29.11, 26.99, and SSIM of 0.9297, 0.8193, 0.7491. For color images we obtained PSNR of 36.68, 31.43, 29.12, and SSIM of 0.9600, 0.8961, 0.8465.
65 citations
TL;DR: In this article, the Jones matrix treatment of compound metapixels consisting of double atoms with tailored in-plane orientation sum and difference allows point-by-point configuring of both the amplitude and polarization rotations of the output beam in an analytical and linear form.
Abstract: Malus' law regulating the intensity of light when passed through a polarizer, forms the solid basis for image steganography based on orthogonal polarizations of light to convey hidden information without adverse perceptions, which underpins important practices in information encryptions, anti-counterfeitings, and security labels. Unfortunately, the restriction to orthogonal states being taken for granted in the common perceptions fails to advance cryptoinformation to upgraded levels of security. By introducing a vectorial compound metapixel design, arbitrary nonorthogonal polarization multiplexing of independent grayscale images with high fidelity and strong concealment is demonstrated. The Jones matrix treatment of compound metapixels consisting of double atoms with tailored in-plane orientation sum and difference allows point-by-point configuring of both the amplitude and polarization rotations of the output beam in an analytical and linear form. With this, both multiplexing two continuous grayscale images in arbitrary nonorthogonal polarization angles and concealing grayscale image on another in an arbitrary disclosure angle window are experimentally demonstrated in the visible TiO2 metasurface platform. The methods shed new light on multifarious metaoptics by harnessing the new degree of freedom and unlock the full potential of metasurface polarization optics.
57 citations
TL;DR: A novel grayscale image cryptosystem based on hybrid chaotic maps that has proper encryption and decryption processing time, unified average changing intensity (UACI), number of pixel change rate (NPCR), and extensive security analysis for kind of images is proposed.
Abstract: In this paper, a novel grayscale image cryptosystem based on hybrid chaotic maps is proposed. The scheme employs both confusion phase to scramble the location of pixels and diffusion phase for changing the content of pixels in consecutive manner. In this scheme, Arnold’s cat map is introduced to perform confusion operation and the principle of diffusion is achieved by using the proper selection of combined Sine map, Logistic map, and Tent map. Furthermore, exclusive OR (XOR), exchange, and transform operations are used to enhance the efficiency of diffusion phase. Accordingly, the use of chaotic maps and XOR operation provides a dual layer of security. Depending on the average absolute value of horizontal, vertical, and diagonal correlation coefficient of plain image as well as bifurcation properties of chaotic maps, one of the mentioned chaotic maps is selected for diffusion phase. First, original gray scale image matrix is extended to square matrix by adding the sequences generated with proper chaotic maps to implement the first step of diffusion phase. Then the Arnold’s cat map changes pixels location of new extended matrix by means of certain equation as confusion phase. The encrypted image is generated after applying XOR, exchange and transform operations on the content of pixels as second step of diffusion phase. Thus the system is able to build several more complicated chaotic structures. In addition the encryption and decryption processing time directly depend on the value of correlation coefficient of original image. Plain images with less correlation coefficient have less encryption and decryption processing time, and vice versa. Compared with several existing methods, the proposed scheme has more better properties, including wider chaotic ranges and more complex chaotic behavior. Experimental results show that the proposed system has proper encryption and decryption processing time, unified average changing intensity (UACI), number of pixel change rate (NPCR), and extensive security analysis for kind of images.
56 citations
TL;DR: A grayscale enhancement colorization network (GECNet) is proposed to bridge the modality gap by retaining the structure of the colored image which contains rich information and demonstrates the superiority of the proposed method over the state-of-the-arts.
Abstract: Visible-infrared person re-identification (VI-ReID) is an emerging and challenging cross-modality image matching problem because of the explosive surveillance data in night-time surveillance applications. To handle the large modality gap, various generative adversarial network models have been developed to eliminate the cross-modality variations based on a cross-modal image generation framework. However, the lack of point-wise cross-modality ground-truths makes it extremely challenging to learn such a cross-modal image generator. To address these problems, we learn the correspondence between single-channel infrared images and three-channel visible images by generating intermediate grayscale images as auxiliary information to colorize the single-modality infrared images. We propose a grayscale enhancement colorization network (GECNet) to bridge the modality gap by retaining the structure of the colored image which contains rich information. To simulate the infrared-to-visible transformation, the point-wise transformed grayscale images greatly enhance the colorization process. Our experiments conducted on two visible-infrared cross-modality person re-identification datasets demonstrate the superiority of the proposed method over the state-of-the-arts.
