scholarly journals Payload and quality augmentation using steganographic optimization technique based on edge detection

2021 ◽  
Vol 23 (1) ◽  
pp. 529
Author(s):  
Mafaz Alanezi ◽  
Iman Subhi Mohammed Altaay ◽  
Saja Younis Hamid Malla'aloo
Keyword(s):  
Edge Detection ◽  
Signal To Noise Ratio ◽  
Optimization Technique ◽  
Human Perception ◽  
Maximum Load ◽  
Cover Image ◽  
Least Significant Bit ◽  
Edge Pixel ◽  
Edge Detectors ◽  
Edge Detection Method

Information security is one of the most significant processes that must be taken into account when confidentially transferring information. This paper introduces a steganography technique using the edge detection method. It focused on three basic and important aspects’ payload, quality and security. Well-known edge detectors were used to generate as many edge pixels as possible to hide data and achieve the highest payload. The least significant bit (LSB) algorithm has been improved by extending the bits used to embed between 2-4 bits in smooth and sharp areas. To increase security, the transaction between the two parties is based on dividing the key and the cover image into several parts and agreeing on the type of edge detection.The experiments achieved the maximum load, for instance with a fuzzy edge detector, at first, embedding in 4 bitplanes if edge pixel and in 2 bitplanes if non-edge pixel, the peak signal-to-noise ratio (PSNR) increased from 43.580to 45.790. At second, embedding in 2 bitplanes if edge pixel, and in 4 bitplanes if non-edge pixel, the PSNR decreased between 38.433-41.593. The suggested scheme achieved a high pay load to embed in the cover image and according to human perception, it preserved the nature of the original image.

WAVELET-BASED EDGE DETECTION IN DIGITAL IMAGES

2008 ◽  
Vol 08 (04) ◽  
pp. 513-533 ◽  
Author(s):  
MUHAMMAD HUSSAIN ◽  
TURGHUNJAN ABDUKIRIM ◽  
YOSHIHIRO OKADA
Keyword(s):  
Edge Detection ◽  
Detection Method ◽  
Local Maxima ◽  
Wavelet Filters ◽  
Dyadic Wavelet ◽  
Edge Detectors ◽  
Frame Work ◽  
Dyadic Wavelet Transform ◽  
Edge Detection Method ◽  
Better Than

This paper proposes a wavelet based multilevel edge detection method that exploits spline dyadic wavelets and a frame work similar to that of Canny's edge detector.2 Using the recently proposed dyadic lifting schemes by Turghunjan et al.1 spline dyadic wavelet filters have been constructed, which are characterized by higher order of regularity and have the potential of better inherent noise filtering and detection results. Edges are determined as the local maxima in the subbands at different scales of the dyadic wavelet transform. Comparison reveals that our method performs better than Mallat's and Canny's edge detectors.

A NEW EDGE DETECTION METHOD THROUGH TEMPLATE MATCHING

1994 ◽  
Vol 08 (04) ◽  
pp. 899-917 ◽  
Author(s):  
MAO-JIUN J. WANG ◽  
SHIAU-CHYI CHANG ◽  
CHIH-MING LIU ◽  
WEN-YEN WU
Keyword(s):  
Edge Detection ◽  
Template Matching ◽  
Pattern Analysis ◽  
Detection Method ◽  
Performance Criteria ◽  
Detection Methods ◽  
Edge Detector ◽  
Edge Detectors ◽  
Edge Detection Method ◽  
Single Pixel

This paper reviews some gradient edge detection methods and proposes a new detector — the template matching edge detector (TMED). This detector utilizes the concepts of pattern analysis and the template matching of 3×3 masks. A set of performance criteria was used to evaluate the gradient edge detectors as well as the template matching edge detector. The results indicate that the new method is superior to the other gradient edge detectors. In addition, the template matching edge detector has also demonstrated good performance on noisy images. It can obtain very precise edge detection of single pixel width.

