Image Sub-blocks Research Articles

An Image Encryption Algorithm Based on Tabu Search and Hyperchaos

In this paper, we propose a digital image encryption scheme based on Tabu Search (TS) algorithm and Chen’s hyperchaos system. First, in order to enhance the security of the algorithm and resist the known-plaintext attack, the key is associated with the ordinary image, and the hash value generated by the ordinary image is used as the initial value of the hyperchaotic system. Moreover, the TS algorithm is used to obtain the optimal subsequence to scramble the sub-block image, which ensures the scrambling effect of the algorithm. In addition, for the sake of minimizing the correlation of neighborhood pixels and strengthening the effect of scrambling, the ordinary image is divided into blocks for scrambling and diffusion. Through simulation and experiments, the key sensitivity, differential attack and pure ciphertext attack are analyzed. Compared with the other encryption schemes, the results verify the effectiveness and reliability of the proposed scheme.

RESEARCH ON RETINEX ENHANCEMENT METHOD FOR VASCULAR MEDICAL IMAGES

To improve the feature extraction performance in vascular medical images, a multi-scale Retinex medical image enhancement algorithm based on a four-step method is proposed. This method is based on the multi-scale Retinex enhancement method, which divides the entire enhancement process into four steps: The first step is to reduce the overall complexity of the enhancement process through block processing; second, perform Retinex enhancement in each sub-block image; the third step is to optimize the connection area of each sub-block; and the fourth step is to highlight detailed features through gradient filtering. During the experiment, three different images were enhanced. The experimental results show that the proposed method can be used in enhancing the vascular features in medical images and be extended to other application fields. This method can effectively suppress the blurring and halo of the original image, while achieving enhancement effects and better protecting detailed features. From the four sets of indicators, the proposed method has shown more significant improvement than both reference methods. Regarding information entropy, the doubles have increased by a maximum of 96.2% and a minimum of 30.3%.

Adaptive block imaging based on compressive sensing in AFM.

Atomic force microscopy (AFM) is a kind of high-precision instrument to measure the surface morphology of various conductive or nonconductive samples. However, obtaining a high-resolution image with standard AFM scanning requires more time. Using block compressive sensing (BCS) is an effective approach to achieve rapid AFM imaging. But, the routine BCS-AFM imaging is difficult to balance the image quality of each local area. It is easy to lead to excessive sampling in some flat areas, resulting in time-consuming. At the same time, there is a lack of sampling in some areas with significant details, resulting in poor imaging quality. Thus, an innovative adaptive BCS-AFM imaging method is proposed. The overlapped block is used to eliminate blocking artifacts. Characteristic parameters (GTV, Lu, and SD) are used to predict the local morphological characteristics of the samples. Back propagation neural network is employed to acquire the appropriate sampling rate of each sub-block. Sampling points are obtained by pre-scanning and adaptive supplementary scanning. Afterward, all sub-block images are reconstructed using the TVAL3 algorithm. Each sample is capable of achieving uniform, excellent image quality. Image visual effects and evaluation indicators (PSNR and SSIM) are employed for the purpose of evaluating and analyzing the imaging effects of samples. Compared with two nonadaptive and two other adaptive imaging schemes, our proposed scheme has the characteristics of a high degree of automation, uniformly high-quality imaging, and rapid imaging speed. HIGHLIGHTS: The proposed adaptive BCS method can address the issues of uneven image quality and slow imaging speed in AFM. The appropriate sampling rate of each sub-block of the sample can be obtained by BP neural network. The introduction of GTV, Lu, and SD can effectively reveal the morphological features of AFM images. Seven samples with different morphology are used to test the performance of the proposed adaptive algorithm. Practical experiments are carried out with two samples to verify the feasibility of the proposed adaptive algorithm.

