SlantletTransformbased VideoDenoising

A technique for noise removal is proposed based on slantlet transform. The proposed algorithm tends to reduce the computational time by reducing the total number of frames through dividing the video film into sub films, finding master frames, applying the slantlet transform which is orthogonal discrete wavelet transform with two zero moments and with improved time localization. Thresholding technique is applied to the details coefficients of the slantlet transform .The denoised frame is repeated to retain the original frame sequence. The proposed method was applied by using MATLAB R2010a with video contaminated by white Gaussian noise .The experimental results show that the proposed method provides better subjective and objective quality, and obtain up to 5-6 dB PSNR improvement from the frames contaminated by noise

Implementation of 8-Point Slantlet Transform Based Polynomial Cancellation Coding-OFDM System Using FPGA

The objective of this paper is to implement a baseband OFDM transceiver on FPGA hardware. The design uses 8-point SLT/ISLT (Slantlet/Inverse Slantlet) for the processing module with processing block of 8 inputs data wide. All modules are designed and implemented using VHDL programming language. Software tools used in this work includes Altera Quartus II 7.2 and ModelSim Altera 6.1g, to assist the design process and downloading process into FPGA board while Cyclone III board EP3C120F780C7 is used to realize the designed module

An Enhancement of Data Hiding Imperceptibility using Slantlet Transform (SLT)

This study proposes a hybrid technique in securing image data that will be applied in telemedicine in future. Based on the web-based ENT diagnosis system using Virtual Hospital Server (VHS), patients are able to submit their physiological signals and multimedia data through the internet. In telemedicine system, image data need more secure to protect data patients in web. Cryptography and steganography are techniques that can be used to secure image data implementation. In this study, steganography method has been applied using hybrid between Discrete Cosine Transform (DCT) and Slantlet Transform (SLT) technique. DCT is calculated on blocks of independent pixels, a coding error causes discontinuity between blocks resulting in annoying blocking artifact. While SLT applies on entire image and offers better energy compaction compare to DCT without any blocking artifact. Furthermore, SLT splits component into numerous frequency bands called sub bands or octave bands. It is known that SLT is a better than DWT based scheme and better time localization. Weakness of DCT is eliminated by SLT that employ an improved version of the usual Discrete Wavelet Transform (DWT). Some comparison of technique is included in this study to show the capability of the hybrid SLT and DCT. Experimental results show that optimum imperceptibility is achieved

An Embedded Data Using Slantlet Transform

Data hiding is the process of encoding extra information in an image by making small modification to its pixels. To be practical, the hidden data must be perceptually invisible yet robust to common signal processing operations. This paper introduces a scheme for hiding a signature image that could be as much as 25% of the host image data and hence could be used both in digital watermarking as well as image/data hiding. The proposed algorithm uses orthogonal discrete wavelet transforms with two zero moments and with improved time localization called discrete slantlet transform for both host and signature image. A scaling factor ? in frequency domain control the quality of the watermarked images. Experimental results of signature image recovery after applying JPEG coding to the watermarking image are included

A Smart Content-Based Image Retrieval Approach Based on Texture Feature and Slantlet Transform

With the advancement of digital storing and capturing technologies in recent years, an image retrieval system has been widely known for Internet usage. Several image retrieval methods have been proposed to find similar images from a collection of digital images to a specified query image. Content-based image retrieval (CBIR) is a subfield of image retrieval techniques that extracts features and descriptions content such as color, texture, and shapes from a huge database of images. This paper proposes a two-tier image retrieval approach, a coarse matching phase, and a fine-matching phase. The first phase is used to extract spatial features, and the second phase extracts texture features based on the Slantlet transform. The findings of this study revealed that texture features are reliable and capable of producing excellent results and unsusceptible to low resolution and proved that the SLT-based texture feature is the perfect mate. The proposed method\u27s experimental results have outperformed the benchmark results with precision gaps of 28.0 % for the Caltech 101 dataset. The results demonstrate that the two-tier strategy performed well with the successive phase (fine-matching) and the preceding phase (coarse matching) working hand in hand harmoniously