A step edge detector based on bilinear transformation

2021 ◽  
pp. 1-12
Author(s):  
Koorosh Dabighi ◽  
Akbar Nazari ◽  
Saeid Saryazdi
Keyword(s):  
Edge Detection ◽  
Signal To Noise Ratio ◽  
Quantitative Comparison ◽  
Discrete Version ◽  
Edge Detector ◽  
Bilinear Transformation ◽  
Proposed Model ◽  
Edge Detectors ◽  
Canny Edge ◽  
Error Metric

Nowadays, Canny edge detector is considered to be one of the best edge detection approaches for the images with step form. Various overgeneralized versions of these edge detectors have been offered up to now, e.g. Saryazdi edge detector. This paper proposes a new discrete version of edge detection which is obtained from Shen-Castan and Saryazdi filters by using bilinear transformation. Different experimentations are conducted to decide the suitable parameters of the proposed edge detector and to examine its validity. To evaluate the strength of the proposed model, the results are compared to Canny, Sobel, Prewitt, LOG and Saryazdi methods. Finally, by calculation of mean square error (MSE) and peak signal-to-noise ratio (PSNR), the value of PSNR is always equal to or greater than the PSNR value of suggested methods. Moreover, by calculation of Baddeley’s error metric (BEM) on ten test images from the Berkeley Segmentation DataSet (BSDS), we show that the proposed method outperforms the other methods. Therefore, visual and quantitative comparison shows the efficiency and strength of proposed method.

scholarly journals Hyperanalytic Wavelet-Based Robust Edge Detection

2021 ◽  
Vol 13 (15) ◽  
pp. 2888
Author(s):  
Alexandru Isar ◽  
Corina Nafornita ◽  
Georgiana Magu
Keyword(s):  
Edge Detection ◽  
Detection Method ◽  
State Of The Art ◽  
Speckle Noise ◽  
Transform Domain ◽  
Edge Detector ◽  
Edge Detectors ◽  
Gradient Based ◽  
New Type ◽  
Edge Detection Method

The imperfections of image acquisition systems produce noise. The majority of edge detectors, including gradient-based edge detectors, are sensitive to noise. To reduce this sensitivity, the first step of some edge detectors’ algorithms, such as the Canny’s edge detector, is the filtering of acquired images with a Gaussian filter. We show experimentally that this filtering is not sufficient in case of strong Additive White Gaussian or multiplicative speckle noise, because the remaining grains of noise produce false edges. The aim of this paper is to improve edge detection robustness against Gaussian and speckle noise by preceding the Canny’s edge detector with a new type of denoising system. We propose a two-stage denoising system acting in the Hyperanalytic Wavelet Transform Domain. The results obtained in applying the proposed edge detection method outperform state-of-the-art edge detection results from the literature.

An Edge Detection Method Based on Adjacent Dispersion

2016 ◽  
Vol 30 (10) ◽  
pp. 1655026
Author(s):  
Qindong Sun ◽  
Yimin Qiao ◽  
Hua Wu ◽  
Jiamin Wang
Keyword(s):  
Image Segmentation ◽  
Edge Detection ◽  
Detection Method ◽  
Superior Performance ◽  
Gray Level ◽  
Edge Detector ◽  
Edge Detectors ◽  
Gray Level Image ◽  
Novel Method ◽  
Edge Detection Method

Edge detection is a vital part in image segmentation. In this paper, a novel method based on adjacent dispersion for edge detection is proposed. This method utilizes adjacent dispersion to detect edges, avoiding thresholds selection, anisotropy in convolution computation and discontinuity in edges, and it is composed of two modules, namely the dispersion operator and the refinement. The dispersion is to obtain a matrix of discrete coefficient of a gray level image and the refinement is to thin edges to one-pixel-point and ensure it logically continuous. The performance of the proposed edge detector is evaluated on different test images and compared with popular edge detectors, Canny and Sobel. Experiment results indicate that the proposed method performs well without thresholds and offers superior performance in continuity in edge detection in digital images.

scholarly journals Implementasi Steganografi Video dengan Menggunakan Metode Egypt, Least Significant Bit (LSB) dan Least Significant Bit (LSB) Fibonacci Edge Pixel