53 citations
TL;DR: In this paper, a lightweight deep underwater object detection network is proposed to solve the problem of underwater color absorption by transforming color images to the corresponding grayscale images to enhance the object detection performance with lower computational complexity.
Abstract: Underwater image processing has been shown to exhibit significant potential for exploring underwater environments. It has been applied to a wide variety of fields, such as underwater terrain scanning and autonomous underwater vehicles (AUVs)-driven applications, such as image-based underwater object detection. However, underwater images often suffer from degeneration due to attenuation, color distortion, and noise from artificial lighting sources as well as the effects of possibly low-end optical imaging devices. Thus, object detection performance would be degraded accordingly. To tackle this problem, in this article, a lightweight deep underwater object detection network is proposed. The key is to present a deep model for jointly learning color conversion and object detection for underwater images. The image color conversion module aims at transforming color images to the corresponding grayscale images to solve the problem of underwater color absorption to enhance the object detection performance with lower computational complexity. The presented experimental results with our implementation on the Raspberry pi platform have justified the effectiveness of the proposed lightweight jointly learning model for underwater object detection compared with the state-of-the-art approaches.
52 citations
TL;DR: In this article, the authors proposed an algorithm with the combination of the oriented FAST and rotated BRIEF (ORB) features and Local Binary Patterns (LBP) features extracted from facial expression.
Abstract: Emotion plays an important role in communication. For human-computer interaction, facial expression recognition has become an indispensable part. Recently, deep neural networks (DNNs) are widely used in this field and they overcome the limitations of conventional approaches. However, application of DNNs is very limited due to excessive hardware specifications requirement. Considering low hardware specifications used in real-life conditions, to gain better results without DNNs, in this paper, we propose an algorithm with the combination of the oriented FAST and rotated BRIEF (ORB) features and Local Binary Patterns (LBP) features extracted from facial expression. First of all, every image is passed through face detection algorithm to extract more effective features. Second, in order to increase computational speed, the ORB and LBP features are extracted from the face region; specifically, region division is innovatively employed in the traditional ORB to avoid the concentration of the features. The features are invariant to scale and grayscale as well as rotation changes. Finally, the combined features are classified by Support Vector Machine (SVM). The proposed method is evaluated on several challenging databases such as Cohn-Kanade database (CK+), Japanese Female Facial Expressions database (JAFFE), and MMI database; experimental results of seven emotion state (neutral, joy, sadness, surprise, anger, fear, and disgust) show that the proposed framework is effective and accurate.
TL;DR: The proposed modular neural network approach, which divides features to achieve specialized analysis in the modules for digital image analysis and classification, achieves high classification accuracy after evaluating the neuro-fuzzy model with three large datasets of chest X-rays.
Abstract: This paper describes a new hybrid approach, based on modular artificial neural networks with fuzzy logic integration, for the diagnosis of pulmonary diseases such as pneumonia and lung nodules. In particular, the proposed approach analyzes medical images, which are digitized chest X-rays, focusing on a classification method based on descriptors, such as grayscale histogram features, gray-level co-occurrence matrix (GLCM) texture-based features, and local binary pattern texture features. Then, to perform feature reduction, a multi-objective genetic algorithm is used to obtain an optimized neuro-fuzzy classifier, which is able to classify the pathology found in the analyzed chest X-ray. The main contribution of this paper is the proposed modular neural network approach, which divides features to achieve specialized analysis in the modules for digital image analysis and classification. The proposed approach achieves high classification accuracy after evaluating the neuro-fuzzy model with three large datasets of chest X-rays.
TL;DR: Experimental results show that the marked medical images generate by the proposed RDHACEM algorithm have better visual quality and larger embedding capacity in the ROI than those generated by the up-to-date RDH methods.
TL;DR: A visually secure image encryption scheme based on two-dimensional compressive sensing and integer wavelet transform (IWT) embedding that can ensure data security and increase the speed of the decryption algorithm, and the IWT embedding can achieve visual security without loss of information is proposed.
TL;DR: The experimental results show that the proposed improved convolutional neural network method can effectively improve the fault detection accuracy of rolling bearings under variable working conditions, which is superior to the existing methods.
TL;DR: A scheme to develop the image over-segmentation task is introduced, it considers the pixels of an image as intuitive fuzzy sets and develops an intuitionistic clustering process of them and provides a method for extracting superpixels with greater adherence to the edges of the regions.