Color image encryption based on discrete memristor logistic map and DNA encoding

This paper proposes a color image encryption scheme with new features based on two-dimensional discrete memristor logistic map (2D-MLM), Deoxyribonucleic acid (DNA) encoding and multi-wing hyperchaotic systems. Firstly, the discrete memristor map and four-wing hyperchaotic system are used to generate various pseudo-random sequences. Secondly, the original image is processed in blocks, and DNA encoding and operation are performed on the sub-block image using multiple pseudo-random sequences. Finally, combining the pseudo-random sequence to diffuse the sub-block image and DNA decoding, we can get the encrypted image. The scheme generates a one-time key for each image, and uses multiple different sub-keys to increase the key space. The use of diverse pseudo-random sequences for image scrambling effectively avoids the security risks associated with using a single chaotic sequence. The key space of this algorithm reaches 2425, the information entropy of the ciphertext image is 7.9993, the number of pixels change rate (NPCR) and the uniformed average change intensity (UACI) reach 99.6095 % and 33.5089 %, and the inter-pixel correlation is close to zero. The simulation results prove that the proposed algorithm has good security against many potential attacks.

Using Deep Learning-Based Defect Detection and 3D Quantitative Assessment for Steel Deck Pavement Maintenance

Efficient distress detection and identification analysis are of great significances for long-span steel bridge deck pavement maintenance. This paper proposes a pavement distress recognition method based on road quality detection equipment and deep learning. Firstly, the sub-block image is used as the processing unit, and the three-dimensional image is divided into fracture surface element and background surface element. A pavement crack recognition network (PDRNet) based on convolutional neural network is proposed, which is used for automatic recognition of pavement background element and pavement crack element. Then, considering the pixel-level neighborhood characteristics of cracks in 3D pavement images, the PDRNet model is used to detect and quantitatively evaluate pavement cracks, which combines the crack elevation detection method to extract the complete contour of cracks in the crack surface element. Finally, the distress database containing three-dimensional markers is integrated into the pavement maintenance management platform. Results show that the PDRNet proposed in this paper has high prediction accuracy on the verification set and shows good robustness to various distress, and the average accuracy is above 88%. The established spatial-temporal integration platform overcomes the communication bottleneck between different types of data, and is important to improve the maintenance management level and predict the long-term development of distress.

Influence of On-Site Camera Calibration with Sub-Block of Images on the Accuracy of Spatial Data Obtained by PPK-Based UAS Photogrammetry

Unmanned Aerial Systems (UAS) Photogrammetry has become widely used for spatial data acquisition. Nowadays, RTK (Real Time Kinematic) and PPK (Post Processed Kinematic) are the main correction methods for accurate positioning used for direct measurements of camera station coordinates in UAS imagery. Thus, 3D camera coordinates are commonly used as additional observations in Bundle Block Adjustment to perform Global Navigation Satellite System-Assisted Aerial Triangulation (GNSS-AAT). This process requires accurate Interior Orientation Parameters to ensure the quality of photogrammetric intersection. Therefore, this study investigates the influence of on-site camera calibration with a sub-block of images on the accuracy of spatial data obtained by PPK-based UAS Photogrammetry. For this purpose, experiments of on-the-job camera self-calibration in the Metashape software with the SfM approach were performed. Afterward, experiments of GNSS-Assisted Aerial Triangulation with on-site calibration in the Erdas Imagine software were performed. The outcomes show that only the experiment of GNSS-AAT with three Ground Control Points yielded horizontal and vertical accuracies close to nominal precisions of the camera station positions by GNSS-PPK measurements adopted in this study, showing horizontal RMSE (Root-Mean Square Error) of 0.222 m and vertical RMSE of 0.154 m. Furthermore, the on-site camera calibration with a sub-block of images significantly improved the vertical accuracy of the spatial information extraction.

AutoBCS: Block-Based Image Compressive Sensing With Data-Driven Acquisition and Noniterative Reconstruction.