2020 ◽  
Vol 10 (1) ◽  
pp. 25
Author(s):  
Ranida Pradita ◽  
Ida Nurhaida
Keyword(s):  
Mean Square Error ◽  
Digital Image ◽  
Digital Video ◽  
3D Model ◽  
Signal To Noise Ratio ◽  
Mean Square ◽  
Least Significant Bit ◽  
Signal To Noise ◽  
Edge Pixel ◽  
Audio Video

Seiring dengan perkembangan teknologi 5G, penyebaran dengan menggunakan video semakin besar dan mudah. Penyebaran informasi baik yang tersembunyi atau tidak semakin mudah disebarluaskan dengan menggunakan internet. Steganografi adalah cara menyembunyikan informasi dalam image atau video. Steganografi berbentuk digital image, text, audio, video, 3D model, dan lain-lain. Media digital yang popularitasnya paling tinggi dalam penelitian algoritma steganografi dengan menggunakan media digital image. Tulisan ini menggunakan media digital video karna media penelitian sebelumnya menggunakan media digital image. Pada tulisan ini akan diulas dan dianalis tentang video steganografi dengan menggunakan metode Egypt, Least Significant Bit (LSB), dan Least Significant Bit (LSB) Fibonacci Edge Pixel. Analisis video steganografi ini bertujuan untuk mendeteksi video yang mengandung unsur pesan rahasia yang kemungkinan untuk pengintaian. Hasil Peak Signal-to-Noise Ratio (PSNR) yang didapat dari penelitian ini rata-rata 40.46 dB dan menghasilkan rata-rata presentase similarity 30.67 %. Rata-rata Mean Square Error (MSE) pada penelitian ini adalah sebesar 0.50657. Untuk metode yang paling optimal yang digunakan dalam video steganografi adalah dengan menggunakan Metode Egypt.

scholarly journals Skema Penyembunyian Data pada Gambar Berbasis Interpolasi Kubik B-Spline Menggunakan Metode Least Significant Bit (LSB)

2019 ◽  
Vol 5 (3) ◽  
pp. 255
Author(s):  
Garno Garno ◽  
Riza Ibnu Adam
Keyword(s):  
Mean Squared Error ◽  
Signal To Noise Ratio ◽  
Image Interpolation ◽  
Cover Image ◽  
Original Image ◽  
Least Significant Bit ◽  
Signal To Noise ◽  
B Spline ◽  
Squared Error ◽  
Noise Ratio

Maraknya kasus pencurian data menyebabkan sistem keamanan pesan harus ditingkatkan. Salah satu cara untuk mengamankan pesan adalah dengan memasukkan pesan ke dalam gambar digital. Penelitian ini bertujuan untuk meningkatkan kualitas gambar digital dalam sistem keamanan pesan tersembunyi. Teknik yang digunakan untuk keamanan pesan adalah steganografi. Cover image akan dikonversi menjadi bit piksel dalam domain spasial. Cover image digunakan dalam bentuk gambar digital dengan format .jpg. Teknik meningkatkan kualitas dan kapasitas gambar digital dilakukan dengan menambahkan dan meningkatkan bit piksel menggunakan metode interpolasi Cubik B-Spline. Cover image yang telah di interpolasi, kemudian disisipi pesan menggunakan metode least significant bit (LSB) untuk memperoleh stegoimage. Pesan yang diselipkan berbentuk file .doc, .docx, .pdf, .xls, .rar, .iso dan .zip dengan ukuran berbeda-beda kapasitasnya. Teknik uji dibuat dengan bantuan perangkat lunak MATLAB versi 2017a. Penelitian melakukan uji dengan mengukur nilai kualitas penyamaran dari stegoimage menggunakan Peak Signal to Noise Ratio (PSNR) dengan rata-rata perolehan stegoimage terhadap Original image 29.06 dB dan stegoimage terhadap Image interpolation 64.34 dB dan uji mean squared error (MSE) dengan rata-rata perolehan 97.54 dB pada Image interpolation terhadap original image dan 97.55 dB pada stegoimage terhadap original image, 0.13 dB nilai MSE stegoimage terhadap Image interpolation. Hasil uji pada penelitian dengan proses interpolasi pada coverimage dengan Cubic B-Spline mempengaruhi terhadap nilai samar atau Nilai PSNR.

