An efficient reversible data hiding with reduplicated exploiting modification direction using image interpolation and edge detection

This paper proposes a steganography method using the digital images. Here, we are embedding the data which is to be secured into the digital image. Human Visual System proved that the changes in the image edges are insensitive to human eyes. Therefore we are using edge detection method in steganography to increase data hiding capacity by embedding more data in these edge pixels. So, if we can increase number of edge pixels, we can increase the amount of data that can be hidden in the image. To increase the number of edge pixels, multiple edge detection is employed. Edge detection is carried out using more sophisticated operator like canny operator. To compensate for the resulting decrease in the PSNR because of increase in the amount of data hidden, Minimum Error Replacement [MER] method is used. Therefore, the main goal of image steganography i.e. security with highest embedding capacity and good visual qualities are achieved. To extract the data we need the original image and the embedding ratio. Extraction is done by taking multiple edges detecting the original image and the data is extracted corresponding to the embedding ratio.

Image steganography is a technique for obscuring secret message within image pixel matrix. It provides means for secretly communicating between two entities with the third party totally unaware of the embedded message. One of the most important methods for image steganography is edge detection technique, wherein secret data is inserted in LSBs of image edge pixels. Data embedding in edge pixels does not create notable distortion in image when compared to embedding data in non-edge pixels. Different types of edge pixel detectors are available including most renowned Canny edge detector, Sobel edge detector, Prewitt edge detector and many others. Hybrid edge detection uses a combination of different edge detectors for generating edge pixels of image. Message to be secretly communicated is inserted in the LSB of these edge pixels, and the image thus formed called stego image is sent to the concerned party. Major drawback of hybrid edge detection technique is its capacity. The number of edge pixels is very less in any image. In the last decade, many papers have focused on increasing capacity of edge detection by using different techniques. In this work, we have proposed an extremely high-capacity hybrid edge detection technique by using data compression. Also, data is encrypted and then inserted in the cover image pixels. For message integrity, SHA512 hash function is used. Subsequently, this work not only improves capacity of edge detection while maintaining the image quality but also provides security and message integrity.

Image interpolation is used widely in the computer vision. Holding edge information is main problem in the image interpolation. By using bilinear and bicubic B-spline interpolation methods, a novel image interpolation approach was proposed in this paper. Firstly, inverse distance weighted average method was used to reduce image’s noise. Secondly, edge detection operator was used to extract image's edges information. It can help us to select different interpolation methods in the image interpolation process. Finally, we selected bilinear interpolation approach at non-edge regions, and bicubic B-spline interpolation method was used near edges regions. Further more, control vertexes were computed from pixels with calculation formula which has been simplified in the B-spline interpolation process. Experiments showed the interpolated image by the proposed method had good vision results for it could hold image's edge information effectively.

Based on the shortcomings of the traditional edge detection algorithm easily lead to the brink of fracture and discontinuous, a new improved Pulse-coupled neural network (PCNN) is proposed to image segmentation and edge detection. Using Gaussian filter pre-processing, gray image is segmented into binary image. Based on this binary image, the edge detection is accomplished using improved PCNN, which can increase contrast of image and enhance the edge of image. Compared with correlative result using Canny operator and others in the existing references, simulation results show that our methods have both preferable results of gray image edge detection and fine applicability.

Since ancient times, there is a need to save data from third parties that spy on the data, leads to the practice of steganography to ensure data integrity and privacy. The advent in multimedia technologies and greater need for security has raised the popularity of image steganography. The traditional algorithm used for image steganography is the Least Significant Bit Method (LSB). A better strategy to overcome the LSB method is to hide data only in edge pixel of image. In this paper, first we apply the ant colony optimization (ACO) method to find edges of an image given by Xiaochen Liu et al. then will hide sensitive message in edges of image randomly to provide more security level. The edge detection technique and chaotic scheme-based steganography is introduced based on the ACO. This strategy efficiently finds the edges and does not attract much attraction compared to other traditional methods. Our result indicates the higher value of PSNR that shows the performance of the proposed algorithm.

Research on improved edge extraction algorithm of rectangular piece

During space reconnaissance applications, edge detection from remote sensing imagery plays an important role in the target recognition processing. However, traditional edge detection methods usually only utilize the high-frequency information in one image. Since low-frequency elements may be aliasing with high-frequency parts, the edges extracted may be unconnected under complex topography, different objects and imaging conditions. This paper proposes a novel image edge detection method based on Non-Subsampled Contourlet Transform (NSCT) to keep the object boundary continuously. It transforms the image into Contourlet domain in both high-frequency and low-frequency sub-bands respectively. Depending on the feature of flexible directivity reservation of an image during NSCT, the further edge extraction consists of 3 steps: firstly, the elements of the high-frequency coefficient matrix in Contourlet domain are filtered with high values left using adaptive thresholds. Then the low-frequency edge information is extracted via Canny operator from the low-frequency sub-band information. Finally, to achieve a more consistent edge image, the low-frequency edge image is achieved according to the low-frequency matrix and adopted to compensate the high-frequency image with the isolated noise points eliminated as well. The numerical simulation and practical test results show the higher effectiveness and robustness of the proposed algorithm when comparing with the classical edge detectors, such as Sobel operator, Canny operator, Log operator and Prewitt operator, etc.

