A comparison between PCA and some enhancement filters for denoising astronomical images

This paper includes a comparison between denoising techniques by using statistical approach, principal component analysis with local pixel grouping (PCA-LPG), this procedure is iterated second time to further improve the denoising performance, and other enhancement filters were used. Like adaptive Wiener low pass-filter to a grayscale image that has been degraded by constant power additive noise, based on statistics estimated from a local neighborhood of each pixel. Performs Median filter of the input noisy image, each output pixel contains the Median value in the M-by-N neighborhood around the corresponding pixel in the input image, Gaussian low pass-filter and Order-statistic filter also be used. Experimental results shows LPG-PCA method gives better performance, especially in image fine structure preservation, compared with other general denoising algorithms.

Download Full-text

A STUDY OF DIGITAL WATERMARKING RECOGNITION USING ORTHOGONAL CODE SEQUENCES WITH A BACK-PROPAGATION NEURAL NETWORK

Transactions of the Canadian Society for Mechanical Engineering ◽

10.1139/tcsme-2013-0035 ◽

2013 ◽

Vol 37 (3) ◽

pp. 459-465

Author(s):

Chih-Ta Yen ◽

Ing-Jr Ding ◽

Zong-Wei Lai

Keyword(s):

Neural Network ◽

Digital Watermarking ◽

Median Filter ◽

Back Propagation ◽

Back Propagation Neural Network ◽

Pass Filter ◽

Low Pass Filter ◽

Noise Interference ◽

Low Pass ◽

Neural Network Algorithm

Digital watermarking is an encryption technology commonly used to protect intellectual property and copyright. In this study, we restored watermarks that had already been affected by noise interference, used the Walsh–Hadamard codes as the watermark identification codes, and applied salt-and-pepper noise and Gaussian noise to destroy watermarks. First method, we used a low-pass filter and median filter to remove noise interferences. The second one, we used a back-propagation neural network algorithm to suppress noises. We removed nearly all noise and recovered the originally embedded watermarks of Walsh–Hadmard codes.

Download Full-text

Klasifikasi Kanker Paru Paru menggunakan Naïve Bayes dengan Variasi Filter dan Ekstraksi Ciri GLCM

INDONESIAN JOURNAL OF APPLIED PHYSICS ◽

10.13057/ijap.v11i2.53213 ◽

2021 ◽

Vol 11 (2) ◽

pp. 256

Author(s):

Mohtar Yunianto ◽

Soeparmi Soeparmi ◽

Cari Cari ◽

Fuad Anwar ◽

Delta Nur Septianingsih ◽

...

Keyword(s):

Naive Bayes ◽

Median Filter ◽

Naïve Bayes ◽

Gray Level ◽

Pass Filter ◽

Low Pass Filter ◽

Otsu Thresholding ◽

Low Pass ◽

Occurrence Matrix ◽

High Pass

<p class="AbstractText">Telah berhasil dilakukan klasifikasi kanker paru-paru dari 120 data citra CT Scan. Pada penelitian, proses preposisi dimulai dengan variasi filtering yaitu low pass filter, median filter, dan high pass filter. Segmentasi yang digunakan yaitu Otsu Thresholding yang kemudian teksturnya akan diekstraksi menggunakan fitur Gray Level Co-occurrence Matrix (GLCM) dengan variasi arah sudut. Hasil dari ekstraksi GLCM dijadikan database yang akan menjadi dataset untuk pengklasifikasian citra menggunakan klasifikasi naïve bayes. Hasil dari penelitian dengan 12 buah variasi diperoleh hasil variasi terbaik adalah median filter dengan arah sudut GLCM 0° menunjukkan tingkat akurasi yang paling tinggi sebesar 88,33 %.</p>

Download Full-text

A Coded Digital Watermarking Recognition Using Neural Network Method

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.284-287.2961 ◽

2013 ◽

Vol 284-287 ◽

pp. 2961-2964

Author(s):

Chih Ta Yen ◽

Ing Jr Ding ◽

Zong Wei Lai

Keyword(s):

Neural Network ◽

Digital Watermarking ◽

Median Filter ◽

Back Propagation ◽

Back Propagation Neural Network ◽

Pass Filter ◽

Low Pass Filter ◽

Noise Interference ◽

Low Pass ◽

Neural Network Algorithm

Digital watermarking is an encryption technology commonly used to protect intellectual property and copyright. Although watermarks possess advantageous secrecy and robustness, environmental interference in the image propagation through the Internet is inevitable and, certainly, human-based image modification can also destroy the watermark. In this study, we restored watermarks that had already been affected by noise interference, used the Walsh-Hadamard codes as the watermark identification codes, and applied salt-and-pepper noise and Gaussian noise to destroy watermarks. First, we used a low-pass filter and median filter to remove noise interferences. Although these filters can suppress noises, watermarked images remain unidentifiable when the noise interferences strongly. Finally, we used a back-propagation neural network algorithm to filter noises, obtaining results that exceeded our expectations. We removed nearly all noise and recovered the originally embedded watermarks of Walsh-Hadmard codes.

Download Full-text

Robust Speech Hashing for Digital Audio Forensics

Applied Sciences ◽

10.3390/app10010249 ◽

2019 ◽

Vol 10 (1) ◽

pp. 249 ◽

Cited By ~ 1

Author(s):

Diego Renza ◽

Jaisson Vargas ◽

Dora M. Ballesteros

Keyword(s):

Principal Component ◽

Experimental Tests ◽

Multimedia Content ◽

Audio Recording ◽

Pass Filter ◽

Low Pass Filter ◽

Audio Recordings ◽

Low Pass ◽

Main Components ◽

Fifth Order

The verification of the integrity and authenticity of multimedia content is an essential task in the forensic field, in order to make digital evidence admissible. The main objective is to establish whether the multimedia content has been manipulated with significant changes to its content, such as the removal of noise (e.g., a gunshot) that could clarify the facts of a crime. In this project we propose a method to generate a summary value for audio recordings, known as hash. Our method is robust, which means that if the audio has been modified slightly (without changing its significant content) with perceptual manipulations such as MPEG-4 AAC, the hash value of the new audio is very similar to that of the original audio; on the contrary, if the audio is altered and its content changes, for example with a low pass filter, the new hash value moves away from the original value. The method starts with the application of MFCC (Mel-frequency cepstrum coefficients) and the reduction of dimensions through the analysis of main components (principal component analysis, PCA). The reduced data is encrypted using as inputs two values from a particular binarization system using Collatz conjecture as the basis. Finally, a robust 96-bit code is obtained, which varies little when perceptual modifications are made to the signal such as compression or amplitude modification. According to experimental tests, the BER (bit error rate) between the hash value of the original audio recording and the manipulated audio recording is low for perceptual manipulations, i.e., 0% for FLAC and re-quantization, 1% in average for volume (−6 dB gain), less than 5% in average for MPEG-4 and resampling (using the FIR anti-aliasing filter); but more than 25% for non-perceptual manipulations such as low pass filtering (3 kHz, fifth order), additive noise, cutting and copy-move.

Download Full-text