Block compressive sensing (CS) is a well-known signal acquisition and reconstruction paradigm with widespread application prospects in science, engineering, and cybernetic systems. However, state-of-the-art block-based image CS (BCS) methods generally suffer from two issues. The sparsifying domain and the sensing matrices widely used for image acquisition are not data driven and, thus, both the features of the image and the relationships among subblock images are ignored. Moreover, it requires to address a high-dimensional optimization problem with extensive computational complexity for image reconstruction. In this article, we provide a deep learning (DL) strategy for BCS, called AutoBCS, which automatically takes the prior knowledge of images into account in the acquisition step and establishes a reconstruction model for performing fast image reconstruction. More precisely, we present a learning-based sensing matrix to accomplish image acquisition, thereby capturing and preserving more image characteristics than those captured by the existing methods. In addition, we build a noniterative reconstruction network, which provides an end-to-end BCS reconstruction framework to maximize image reconstruction efficiency. Furthermore, we investigate comprehensive comparison studies with both traditional BCS approaches and newly developed DL methods. Compared with these approaches, our proposed AutoBCS can not only provide superior performance in terms of image quality metrics (SSIM and PSNR) and visual perception but also automatically benefit reconstruction speed.

Interactive visualization of forearm internal rotation angle in Tai Chi training

Modeling the visual features of the forearm internal rotation angle during Tai Chi training is difficult, and its accurate detection is lacking. To address this issue, this paper proposes an interactive visualization analysis method based on Harris corner detection and edge contour feature extraction to visualize and adjust the internal rotation of the forearm during Tai Chi training in real time. In the proposed method, the visual images of limb movement and forearm rotation during the Tai Chi training are collected as features, and the wavelet image denoising method is used to denoise the images to improve the signal-to-noise ratio of the output. Meanwhile, the image pixel uniform traversal method is used to continuously traverse the image subblocks to extract the edge contour features during the forearm internal rotation interaction. By means of superpixel feature decomposition and grayscale information superposition, the fitting effect of the forearm internal rotation angle is fed back, and based on this, the forearm internal rotation angle is modified to realize the interactive visual analysis of the forearm internal rotation angle. Experimental results show that, compared with existing methods, the interactive visualization performance of the proposed method is better. The proposed method can fit, track, and solve the rotation angle in real time; the required analysis training images are more accurate, and the Tai Chi training data detection and dynamic analysis abilities are improved.

War Optimization Method for Image Encryption Algorithm Based on A Chaotic Bit-Plane Decomposition

In this paper, develop image encryption algorithm based on a chaotic bit-plane decomposition and optimization algorithm of a War Optimization Algorithm (WOA). Initially, utilization of SHA-256 hash algorithm for computing the plaintext images hash parameter as initial parameter of the fractional Lorenz hyperchaotic system after the process. Use the chaotic sequence for permuting plaintext image in a bit plane to achieve the scrambled images. After that, block the scrambles image into four sub images of similar size and count the hash parameter of every row of every block through the SHA-256 hash algorithm as the initial parameter of the Sine-Tent Logistic chaotic system. Utilize the achieved chaotic sequence to substitute the images. After that, the four sub-block images to get the last encrypted image and the population can be achieved. At last, utilization information entropy of ciphertext images as the fitness function of WOA. Choose the ciphertext image with the optimal information entropy of ciphertext images as the fitness function of the WOA. Select the ciphertext image with the best information entropy from the population as the optimal encrypted image, and then, return the position value of the best war source meanwhile. The proposed method is implemented in MATLAB and performance is analyzed with performance metrices. The proposed method is compared with the conventional techniques.

Detection of audio copy-move-forgery with novel feature matching on Mel spectrogram

Audio copy-move-forgery created by copying one or more segments of an audio file and pasting it in a different position within the same audio is one of the most widely used methods in the field of audio forensics. This type of forgery is easy to apply but difficult to detect in the case of post-processing operations applied to forged speech to hide traces of forgeries. This paper proposes a robust method for the detection and localization of the audio copy-move forgery using a keypoint-based approach to the Mel spectrogram representation of audio. In the proposed method, first, the Mel spectrogram image is created from the input audio. Then, SIFT keypoints are obtained from each RGB color channel of this image. The obtained keypoints from each channel are matched via feature vectors to reveal the clues of the forgery regions, and the image sub-blocks whose keypoints are determined to be the center are labeled as forged blocks. Then the blocks in the neighborhood of the forged blocks are investigated whether forged or not. The proposed post-processing stage completes the determination of the forged regions. This stage eliminates the possible false positives and marks the forged areas in the spectrogram image. The forged segments are marked in the audio file by utilizing the positions of the forged regions in the spectrogram image. Experimental studies are carried out on two pitch-based datasets, using TIMIT and Arabic Speech Corpus. The paper presents the detailed performance results of popular referenced studies on these datasets. The performance results prove that the proposed method is more robust against common post-processing operations such as noise addition, filtering operation, and especially compression operation.

