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This paper presents a new method for extracting the image features and lineaments related to local extrema of an image or a digital elevation model (DEM) such as ridges and valleys based on the continuous wavelet transform (CWT) of a set of variously illuminated hillshades. The method originates from the principle that a hillshade can exactly reflect the lineaments nearly perpendicular to the illumination direction of the hillshade, but not other ones. The method consists of four steps: (1) preparation of a set of differently illuminated hillshades of the input data, (2) detection of directional edges nearly perpendicular to the illumination direction from each hillshade based on the CWT, (3) a combination of multidirectional edges into an omnidirectional feature image, and (4) identification of lineaments through linkage and linearization of image feature lines. CWT coefficients of each hillshade are used to calculate the gradient and its direction of the hillshade. For each hillshade, directional edge pixels where the gradient direction is parallel to the illumination direction are selectively detected to form accurate and solitary image feature lines related to local extrema of the input data. Directional edges of each hillshade are easily classified into positive and negative edges using the hillshade gradient. As they have similar directions, they are easily linked to form longer line raster objects, which are converted into vector objects, that is, directional lineaments. The multidirectional edges and lineaments given from all the hillshades are combined to form an omnidirectional feature image and a group of omnidirectional lineaments. Its application to real DEMs shows the demonstrated advantages of the proposed method over other methods and the similarity between detected lineaments and fault lines in the study area. [ABSTRACT FROM AUTHOR]

Featured Application: The future applications of the present study are to secure patient information and diagnostic details within medical images, ensuring data integrity and authenticity. The method enhances the security of medical records during storage, transmission, and especially in telemedicine, where images are frequently shared over digital networks. The growing adoption of telemedicine necessitates robust security measures for medical images during transmission. This paper proposes a novel blind watermarking system for medical images that utilizes both image gradients and the Discrete Wavelet Transform (DWT). Image gradients, acting as spatial derivatives, provide a "topological map" of the image, aiding in the identification of areas susceptible to disruption. The DWT, with its multi-resolution analysis, offers a favorable balance between robustness and imperceptibility. The proposed method embeds the watermark within the low–low band (LL) of the DWT-decomposed image, specifically in 3 × 3 block regions selected based on gradient information. The mathematical relationships between the gradient's direction and magnitude are employed to extract the corresponding blocks and their codes adequately. These codes are then XORed with the watermark and embedded into the chosen blocks using the least significant bit (LSB) technique. Extensive experimentation on a medical image dataset evaluates the system's performance against some attacks like filtering, noise, and scaling. The results demonstrate the efficacy of the proposed approach in hiding information while ensuring the security and integrity of watermarked medical images. [ABSTRACT FROM AUTHOR]

The growing adoption of telemedicine necessitates robust security measures for medical images during transmission. This paper proposes a novel blind watermarking system for medical images that utilizes both image gradients and the Discrete Wavelet Transform (DWT). Image gradients, acting as spatial derivatives, provide a “topological map” of the image, aiding in the identification of areas susceptible to disruption. The DWT, with its multi-resolution analysis, offers a favorable balance between robustness and imperceptibility. The proposed method embeds the watermark within the low–low band (LL) of the DWT-decomposed image, specifically in 3 × 3 block regions selected based on gradient information. The mathematical relationships between the gradient’s direction and magnitude are employed to extract the corresponding blocks and their codes adequately. These codes are then XORed with the watermark and embedded into the chosen blocks using the least significant bit (LSB) technique. Extensive experimentation on a medical image dataset evaluates the system’s performance against some attacks like filtering, noise, and scaling. The results demonstrate the efficacy of the proposed approach in hiding information while ensuring the security and integrity of watermarked medical images.

With the widespread application of deep learning, many artificially generated fake images and videos appear on the Internet. However, it is difficult for people to distinguish the real from the fake ones, making the research on detecting and recognizing fake images or videos receive significant attention. Since new forgery techniques can reduce the effectiveness of specific detection methods or even make them ineffective, research on detecting facial depth forgery needs to be continuously developed. To defend against the onslaught of new facial depth forgery methods, we proposed an image gradient-based approach to transform the facial depth forgery detection problem into the recognition and analysis of video frames. Specifically, there are two key components in this approach: (1) we capture images from videos and crop the face section, which dramatically reduces the amount of data; (2) we use the image gradient operator to process the face image that extracts image features for detection and recognition. After these, we have conducted extensive experiments on different facial depth forgery datasets. Experimental results demonstrated that using our image gradient approach could effectively detect facial depth forgery and achieve excellent detection and identification performance. [ABSTRACT FROM AUTHOR]