TL;DR: The proposed framework consists of a generative model that is responsible for colorizing grayscale and dark images, followed by a classification model, which highlighted the significant negative impact of the absence of color information and proved the vital role of the framework.
Abstract: Getting to an ear recognition model that can overcome all challenges and difficulties was and still the main objective of researchers for years. One particular problem we highlight here, which is the loss of color information during the test phase, in other words, feeding grayscale, mono-color or dark test images to a model that is trained with colored images. In this paper, we propose a framework that involves conditional Deep Convolutional Generative Adversarial Networks (DCGAN), and Convolutional Neural Network (CNN) models. The proposed framework consists of a generative model that is responsible for colorizing grayscale and dark images, followed by a classification model. The performance of the proposed framework has been evaluated using the constrained AMI and the unconstrained AWE ear datasets. Performance metrics have been measured under three experimental scenarios, the obtained results highlighted the significant negative impact of the absence of color information and proved the vital role of our framework.
TL;DR: A comparison in the coefficient correlation values is drawn to evaluate the performance of the proposed algorithm with respect to many lately proposed image encryption schemes.
Abstract: Image encryption converts a meaningful image into some random arrangement of pixel intensities. That means, the intelligible property of an image is destroyed. Taking into consideration excessively large time and space complexity required by the image encryption algorithm using multiple chaotic systems, this paper proposes an image encryption method in which employs three chaotic sequence to achieve fairly high level of encryption. Novelty of the proposed approach lies in the designed algorithm to achieve both permutation and substitution processes of image encryption. In the end, a comparison in the coefficient correlation values is drawn to evaluate the performance of the proposed algorithm with respect to many lately proposed image encryption schemes.
TL;DR: A novel multi-modal medical image fusion method based on Non-Subsampled Shearlet Transform (NSST) called Denoised Optimum B-Spline shearlet Image Fusion(DOBSIF) that is based on real-time and standard radiological datasets is proposed.
TL;DR: Experimental results show that SCGAN can generate more reasonable colorized images than state-of-the-art techniques and proposes a novel saliency map-based guidance method.
Abstract: Given a grayscale photograph, the colorization system estimates a visually plausible colorful image. Conventional methods often use semantics to colorize grayscale images. However, in these methods, only classification semantic information is embedded, resulting in semantic confusion and color bleeding in the final colorized image. To address these issues, we propose a fully automatic Saliency Map-guided Colorization with Generative Adversarial Network (SCGAN) framework. It jointly predicts the colorization and saliency map to minimize semantic confusion and color bleeding in the colorized image. Since the global features from pre-trained VGG-16-Gray network are embedded to the colorization encoder, the proposed SCGAN can be trained with much less data than state-of-the-art methods to achieve perceptually reasonable colorization. In addition, we propose a novel saliency map-based guidance method. Branches of the colorization decoder are used to predict the saliency map as a proxy target. Moreover, two hierarchical discriminators are utilized for the generated colorization and saliency map, respectively, in order to strengthen visual perception performance. The proposed system is evaluated on ImageNet validation set. Experimental results show that SCGAN can generate more reasonable colorized images than state-of-the-art techniques.
TL;DR: For vibration signal of rolling bearing with long time series obtained from multiple sampling points, hybrid multimodal fusion with deep learning is proposed for fault diagnosis and can achieve higher fault diagnosis accuracy.
TL;DR: Zhang et al. as discussed by the authors used an improved multiview FCM clustering algorithm (IMV-FCM) to improve the segmentation accuracy of brain MRI images.
Abstract: Background: The brain magnetic resonance imaging (MRI) image segmentation method mainly refers to the division of brain tissue, which can be divided into tissue parts such as white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) The segmentation results can provide a basis for medical image registration, 3D reconstruction, and visualization Generally, MRI images have defects such as partial volume effects, uneven grayscale, and noise Therefore, in practical applications, the segmentation of brain MRI images has difficulty obtaining high accuracy Materials and Methods: The fuzzy clustering algorithm establishes the expression of the uncertainty of the sample category and can describe the ambiguity brought by the partial volume effect to the brain MRI image, so it is very suitable for brain MRI image segmentation (B-MRI-IS) The classic fuzzy c-means (FCM) algorithm is extremely sensitive to noise and offset fields If the algorithm is used directly to segment the brain MRI image, the ideal segmentation result cannot be obtained Accordingly, considering the defects of MRI medical images, this study uses an improved multiview FCM clustering algorithm (IMV-FCM) to improve the algorithm's segmentation accuracy of brain images IMV-FCM uses a view weight adaptive learning mechanism so that each view obtains the optimal weight according to its cluster contribution The final division result is obtained through the view ensemble method Under the view weight adaptive learning mechanism, the coordination between various views is more flexible, and each view can be adaptively learned to achieve better clustering effects Results: The segmentation results of a large number of brain MRI images show that IMV-FCM has better segmentation performance and can accurately segment brain tissue Compared with several related clustering algorithms, the IMV-FCM algorithm has better adaptability and better clustering performance
TL;DR: In this article, a color and grayscale generation approach based on the tuning of a single nanostructure geometry is presented. But, achieving different shades of gray and control of color saturation remain challenging.