Segmentation algorithm for overlap recognition of seedling lettuce and weeds based on SVM and image blocking

For the problem of a low recognition rate and shape feature failure caused by overlapping seedlings and weeds during the development of an intelligent lettuce weeding robot, a method to identify seedling lettuce and weeds based on an image block and support vector machine (SVM) is proposed, which realizes their precise identification and boundary segmentation. The a* channel is used to grayscale the collected image. The Otsu and morphological methods are selected to extract all the green targets in the image. The connected component analysis method is applied to label the green targets with regions of interest (ROIs), and those with pixel areas larger than the area threshold are normalized to 256 × 256 pixels. The image blocking technique is introduced to separately aliquot the normalized ROI, with block sizes of 16 × 16, 32 × 32, and 64 × 64 pixels. On this basis, the image sub-blocks are manually labeled, block by block, to extract three texture features: histogram of oriented gradient (HOG), local binary pattern (LBP), and gray-level co-occurrence matrix (GLCM). With the accuracy of fivefold cross-validation as the optimization objective, a genetic algorithm (GA) is used to optimize the SVM penalty and kernel parameters of 21 groups of research objects (one block size has three texture features, which are arbitrarily combined to form seven research objects, with a total of three block sizes). We compare the recognition performance of the SVM, RF, KNN, and GA-SVM classifiers in a single feature and a combination of fusion strategies through comparative analysis. When the block size is 32 × 32 pixels, the fusion of LBP and GLCM features under the GA-SVM classifier has the highest accuracy, and the optimal SVM model for the identification of lettuce and weeds in the seedling stage is obtained. For the misidentified image sub-blocks in optimization model recognition, an image block reconstruction method based on the comparison of the center point and eight-neighbor label value is proposed, and this is combined with the proportion of image blocks of two labels for comprehensive judgment. The center point label value is reconstructed to the improve recognition accuracy. Experimental results show that the average precision, recall, and F1 score of the proposed method are 0.9473, 0.9529, and 0.9498, respectively, and those of images without overlapping leaves can all reach 1, thus providing a theoretical basis for crop recognition and segmentation.

New Unequal Error Protection Strategy for Image Transmission Based on Bilayer-Lengthened PLDPC Code in Half-Duplex Relay System

To reduce the waste of energy in communications, unequal error protection (UEP) is used to provide asymmetric protection for messages with different levels of importance. This paper proposes new efficient strategies of UEP based on bilayer protograph-based low-density parity check (PLDPC) codes in decoding-and-forward (DF) relay systems. In particular, we jointly utilize source coding and channel coding to design UEP strategies and then save transmission energy. According to the different levels of importance of discrete cosine transform (DCT) coefficients of image and variance statistical characteristics of image sub-blocks, bilayer-lengthened PLDPC codes are exploited to protect the transmitted image information with different importance levels at the half-duplex relay system. In the end, the simulation result shows that the proposed UEP schemes achieve excellent performance gains compared to conventional equal error protection (EEP) scheme. Additionally, the complexity analysis of the UEP strategies is given.

Image Encryption Algorithm Based on Artificial Bee Colony Algorithm and Chaotic System

This article proposes an image encryption algorithm based on a chaotic bit-plane decomposition and optimization algorithm of a crossover operator artificial bee colony algorithm. Firstly, use the SHA-256 hash algorithm to calculate the plaintext image’s hash value as the starting value of the fractional Lorenz hyperchaotic system after operation. Utilize the chaotic sequence to permutate plaintext image in a bit plane to obtain the scrambled image. Secondly, block the scrambled image into four subimages of equal size, and count the hash value of each row of each block by the SHA-256 hash algorithm as the starting value of the Sine-Tent-Logistic chaotic system. Use the obtained chaotic sequence to substitute the images. Then, stitch the four sub-block images to get the final encrypted image, and the population is obtained. Finally, use the information entropy of ciphertext image as the fitness function of the artificial bee colony algorithm based on a crossover operator. Select the ciphertext image with the best information entropy from the population as the optimal encrypted image, and then, return the position value of the best honey source meanwhile. The experimental simulation and security analysis indicate that the scheme has an excellent encryption effect and ability to oppose various general attacks.