In order to improve the recognition rate and operational efficiency of the system, a method in which the image features compensation coefficients are optimized by using an improved simulated annealing algorithm is proposed. Firstly, eight computational factors with low computational complexity are given, which can be used to compensate image features. Secondly, the design flow of face recognition algorithm is presented. Thirdly, an improved simulated annealing algorithm is designed to solve the optimal combination of feature compensation coefficients in the face recognition system. Fourthly, the results of the feature compensation coefficients recommended by the improved simulated annealing algorithm are applied to the Efficient Face Recognition Algorithm (EFRA) in this paper, and verified on the simulation platform. Experiments show that the recognition rate can reach 100% when the training images are 6 in ORL. The proposed algorithm also performs well in MU_PIE dataset. [ABSTRACT FROM AUTHOR]

Flow measurement in rivers and channels is crucial for water resource management and infrastructure planning, especially under the context of climate change. However, traditional methods like mechanical current meters and hydroacoustic instruments face limitations in terms of cost, intrusiveness, and accessibility. In recent years, image-based velocimetry techniques have emerged as promising alternatives due to their non-contact nature and cost-effectiveness. Nevertheless, persistent challenges remain, particularly concerning the uniform distribution of surface tracers necessary for precise measurements. These challenges are particularly pronounced in cases involving artificial seeding, where ensuring uniform distribution poses a significant obstacle. To address this issue, this study presents a novel methodology for filtering Large Scale Particle Image Velocimetry (LSPIV) data based on indicators of pixel intensity gradients. The methodology was evaluated across various field measurements under low flow conditions, encompassing a wide range of seeding characteristics. The evaluations demonstrated improvements in mean surface velocity profile estimation, showing reductions of up to 70 % in normalized root mean square error compared to not using filters. Additionally, the results were compared with filters typically employed by experienced LSPIV users, such as background subtraction and cross-correlation coefficient thresholds, showing improvements with the proposed filter. Implementation of the proposed strategy reduces the subjectivity in LSPIV implementation, particularly for users with limited knowledge of the technique, but also require minimal post-processing efforts. The methodology is anticipated to be integrated into existing software tools, thereby enhancing the accessibility of LSPIV for individuals with limited expertise in image velocimetry. Overall, this methodology facilitates cost-effective expansion of hydrological information availability, particularly in resource-constrained regions. • New filtering method for image velocimetry in rivers handles heterogeneous seeding. • The method enhances surface velocity estimations for more accurate flow measurement. • The methodology was validated in a wide range of environmental and seeding conditions. • This approach minimizes the need for subjective choices during data analysis. [ABSTRACT FROM AUTHOR]

Adaptive video monitoring settings have been extensively deployed in recent years. Smart video monitoring technology enables the acquisition and analysis of movies from various devices, as well as automatic analysis based on knowledge gathering. However, the storage capacity is restricted, and the important frames from the movie cannot be saved. Though, if the movie employs face as keyframes, it creates space and time complexity. To address this issue, the Viola-Jones Algorithm was used to detect faces from extracted keyframes in Video Indexing through Human Face Images using LGFA and the sliding window technique. The image gradient for brightness is created by integrating the sliding windowing method with LGFA, and scanning the input image horizontally takes up 70% of the facial image. As a result, Barcode as an index using the sequence table of the EAN 8 approach converts a video's human face into an EAN-8 linear video indexing barcode and thereby reducing bandwidth, storage space, and time complexity. Regular TV series video datasets, datasets of YouTube faces, and data sets of Hollywood clips were used to evaluate the proposed technique, and shown to be effective for indexing videos based on human faces. [ABSTRACT FROM AUTHOR]

In the field of reversible data hiding in encrypted images (RDH-EI), predict an image effectively and embed a message into the image with lower distortion are two crucial aspects. However, due to the linear regression prediction being sensitive to outliers, it is a challenge to improve the accuracy of predictions. To address this problem, this paper proposes an RDH-EI scheme based on adaptive prediction-error label map. In the prediction stage, an adaptive threshold estimation algorithm based on local complexity is proposed. Then, the pixels selection method based on gradient of image is designed to train the parameters of the prediction model. In the embedding stage, reserve enough space to embed auxiliary information and secret data embedding by flipping the least significant bits (LSBs) to encrypt the original image. In the receiver, based on the prediction- error map, error-free image recovery can be achieved. Extensive experimental results have shown that the proposed method can achieve effective pixel prediction results and get a higher embedding rate (ER). [ABSTRACT FROM AUTHOR]