Abstract: Sculpting nanostructures into different geometries in either one or two dimensions produces a wide range of colorful elements in microscopic prints. However, achieving different shades of gray and control of color saturation remain challenging. Here, we report a complete approach to color and grayscale generation based on the tuning of a single nanostructure geometry. Through two-photon polymerization lithography, we systematically investigated color generation from the basic single nanopillar geometry in low-refractive-index (n < 1.6) material. Grayscale and full color palettes were achieved that allow decomposition onto hue, saturation, and brightness values. This approach enabled the "painting" of arbitrary colorful and grayscale images by mapping desired prints to precisely controllable parameters during 3D printing. We further extend our understanding of the scattering properties of the low-refractive-index nanopillar to demonstrate grayscale inversion and color desaturation and steganography at the level of single nanopillars.
TL;DR: A robust Elliptic curve based image encryption and authentication model for both grayscale and color images has been proposed that is robust with high resilience against statistical, differential, chosen-plaintext(CPA), known-plain text(KPA) and occlusion attacks.
Abstract: Many researchers have used the properties of the popular Elliptic Curve Cryptography(ECC) to devise a stronger and faster image encryption algorithm to assure the secrecy of images during online transmission. In this paper, a robust Elliptic curve based image encryption and authentication model for both grayscale and color images has been proposed. The model uses the secure Elliptic Curve Diffie-Hellman(ECDH) key exchange to compute a shared session key along with the improved ElGamal encoding scheme. 3D and 4D Arnold Cat maps are used to effectively scramble and transform the values of plain image pixels. A well-structured digital signature is used to verify the authenticity of the encrypted image prior to decryption. The model produces good-quality cipher images with an average entropy of 7.9993 for grayscale and 7.99925 for the individual components of color images. The model has high average NPCR of 99.6%, average UACI of 33.3% and low correlation for both grayscale and color images. The model has low computational costs with minimized point multiplication operations. The proposed model is robust with high resilience against statistical, differential, chosen-plaintext(CPA), known-plaintext(KPA) and occlusion attacks.
TL;DR: In this article, the color information is encoded into a set of Gaussian distributed latent variables via INNs, and the original color image can be efficiently recovered by randomly re-sampling a new set of distributed variables, together with the synthetic grayscale.
Abstract: Invertible image decolorization is a useful color compression technique to reduce the cost in multimedia systems. Invertible decolorization aims to synthesize faithful grayscales from color images, which can be fully restored to the original color version. In this paper, we propose a novel color compression method to produce invertible grayscale images using invertible neural networks (INNs). Our key idea is to separate the color information from color images, and encode the color information into a set of Gaussian distributed latent variables via INNs. By this means, we force the color information lost in grayscale generation to be independent of the input color image. Therefore, the original color version can be efficiently recovered by randomly re-sampling a new set of Gaussian distributed variables, together with the synthetic grayscale, through the reverse mapping of INNs. To effectively learn the invertible grayscale, we introduce the wavelet transformation into a UNet-like INN architecture, and further present a quantization embedding to prevent the information omission in format conversion, which improves the generalizability of the framework in real-world scenarios. Extensive experiments on three widely used benchmarks demonstrate that the proposed method achieves a state-of-the-art performance in terms of both qualitative and quantitative results, which shows its superiority in multimedia communication and storage systems.
TL;DR: A multilevel thresholding approach based on the LSHADE method for the segmentation of magnetic resonance brain imaging is presented and it is demonstrated that the suggested approach improves consistency and segmentation quality.
TL;DR: This article designs a three-step iterative algorithm to solve the sparse regularization-based FCM model, which is constructed by the Lagrangian multiplier method, hard-threshold operator, and normalization operator, respectively and indicates that the proposed algorithm has good ability for multiphase image segmentation, and performs better than other alternative FCM-related algorithms.
