scholarly journals An approach for image Contrast and Resolution Enhancement based Wavelet Transform by incorporating Cuckoo Search and SVD algorithms

2019 ◽  
Vol 8 (11) ◽  
pp. 24858-24868
Author(s):  
Sandeepa K S ◽  
Basavaraj N Jagadale ◽  
J S Bhat
Keyword(s):  
Wavelet Transform ◽  
Singular Value Decomposition ◽  
Search Algorithm ◽  
Resolution Enhancement ◽  
Cuckoo Search ◽  
Singular Value ◽  
Image Contrast ◽  
Discrete Wavelet ◽  
Maximum Value ◽  
Value Decomposition

Image enhancement techniques are prominently used to analyze the image by enhancing key factors like contrast, resolution, and quality of the image. With the proper analysis of images, it is desirable to pre-process the image for resolution and contrast enhancement. We present here a new approach based on discrete wavelet transform (DWT), singular value decomposition (SVD) for image contrast and resolution enhancement, The contrast of the image is enhanced by maximum value fusion technique applied to the images created by using modified cuckoo search algorithm (CSA) and singular value decomposition separately. The masking approach is employed, for obtaining residual pixel value between original and scaled images independently. The resolution of the image is enhanced by combining interpolated high-frequency sub-band and maximum value fusion images. The proposed algorithm helps to minimize the noise artifacts and over enhancement problems. Experimental results are tested in terms of peak signal to noise ratio (PSNR) and absolute mean brightness error (AMBE). The proposed method shows better performance compared to other contrast and resolution enhancement techniques.

scholarly journals Biosignal Compression Toolbox for Digital Biomarker Discovery

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 516
Author(s):  
Brinnae Bent ◽  
Baiying Lu ◽  
Juseong Kim ◽  
Jessilyn P. Dunn
Keyword(s):  
Wavelet Transform ◽  
Singular Value Decomposition ◽  
Data Compression ◽  
Biomarker Discovery ◽  
Singular Value ◽  
Sensor Data ◽  
Discrete Wavelet ◽  
Wearable Sensor ◽  
Huffman Encoding ◽  
Value Decomposition

A critical challenge to using longitudinal wearable sensor biosignal data for healthcare applications and digital biomarker development is the exacerbation of the healthcare “data deluge,” leading to new data storage and organization challenges and costs. Data aggregation, sampling rate minimization, and effective data compression are all methods for consolidating wearable sensor data to reduce data volumes. There has been limited research on appropriate, effective, and efficient data compression methods for biosignal data. Here, we examine the application of different data compression pipelines built using combinations of algorithmic- and encoding-based methods to biosignal data from wearable sensors and explore how these implementations affect data recoverability and storage footprint. Algorithmic methods tested include singular value decomposition, the discrete cosine transform, and the biorthogonal discrete wavelet transform. Encoding methods tested include run-length encoding and Huffman encoding. We apply these methods to common wearable sensor data, including electrocardiogram (ECG), photoplethysmography (PPG), accelerometry, electrodermal activity (EDA), and skin temperature measurements. Of the methods examined in this study and in line with the characteristics of the different data types, we recommend direct data compression with Huffman encoding for ECG, and PPG, singular value decomposition with Huffman encoding for EDA and accelerometry, and the biorthogonal discrete wavelet transform with Huffman encoding for skin temperature to maximize data recoverability after compression. We also report the best methods for maximizing the compression ratio. Finally, we develop and document open-source code and data for each compression method tested here, which can be accessed through the Digital Biomarker Discovery Pipeline as the “Biosignal Data Compression Toolbox,” an open-source, accessible software platform for compressing biosignal data.

scholarly journals Implementasi Teknik Watermarking Menggunakan Metode Discrete Wavelet Transform (DWT) dan Singular Value Decomposition (SVD) pada Citra Digital

2021 ◽  
Vol 14 (2) ◽  
pp. 125
Author(s):  
Verryna Adzillatul Fathiha
Keyword(s):  
Wavelet Transform ◽  
Singular Value Decomposition ◽  
Discrete Wavelet Transform ◽  
Singular Value ◽  
Discrete Wavelet ◽  
Value Decomposition

Watermarking merupakan teknik penyembunyian data/informasi kedalam suatu citra digital yang tidak kasat mata atau tidak dapat diketahui secara visual. Penyembunyian data/informasi kedalam citra tersebut bersifat rahasia. Karena tahan terhadap proses digitalisasi, teknik watermarking dapat digunakan untuk melindungi kepemilikan suatu citra digital. Ada tiga kriteria yang harus diperhatikan dalam watermarking pada citra digital, diantaranya adalah security, impreceptibily, dan robustness. Pada tugas Penulisan Karya Ilmiah (PI) ini dibuat suatu program watermarking menggunakan metode Discrete Wavelet Transform (DWT) dan Singular Value Decomposition (SVD) yang kemudian juga akan dipaparkan bagaimana cara menggunakan program watermarking yang telah dibuat. Keunggulan dari program ini citra yang digunakan dalam program watermarking ini dapat berupa citra berwarna maupun citra grayscale. Program watermarking ini dibuat menggunakan bahasa pemrograman C++ melalui aplikasi Matlab Kata Kunci            : Wateramarking , Singular Value Decomposition (SVD), Discrete Wavelet Transform (DWT), Matlab, Bahasa Pemrograman C++.

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