This paper proposes an image interpolation method with regional gradient estimation (GEI) to solve the problem of the nonlinear interpolation method not sufficiently considering non-edge pixels. First, the approach presented in this paper expanded on the edge diffusion idea used in CGI and proposed a regional gradient estimation strategy to improve the problem of gradient calculation in the CGI method. Next, the gradient value was used to determine whether a pixel was an edge pixel. Then, a 1D directional filter was employed to process edge pixels while interpolating non-edge pixels using a 2D directionless filter. Finally, we experimented with various representative interpolation methods for grayscale and color images, including the one presented in this paper, and compared them in terms of subjective results, objective criteria, and computational complexity. The experimental results showed that GEI performed better than the other methods in an experiment concerning the visual effect, objective criteria, and computational complexity. [ABSTRACT FROM AUTHOR]

To improve the efficiency of traditional face recognition techniques, this paper proposes a novel face recognition algorithm called Image Gradient Feature Compensation (IGFC). Based on the gradients along four directions in an image, a fusion algorithm and a compensation method are implemented to obtain features of the original image. In this study, gradient magnitude maps of a face image are calculated along four directions. Fusion gradients and differential fusion gradients are produced by fusing the four gradient magnitude maps of a face image in multiple ways, and they are used as compensation variables to compensate the appropriate coefficients on the original image and build IGFC feature maps of the original face image. Subsequently, IGFC feature maps are divided into several blocks to calculate the concatenated histogram over all blocks, which is in turn utilized as the feature descriptor for face recognition. Principal component analysis (PCA) is used to cut down the number of dimensions in high-dimensional features, which are recognized by the Support Vector Machine (SVM) classifier. Finally, the proposed IGFC method is superior to traditional methods as suggested by verification studies on YALE, ORL, CMU_PIE, and FERET face databases. When the LibSVM parameter was set to '-s 0 -t 2 -c 16 -g 0.0009765625', the algorithm achieved 100% recognition on Yale and ORL data sets, 92.16% on CMU_PIE data sets, and 74.3% on FERET data sets. The average time for simultaneous completion of the data sets examined was 1.93 s, and the algorithm demonstrated a 70.71% higher running efficiency than the CLBP algorithm. Therefore, the proposed algorithm is highly efficient in feature recognition with excellent computational efficiency. [ABSTRACT FROM AUTHOR]

Aiming at the contradiction of color distortion and image detail enhancement when Retinex algorithm is applied to underwater image enhancement, the adaptive multi-scale Retinex underwater image enhancement algorithm combined with detail information is proposed in this paper. The selection requirements of the scale of convolution function in the enhancement of Retinex algorithm for underwater images containing different detail information are analyzed. Image gradient is used as the adjustment factor to adaptively adjust the weight of multi-scale Retinex operator, which is used to meet the requirements of underwater image with different detail information for contrast enhancement, and effectively alleviate the contradiction between color distortion and detail contrast enhancement in underwater image enhancement. Several groups of experiments show that the algorithm is superior to the traditional multi-scale (MSR) and multi-scale Retinex with color restoration (MSRCR) in removing the blue-green background of underwater image, avoiding color distortion, eliminating non-uniform illumination and image detail enhancement. [ABSTRACT FROM AUTHOR]

In the modern age, watermarking techniques are mandatory to secure digital communication over the internet. For an optimal technique, a high signal-to-noise ratio and normalized correctional is required. In this paper, a digital watermarking technique is proposed on the basis of the least significant bit through an image gradient and chaotic map. The image is segmented into noncorrelated blocks, and the gradient of each block is calculated. The gradient of the image expresses the rapid changes in an image. A chaotic substitution box (S-Box) is used to scramble the watermark according to a piecewise linear chaotic map (PWLCM). PWLCM has a positive Lyapunov exponent and better balance property as compared to other chaotic maps. This S-Box technique is capable of producing a disperse sequence with high nonlinearity in the generated sequence. Least significant bit is a simple technique for embedding but it has a high payload capacity and direct pixel manipulation. The embedding payload introduces a tradeoff between robustness and imperceptibility; hence, the image gradient is a technique to identify the best-suited place to embed a watermark and avoid image degradation. By modifying the least significant bits of the original image, the watermark signal is embedded according to the image gradient. In the image gradient, the direction and magnitude decide how much embeding can be done. In comparison with other methods, the experimental results show satisfactory progress in robustness against several image processing and geometrical attacks while maintaining the imperceptibility of the watermark signal. [ABSTRACT FROM AUTHOR]