In this paper ,a new method for edge detection in multispectral imags is presented. It is based on the use of the Self-Organizing Map (SOM), Peano scan and a conventional edge detector. The method presented in this paper order the vectors of the original image in such a way that vectors that are near each other according to some similarity criterium have scalar ordering values near each other. This is achieved using a 2D self-organizing map and the Peano scan. After ordering, the original vector image reduces to a gray-value image, and a conventional edge detector can be applied. In this paper, the Laplace and the Canny edge detectors are used. It is shown, that using the proposed method sit is possible to find the same relevant edges that R-ordering based methods find. Furthermore, it is also possible to find edges in images which consist of metameric colors, i.e. images in which every pixel vector maps into the same location in RGB space. This is not possible using conventional edge detectors which use an RGB image as input. Finally, the new method is tested with a real-world airplane image, giving results comparable with R-ordering based methods.© (1999) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

According to the theory of mathematical morphological, edge detection operators and characteristics of wood decay image, a multi-structure element multi-scale edge detection algorithm based on mathematical morphology is proposed. The image is filtered by an alternative-order morphological corrosion-expansion filter with a multi-scale element, and the edge information is obtained using multi-structure elements open-close operation. Canny operator is used to extract the edge of the decay image. Experimental results show that the proposed algorithm is insensitive to noise and is efficient in edge detection.

In this paper, we present the effect of binocular cues which plays crucial role for the visualization of a stereoscopic or 3D image. This study is useful in extracting depth and disparity information by image processing technique. A linear relation between the object distance and the image distance is presented to discuss the cause of cybersickness. In the experimental results, three dimensional view of the depth map between the 2D images is shown. A median filter is used to reduce the noises available in the disparity map image. After the median filter, two filter algorithms such as 'Gabor' filter and 'Canny' filter are tested for disparity visualization between two images. The 'Gabor' filter is to estimate the disparity by texture extraction and discrimination methods of the two images, and the 'Canny' filter is used to visualize the disparity by edge detection of the two color images obtained from stereoscopic cameras. The 'Canny' filter is better choice for estimating the disparity rather than the 'Gabor' filter because the 'Canny' filter is much more efficient than 'Gabor' filter in terms of detecting the edges. 'Canny' filter changes the color images directly into color edges without converting them into the grayscale. As a result, more clear edges of the stereo images as compared to the edge detection by 'Gabor' filter can be obtained. Since the main goal of the research is to estimate the horizontal disparity of all possible regions or edges of the images, thus the 'Canny' filter is proposed for decipherable visualization of the disparity.

SEDMI: Saliency based edge detection in multispectral images

The image edge is a region in the image that exhibits a clear discontinuity and change, which can serve as a reflection of the most fundamental attributes of the image. The research concentration in the field of image processing and computer vision is also on edge detection technology, which is employed to ascertain the contour details between various objects and regions of the image. This paper first examines the principles and methodologies of traditional edge detection algorithms, succinctly outlining the advantages and disadvantages of the Roberts, Prewitt, Sobel, and Canny operators. It then introduces representative algorithms for image edge detection based on fractional-order differentiation. Finally, it compares traditional edge detection algorithms with those based on fractional-order differentiation through experimental analysis, demonstrating that the latter exhibits superior performance in contour continuity and detail integrity. The application of fractional-order differential theory in image processing demonstrates significant potential.

As an indispensable measuring instrument, the pointer instrument has been widely used in the fields of industrial production. While the manual instrument identification has problems of higher labor intensity, higher rate of misjudgment and lower production, we take advantage of computer vision to deal with the images of pointer instrument dials. Two algorithms are proposed for image edge detection: structured random forests edge detection and canny edge detection, Hough transform is used to recognize the straight lines of the pointer, then the automation of instrument identification can be realized. Our results show that the accuracy of the dials printed by the two methods can meet the production requirements.

In order to surmount the infrared-image object differentiation difficulty caused by the blurred image edge, a kind of adaptive filter based infrared-image nonlinear edge enhancement technology was proposed in this paper. This technology integrates image nonlinear edge-sharpening and Multi-scale analyze method. The approach of Gauss pyramid structure can enhance detail information by using non-linear algorithms in different scales. The enhanced detail information is then added back to the original image iteratively. While saving the image edge information it can filter image noise and edge distortion caused by edge-sharpening and improve image’s clarity and SNR obviously. Gray scale grads was defined based on gray linear increment, image edge enhancement arithmetic can be real time realized, and has been applied in high performance thermal imager. As it is shown in experiments, this algorithm has practicality and potential application value in the field of infrared images contrast enhancement

The use of edge information for noise removal from digital images is of vital importance for preserving image details after filtering process. In this work, the edges in the speckled image are detected by employing a type-2 fuzzy edge detector and then the mean filter is used for removing speckle noise. Although the mean filter is effective for speckle noise removal, it also introduces undesirable blurrings, especially at the edges in the image. This can be a significant problem when the edge information is utilized for understanding and interpreting the image. The fuzzy edge detector can be highly efficient for detection of the edges correctly in the images with a minimum influence by the speckle noise. In the proposed filter, the mean filter window size is adjusted according to the edge information after detecting edges in the image. The window size in the vicinity of the edges is made smaller in comparison with the window size in the other areas to protect the edges. Experimental results demonstrate that the proposed method performs very well.