Image Edge Detection Based on Direction Fuzzy Entropy

2011 ◽  
Vol 268-270 ◽  
pp. 1234-1238
Author(s):  
Xian Qing Ling ◽  
Jun Lu ◽  
Lei Wang
Keyword(s):  
Edge Detection ◽  
Detection Method ◽  
Feature Space ◽  
Fuzzy Entropy ◽  
Noise Performance ◽  
Edge Pixel ◽  
Image Edge Detection ◽  
Image Edge ◽  
Edge Detection Method ◽  
Image Edges

To improve the ability of the fuzzy edge detection and anti-noise performance, the paper proposes a new weighted direction fuzzy entropy image edge detection method. The proposed method converts the feature space of image gray to the fuzzy feature space, and then extracts the weighted information measure of the direction structural in the fuzzy entropy feature space. Finally, the proposed method determines the edge pixel by an adaptive threshold after non-maxima suppression. The experiment demonstrates that the proposed method can extract the image edges effectively by means of the fuzzy edge detection.

An adaptive edge-detection method based on histogram

2018 ◽  
Vol 32 (34n36) ◽  
pp. 1840088
Author(s):  
Hongyang Zhao ◽  
Miaoyi Shang
Keyword(s):  
Edge Detection ◽  
Detection Method ◽  
Local Area ◽  
Adaptive Method ◽  
Good Effect ◽  
High Threshold ◽  
Edge Pixel ◽  
Edge Points ◽  
Current Threshold ◽  
Edge Detection Method

In order to solve the problems of poor adaptability when setting threshold and the high probability of detecting pseudo-edges in the existing methods of edge detection, the paper proposes an adaptive edge-detection method based on histogram. Multi-scale wavelet transform is used to preprocess the image, the image details are highlighted obviously, and it also can avoid the effect of manual setting filter coefficients. Difference of gray values between the pixels of local area are used to calculate the gradients comprehensively, it extends the gradient direction to four directions. When calculating the gradient of edge pixel, the four directions make the expression of the gradients of edge points more perfect and avoid the edge points missing. The adaptive method is used to compute the threshold of edge-detection, the image is represented by histogram. Then use the ratio of the number of pixels in the bar and the total numbers of pixels to set the initial threshold. The regions on both sides of the initial threshold are used to calculate the high threshold and low threshold until the reasonable error between the current threshold and the previous threshold is very small iteratively. The acquired threshold makes the self-adaptability more reasonable and stronger, it also avoids the detection errors, the connection errors and the pseudo-edges which are caused by setting threshold artificially. The experimental results show that the proposed algorithm of edge detection has a good effect of preserving edge detail and filtering noise of image.

Macrozooplankton Image Edge Detection Using Wavelet Packet Decomposition

2010 ◽  
Vol 108-111 ◽  
pp. 44-49
Author(s):  
Jing Ying Zhao ◽  
Hai Guo ◽  
Xing Bin Sun
Keyword(s):  
Edge Detection ◽  
Detection Method ◽  
Wavelet Packet ◽  
Detection Methods ◽  
Automatic Identification ◽  
Wavelet Packet Decomposition ◽  
Edge Detectors ◽  
Reconstruction Image ◽  
Image Edge ◽  
Edge Detection Method

Comparing with the phytoplankton, there are few researches on zooplanktons. Now, many waterworks don’t monitor the zooplanktons in source water. There isn’t effective detection method for several common macro zooplanktons such as chironomid larvae, cyclops and so on, and little has been done in the field of the macro zooplanktons automatic identification and monitor. This paper puts for forward a macrozooplankton edge detection method based on wavelet packet decomposition and reconstruction. We erase the high frequency parts by applying wavelet packet decomposition in the original images and then detect the edge of reconstruction images using the common edge detectors such as Prewitt, Sobel, Roberts, Laplacian of Gaussion, Canny and so on. The experimental results show that the edge detection methods in the reconstruction image work better than in the original image.

Sign in / Sign up

Export Citation Format

Share Document