• Our method detects and removes specular highlight effectively. • Gaussian mixture model clustering is more accurate than k-means clustering. • Confidence strategy makes the calculated unsaturated pixels more realistic. • New exemplar patch matching criterion finds the best exemplar patch quickly. In this paper, a new method based on dichromatic reflection model (DRM) and exemplar patch is proposed to remove highlight pixels for light field images. Firstly, a Gaussian mixture model clustering method with strong generalization performance combined with depth information is used to classify saturated highlight pixels and unsaturated highlight pixels. A confidence strategy based on DRM is proposed to remove unsaturated highlight pixels, and an exemplar patch matching method based on gradient combined with the sum of square of color difference is designed to remove saturated highlight pixels. Meanwhile, a method named SSIME based on information entropy with structural similarity index measure is designed to quantitatively evaluate the effectiveness of proposed method. Experiments show that our proposed method not only effectively detects and removes specular highlights, but also applies to objects with a large range of highlights, which breakthroughs the limitation that large-region specular highlight cannot be removed due to the short baseline of the light field camera. [ABSTRACT FROM AUTHOR]

This paper proposes an image interpolation method with regional gradient estimation (GEI) to solve the problem of the nonlinear interpolation method not sufficiently considering non-edge pixels. First, the approach presented in this paper expanded on the edge diffusion idea used in CGI and proposed a regional gradient estimation strategy to improve the problem of gradient calculation in the CGI method. Next, the gradient value was used to determine whether a pixel was an edge pixel. Then, a 1D directional filter was employed to process edge pixels while interpolating non-edge pixels using a 2D directionless filter. Finally, we experimented with various representative interpolation methods for grayscale and color images, including the one presented in this paper, and compared them in terms of subjective results, objective criteria, and computational complexity. The experimental results showed that GEI performed better than the other methods in an experiment concerning the visual effect, objective criteria, and computational complexity.

In the modern age, watermarking techniques are mandatory to secure digital communication over the internet. For an optimal technique, a high signal-to-noise ratio and normalized correctional is required. In this paper, a digital watermarking technique is proposed on the basis of the least significant bit through an image gradient and chaotic map. The image is segmented into noncorrelated blocks, and the gradient of each block is calculated. The gradient of the image expresses the rapid changes in an image. A chaotic substitution box (S-Box) is used to scramble the watermark according to a piecewise linear chaotic map (PWLCM). PWLCM has a positive Lyapunov exponent and better balance property as compared to other chaotic maps. This S-Box technique is capable of producing a disperse sequence with high nonlinearity in the generated sequence. Least significant bit is a simple technique for embedding but it has a high payload capacity and direct pixel manipulation. The embedding payload introduces a tradeoff between robustness and imperceptibility; hence, the image gradient is a technique to identify the best-suited place to embed a watermark and avoid image degradation. By modifying the least significant bits of the original image, the watermark signal is embedded according to the image gradient. In the image gradient, the direction and magnitude decide how much embeding can be done. In comparison with other methods, the experimental results show satisfactory progress in robustness against several image processing and geometrical attacks while maintaining the imperceptibility of the watermark signal.

• Proposal of a multi-classification approach for HFOs in intracranial EEG signals. • Coupled time-frequency and image-related features towards efficient HFOs multi-classification. • High performance on simulated and real iEEG signals. High Frequency Oscillations (HFOs) occurring in the range of [30–500 Hz] in epileptic intracranial ElectroEncephaloGraphic (iEEG) signals have recently proven to be good biomarkers for localizing the epileptogenic zone. Identifying these particular cerebral events and their discrimination from other transient events like interictal epileptic spikes is traditionally performed by experts through a visual inspection. However, this is laborious, very time-consuming and subjective. In this paper, a new classification approach of HFOs is proposed. This approach mainly relies on the combination of raw time frequency (TF) features, computed from a TF representation of HFOs using S-transform, with relevant image-based ones derived from a binarization of the corresponding TF grayscale image. The obtained feature vector is then used to learn a multi-class Radial Basis Function (RBF) based Support Vector Machine (SVM) classifier. The efficiency of the proposed approach, compared to conventional classification schemes based only on time, frequency or energy-based features, is confirmed, using both simulated and real iEEG signals. The proposed classification system has achieved, using simulated data and a signal to noise ratio (SNR) of 15 dB, a sensitivity, specificity, accuracy, area under the curve and F1-score around 0.990, 0.996, 0.995, 0.993 and 0.990 respectively. Besides, for real data, our proposed approach has attained the scores of 0.765, 0.941, 0.906, 0.929 and 0.768 for sensitivity, specificity, accuracy, area under the curve and F1-score respectively. These results confirm the relevance of coupling TF and image-related features, in the way proposed in this paper, for higher HFOs classification quality compared to already existing approaches. [ABSTRACT FROM AUTHOR]