Preserving privacy while revealing thumbnail for content-based encrypted image retrieval in the cloud

Owing to the rapid development of cloud services and personal privacy requirements, content-based encrypted image retrieval in the cloud has been increasing. Outsourced images are encrypted into noiselike ones to protect privacy, however, the obtained unrecognized appearance limits their availability. Besides, users have to decrypt all search results to browse, while some of them may not be needed, which undoubtedly wastes bandwidth and computing resources. To cope with this problem, a compromise strategy is proposed that considers the tradeoff between privacy and usability of cipher images. Wherein, a thumbnail preserving encryption (TPE) based on genetic algorithm is proposed. The pixels in the sub-blocks of the plain image are scrambled and diffused at the bit-level through crossover and mutation operators of the genetic algorithm. Moreover, two new operators of Mutation Compensation and Mutation Failure are defined and incorporated into the traditional genetic algorithm to achieve an ideal TPE, that cipher image has the same thumbnail as the original image. Additionally, a color histogram-based retrieval algorithm is introduced to retrieve cipher images using the color information preserved by thumbnails; and to improve retrieval accuracy by using the Bhattacharyya distance. A series of simulations verify the security and effectiveness of our scheme.

TransCS: A Transformer-Based Hybrid Architecture for Image Compressed Sensing.

Well-known compressed sensing (CS) is widely used in image acquisition and reconstruction. However, accurately reconstructing images from measurements at low sampling rates remains a considerable challenge. In this paper, we propose a novel Transformer-based hybrid architecture (dubbed TransCS) to achieve high-quality image CS. In the sampling module, TransCS adopts a trainable sensing matrix strategy that gains better image reconstruction by learning the structural information from the training images. In the reconstruction module, inspired by the powerful long-distance dependence modelling capacity of the Transformer, a customized iterative shrinkage-thresholding algorithm (ISTA)-based Transformer backbone that iteratively works with gradient descent and soft threshold operation is designed to model the global dependency among image subblocks. Moreover, the auxiliary convolutional neural network (CNN) is introduced to capture the local features of images. Therefore, the proposed hybrid architecture that integrates the customized ISTA-based Transformer backbone with CNN can gain high-performance reconstruction for image compressed sensing. The experimental results demonstrate that our proposed TransCS obtains superior reconstruction quality and noise robustness on several public benchmark datasets compared with other state-of-the-art methods. Our code is available on TransCS.

An Improved Time-Domain Autofocus Method Based on 3-D Motion Errors Estimation

Spatial-variant phase errors (PEs) are important factors which defocus the synthetic aperture radar (SAR) image. In time-domain SAR imaging, the exact calculation of instantaneous range is carried out to realize imaging. Accurate trajectory is the key to compensate spatial-variant PEs and ensure image focus. Thus, an improved time-domain autofocus method based on three-dimensional motion errors (3-D MEs) estimation is proposed in this article. First, an improved maximizing-maximum-pixel-value method is used to estimate nonspatial-variant PEs. Meanwhile, a theoretical explanation combined with <inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula>-dimensional Euclidean space is described. Then, residual PEs and wrapped PEs are discussed successively. A part-overlapped partitioning scheme for sub-block images (SBIs) and a wrapped-PE model are proposed for 3-D MEs estimation. Then, the estimation problem is turned into a mixed integer programming problem, which can be solved by the combination of genetic algorithm (GA) and Tikhonov regularization. Finally, the well-focused image is obtained through updated trajectory. The effectiveness of the proposed method is proven by results of simulation and real SAR data processing.