Abstract: The conventional fuzzy C -means (FCM) algorithm is not robust to noise and its rate of convergence is generally impacted by data distribution. Consequently, it is challenging to develop FCM-related algorithms that have good performance and require less computing time. In this article, we elaborate on a comprehensive FCM-related algorithm for image segmentation. To make FCM robust, we first utilize a morphological grayscale reconstruction (MGR) operation to filter observed images before clustering, which guarantees noise-immunity and image detail-preservation. Since real images can generally be approximated by sparse coefficients in a tight wavelet frame system, feature spaces of observed and filtered images can be obtained. Taking such features to be clustered, we investigate an improved FCM model in which a sparse regularization term is introduced into the objective function of FCM. We design a three-step iterative algorithm to solve the sparse regularization-based FCM model, which is constructed by the Lagrangian multiplier method, hard-threshold operator, and normalization operator, respectively. Such an algorithm can not only perform well for image segmentation, but also come with high computational efficiency. To further enhance the segmentation accuracy, we use MGR to filter the label set generated by clustering. Finally, a large number of supporting experiments and comparative studies with other FCM-related algorithms available in the literature are provided. The obtained results for synthetic, medical and color images indicate that the proposed algorithm has good ability for multiphase image segmentation, and performs better than other alternative FCM-related algorithms. Moreover, the proposed algorithm requires less time than most of the existing algorithms.
TL;DR: Experimental simulation and test results indicate that the devised multi-image encryption scheme can effectively encrypt multiple images, which increase the efficiency of image encryption and transmission, and have good security performance.
Abstract: A multi-image encryption scheme based on the fractional-order hyperchaotic system is designed in this paper. The chaotic characteristics of this system are analyzed by the phase diagram, Lyapunov exponent and bifurcation diagram. According to the analyses results, an interesting image encryption algorithm is proposed. Multiple grayscale images are fused into a color image using different channels. Then, the color image is scrambled and diffused in order to obtain a more secure cipher image. The pixel confusion operation and diffusion operation are assisted by fractional hyperchaotic system. Experimental simulation and test results indicate that the devised multi-image encryption scheme can effectively encrypt multiple images, which increase the efficiency of image encryption and transmission, and have good security performance.
TL;DR: It is established that this robust and secure data hiding scheme to transmit grayscale image in encryption-then-compression domain has a better ability to recover concealed mark than conventional ones at low cost.
Abstract: This paper introduces a robust and secure data hiding scheme to transmit grayscale image in encryption-then-compression domain. First, host image is transformed using lifting wavelet transform, Hessenberg decomposition and redundant singular value decomposition. Then, we use appropriate scaling factor to invisibly embed the singular value of watermark data into the lower frequency sub-band of the host image. We also use suitable encryption-then-compression scheme to improve the security of the image. Additionally, de-noising convolutional neural network is performed at extracted mark data to enhance the robustness of the scheme. Experimental results verify the effectiveness of our scheme, including embedding capacity, robustness, invisibility, and security. Further, it is established that our scheme has a better ability to recover concealed mark than conventional ones at low cost.
TL;DR: In this article, the Sine Cosine Algorithm (SCA) is used for detecting the ROI in the attacked image and the computed histogram of this ROI is binarized with a secret method, and the resulting sequence of binary values is compared to the original one generated from the original ROI.
Abstract: Zero-watermarking methods are widely used for efficient copyright protection of digital images. These methods have the ability to withstand both common image processing attacks and some geometric attacks. However, they cannot effectively resist the complex image attacks such as translation, cropping, combined geometric attacks, UnZign, etc. For this purpose, we propose in this work a novel robust zero-watermarking method that can effectively resist several complex image attacks. The proposed method involves the use of the histogram descriptor to generate a secret sequence of binary values from a user-selected Region of Interest (ROI). In order to check the intellectual property rights, the Sine Cosine Algorithm (SCA) is used for detecting the ROI in the attacked image. Then, the computed histogram of this ROI is binarized with a secret method. Next, the resulting sequence of binary values is compared to the original one generated from the original ROI. If there is a high similarity between these binary sequences, the grayscale image copyrights are validated. The simulation results show that the proposed method is not only resistant to geometric attacks (rotation, scaling) and to common image attacks (JPEG compression, filtering, etc.), but it is also robust against the most complex image attacks (cropping, translation, combined geometric attacks, etc.). Furthermore, the results of the comparisons carried out in terms of robustness against different types of attacks prove the superiority of our method over other similar recent zero-watermarking methods.