Combining convolutional neural networks and slantlet transform for an effective image retrieval scheme

In the latest years there has been a profound evolution in computer science and technology, which incorporated several fields. Under this evolution, Content Base Image Retrieval (CBIR) is among the image processing field. There are several image retrieval methods that can easily extract feature as a result of the image retrieval methods’ progresses. To the researchers, finding resourceful image retrieval devices has therefore become an extensive area of concern. Image retrieval technique refers to a system used to search and retrieve images from digital images’ huge database. In this paper, the author focuses on recommendation of a fresh method for retrieving image. For multi presentation of image in Convolutional Neural Network (CNN), Convolutional Neural Network - Slanlet Transform (CNN-SLT) model uses Slanlet Transform (SLT). The CBIR system was therefore inspected and the outcomes benchmarked. The results clearly illustrate that generally, the recommended technique outdid the rest with accuracy of 89 percent out of the three datasets that were applied in our experiments. This remarkable performance clearly illustrated that the CNN-SLT method worked well for all three datasets, where the previous phase (CNN) and the successive phase (CNN-SLT) harmoniously worked together

A novel islanding detection technique using modified Slantlet transform in multi-distributed generation

In this paper, a new hybrid islanding detection scheme based on a combination of a modified Slantlet Transform (MSLT) and machine learning is applied to a passive time frequency islanding detection of multiple distributed generation units. A Harmony Search Algorithm (HSA) is used to optimally specify suitable scales of Slantlet transform and Slantlet decomposition levels for accurate islanding classification. Slantlet transform is utilized to derive the features in the required detection parameters measured from islanding and non-islanding events using identified Slantlet scales. In order to automate classification process, machine learning classifiers are utilized to detect islanding and non-islanding conditions with an objective of increasing the detection rate and avoiding nuisance distributed generation tripping during non-islanding situations. Islanding and non-islanding events are simulated for a multi-distributed generations system and used to assess the performance of the proposed anti-islanding protection method. The numerical results showing the efficiency of the proposed islanding detection technique are explained and conclusions are drawn

Brain image clustering by wavelet energy and CBSSO optimization algorithm

Previously, the diagnosis of brain abnormality was significantly important in the saving of social and hospital resources. Wavelet energy is known as an effective feature detection which has great efficiency in different utilities. This paper suggests a new method based on wavelet energy to automatically classify magnetic resonance imaging (MRI) brain images into two groups (normal and abnormal), utilizing support vector machine (SVM) classification based on chaotic binary shark smell optimization (CBSSO) to optimize the SVM weights. The results of the suggested CBSSO-based KSVM are compared favorably to several other methods in terms of better sensitivity and authenticity. The proposed CAD system can additionally be utilized to categorize the images with various pathological conditions, types, and illness modes

Comparative Analysis of Image Steganography using SLT, DCT and SLT-DCT Algorithm

Steganography is an interesting science to be studied and researched at this time, because steganography is the science of hiding messages on other digital media so that other parties are not aware of the existence of information in the digital media. Steganography is very effective in maintaining information security, because the existence of this information is obscured so that it is difficult to know where it is. This paper discusses hiding text into images using the Slantlet Transform (SLT) method, Descreate Cosine Transform (DCT) and Hybrid of SLT and DCT. The three methods are implemented in the frequency domain where steganographic imagery is transformed from the spatial domain to the frequency domain and the message bit is inserted into the cover image frequency component. The comparison parameters of these three techniques are based on MSE, PSNR, Capacity & Robustness. From the results of the tests that have been done, it is obtained that the highest PSNR value is generated using the SLT-DCT method, the largest storage capacity is the SLT method while the resistance, SLT-DCT method and DCT method are more resistant to attack than the SLT method