Spatial-temporal joint zinc flotation froth image denoising based on bubble dynamic features

The environment of the flotation plant is harsh, and the noise pollution of the collected froth images is severe. It is challenging to obtain real-time and high-quality froth images by common denoising methods. A spatial-temporal joint denoising method based on bubble dynamic features is proposed. This method first analyses the dynamic features of the bubble and detects the stability of each reference image sub-blocks; then selects the corresponding filtering to denoising according to the different states of the froth sub-blocks; and finally obtains a weighted average result of temporal filtering and spatial filtering. This method uses a modified and noise-robust cross-correlation method to estimate the sub-blocks' motion, which improves the denoising performance of time-domain filtering. The experiments show that the method can obtain a high signal-to-noise ratio and strong structural similarity result for non-static background images. It has also been well verified in the application of zinc flotation froth images.

A Fast Image Compression Algorithm Based on Wavelet Transform

With multimedia becoming widely popular, the conflict between mass data and finite memory devices has been continuously intensified; so, it requires more convenient, efficient and high-quality transmission and storage technology and meanwhile, this is also the researchers’ pursuit for highly efficient compression technology and it is the fast image transmission that is what people really seek. This paper mainly further studies wavelet analysis and fractal compression coding, proposes a fast image compression coding method based on wavelet transform and fractal theory, and provides the theoretical basis and specific operational approaches for the algorithm. It makes use of the smoothness of wavelet, the high compression ratio of fractal compression coding and the high quality of reconstructed image. It firstly processes the image through wavelet transform. Then it introduces fractal features and classifies the image according to the features of image sub-blocks. Each class selects the proper features. In this way, for any sub-block, it only needs to search the best-matched block in a certain class according to the corresponding features. With this method, it can effectively narrow the search in order to speed up coding and build the relation of inequality between the sub-block and the matching mean square error. So, it can effectively combine wavelet transform with fractal theory and further improves the quality of reconstructed image. By comparing the simulation experiment, it objectively analyzes the performance of algorithm and proves that the proposed algorithm has higher efficiency.

Deep Learning for Face Recognition under Complex Illumination Conditions Based on Log-Gabor and LBP

The complex light conditions, and this is one of the most important and difficult problems in practical face recognition, in this paper, we propose a new deep learning-based method to solve the problem of the effect of the light of the changes in the facial recognition process. First, the primary treatment of the lighting can be used to improve the negative effects of intensive changes in the lighting of a photo of a face, and for a second, the Log-Gabor filters in order to get the images used in the Log-Gabor features at different scales and in different directions, and then, the LBP (Local Binary Pattern) features on the image subblock is obtained. Finally, the histogram of the texture of the features of the formation and the visual layer of the deep belief network (DBN) to drop, and then the classification and the recognition is done with a deep-learning-DBN. The experimental results show that superior performance can be obtained in the application of the strategy in comparison to some of the modern technology.

Research on Face Recognition Algorithm Based on Block CR

Partial absence of face information challenges the robustness of face recognition algorithms. In order to reduce the effect of partial information loss on face recognition, a Face Recognition Method based on partitioning Collaborative Representation (FRAPCR) is proposed in this paper. Firstly, the face image is divided into several sub-blocks. Secondly, the Collaborative Representation (CR) is used to calculate the minimum sparse representation coefficient of each sub-block and the residual between the sub-block and the corresponding samples of each class, taking the class corresponding to the minimum residual as the class to which the sub-block belongs. Thirdly, a voting mechanism is introduced to count the categories of all sub-blocks of each face image, and the category with the largest number of votes is the category to which the whole face image belongs. Through the experiments on face databases ORL, Extend Yale B, and AR by the proposed method (FRAPCR), the best partitioning way of the face image is obtained. When there is partial information missing (pixel information missing, corrosion block and occlusion) in the face image, the images in each face database is divided in its corresponding optimal partitioning way. And comparative experiments between the FRAPCR and traditional CR face recognition methods are performed. The results show that FRAPCR has high recognition rate and stable recognition effect when there is partial information missing in face images.