scholarly journals Resolution enrichment of side scan sonar image using wavelet based interpolation methods

2018 ◽  
Vol 7 (2.21) ◽  
pp. 375
Author(s):  
R Kumudham ◽  
S Tarun ◽  
R Avinash ◽  
V Rajendran
Keyword(s):  
Image Processing ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Discrete Wavelet ◽  
Stationary Wavelet Transform ◽  
Side Scan Sonar ◽  
Image Processing Techniques ◽  
Sonar Image ◽  
Processing Techniques

The resolution of the side scan sonar image which is used to detect on seabed such as mines, ship wrecks, etc is low. This paper helps to utilizes image processing techniques to enhance the resolution and thereby it makes detection and classification of underwater objects accurately. The proposed methods discussed in this paper are Discrete wavelet transform and stationary wavelet transform for enhancing the resolution.  

scholarly journals ENCRYPTION AND DECRYPTION USING IMAGE PROCESSING TECHNIQUES

2021 ◽  
Vol 5 (12) ◽  
Author(s):  
R.Uma Maheshwari
Keyword(s):  
Image Processing ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Binary Operation ◽  
Speckle Noise ◽  
Discrete Wavelet ◽  
Arnold Transform ◽  
Image Processing Techniques ◽  
Encryption And Decryption ◽  
Processing Techniques

Images consists various kinds of information which can be used for encryption and decryption of messages through them. This paper proposes a photograph encryption and decryption method to encrypt a covert snapshot with the aid of combining the Arnold transform system within the area and decrypting the duvet photograph through combining inverse Arnold become. First, grow to be a cover snapshot into subparts which consists of eight binary pictures with the aid of decimal value to eight-digit binary operation. Then, change into eight binary snap shots into sub-blocks of eight binary scrambled pixel by means of the Arnold change into, respectively. And then, recombine the sequence of the eight binary scrambled matrices right into a scrambled matrix with 256 grey phases in keeping with the special membership. Discrete Wavelet Transform (DWT) is used to perform picture compression on the input picture and secretly hidden photograph which is finished making use of alpha mixing. Sooner or later, derive an encrypted image from the scrambled photograph with the aid of the Hartley turn out to be. Second, decode the encrypted picture making use of inverse Arnold develop. Inverse DWT is performed to regain the compressed pictures. Simulations indicate that the proposed approach has a bigger photograph scrambling measure, more protection and has the robustness against occlusion and speckle noise attacks.

SOME NOTES ON THE APPLICATION OF DISCRETE WAVELET TRANSFORM IN IMAGE PROCESSING

2011 ◽  
Author(s):  
Egydio C. S. Caria ◽  
Trajano A. de A. Costa ◽  
João Marcos A. Rebello ◽  
Donald O. Thompson ◽  
Dale E. Chimenti
Keyword(s):  
Image Processing ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Discrete Wavelet

scholarly journals Classification of Asphalt Pavement Cracks Using Laplacian Pyramid-Based Image Processing and a Hybrid Computational Approach

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Nhat-Duc Hoang
Keyword(s):  
Image Processing ◽  
Computational Intelligence ◽  
Asphalt Pavement ◽  
Classification Model ◽  
Laplacian Pyramid ◽  
Pavement Condition ◽  
Crack Patterns ◽  
Image Processing Techniques ◽  
Processing Techniques

To improve the efficiency of the periodic surveys of the asphalt pavement condition, this study puts forward an intelligent method for automating the classification of pavement crack patterns. The new approach relies on image processing techniques and computational intelligence algorithms. The image processing techniques of Laplacian pyramid and projection integral are employed to extract numerical features from digital images. Least squares support vector machine (LSSVM) and Differential Flower Pollination (DFP) are the two computational intelligence algorithms that are employed to construct the crack classification model based on the extracted features. LSSVM is employed for data classification. In addition, the model construction phase of LSSVM requires a proper setting of the regularization and kernel function parameters. This study relies on DFP to fine-tune these two parameters of LSSVM. A dataset consisting of 500 image samples and five class labels of alligator crack, diagonal crack, longitudinal crack, no crack, and transverse crack has been collected to train and verify the established approach. The experimental results show that the Laplacian pyramid is really helpful to enhance the pavement images and reveal the crack patterns. Moreover, the hybridization of LSSVM and DFP, named as DFP-LSSVM, used with the Laplacian pyramid at the level 4 can help us to achieve the highest classification accuracy rate of 93.04%. Thus, the new hybrid approach of DFP-LSSVM is a promising tool to assist transportation agencies in the task of pavement condition surveying.

scholarly journals HOUGH TRANSFORM GENERATED STRONG IMAGE HASHING SCHEME FOR COPY DETECTION

2018 ◽  
Author(s):  
Mayank Srivastava ◽  
Jamshed M Siddiqui ◽  
Mohammad Athar Ali
Keyword(s):  
Image Processing ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Hough Transform ◽  
Rapid Development ◽  
Discrimination Performance ◽  
Discrete Wavelet ◽  
Image Hashing ◽  
Copy Detection ◽  
Detection Scheme

The rapid development of image editing software has resulted in widespread unauthorized duplication of original images. This has given rise to the need to develop robust image hashing technique which can easily identify duplicate copies of the original images apart from differentiating it from different images. In this paper, we have proposed an image hashing technique based on discrete wavelet transform and Hough transform, which is robust to large number of image processing attacks including shifting and shearing. The input image is initially pre-processed to remove any kind of minor effects. Discrete wavelet transform is then applied to the pre-processed image to produce different wavelet coefficients from which different edges are detected by using a canny edge detector. Hough transform is finally applied to the edge-detected image to generate an image hash which is used for image identification. Different experiments were conducted to show that the proposed hashing technique has better robustness and discrimination performance as compared to the state-of-the-art techniques. Normalized average mean value difference is also calculated to show the performance of the proposed technique towards various image processing attacks. The proposed copy detection scheme can perform copy detection over large databases and can be considered to be a prototype for developing online real-time copy detection system.   

scholarly journals Classification Of Wheat Grains Using Image Processing Techniques Based Neuro-Fuzzy System Model

2018 ◽  
pp. 57-61
Author(s):  
Ahmet Kayabasi ◽  
Kadir Sabanci ◽  
Abdurrahim Toktas
Keyword(s):  
Image Processing ◽  
Fuzzy System ◽  
Absolute Error ◽  
Data Set ◽  
Wheat Grains ◽  
Image Processing Techniques ◽  
Neuro Fuzzy ◽  
Length Width ◽  
Processing Techniques

In this study, an image processing techniques (IPTs) and a Sugeno-typed neuro-fuzzy system (NFS) model is presented for classifying the wheat grains into bread and durum. Images of 200 wheat grains are taken by a high resolution camera in order to generate the data set for training and testing processes of the NFS model. The features of 5 dimensions which are length, width, area, perimeter and fullness are acquired through using IPT. Then NFS model input with the dimension parameters are trained through 180 wheat grain data and their accuracies are tested via 20 data. The proposed NFS model numerically calculate the outputs with mean absolute error (MAE) of 0.0312 and classify the grains with accuracy of 100% for the testing process. These results show that the IPT based NFS model can be successfully applied to classification of wheat grains.

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