Adaptive Smoothing Technique for Remotely Sensed Images Enhancement

J. J. Muhsin

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Publication Date

Sat Mar 01 2008

Journal Name

Iraqi Journal Of Physics

Volume

5

Issue Number

1

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Adaptive Smoothing Technique for Remotely Sensed Images Enhancement

Smoothing

Enhancement

Image

J. J. Muhsin

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Spatial and frequency domain techniques have been adopted in this search. mean
value filter, median filter, gaussian filter. And adaptive technique consists of
duplicated two filters (median and gaussian) to enhance the noisy image. Different
block size of the filter as well as the sholding value have been tried to perform the
enhancement process.

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Publication Date

Thu Feb 01 2018

Journal Name

Journal Of Economics And Administrative Sciences

Estimation of parameters of two-dimensional sinusoidal signal model by employing Deferential Evaluation algorithm and the use of Sequential approach in estimation

Two dimension sinusoidal signal model

sequential Deferential Evaluation estimators method

Symmetric gray scale texture image.

ظافر حسين

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Estimation the unknown parameters of a two-dimensional sinusoidal signal model is an important and a difficult problem , The importance of this model in modeling Symmetric gray- scale texture image . In this paper, we propose employment Deferential Evaluation algorithm and the use of Sequential approach to estimate the unknown frequencies and amplitudes of the 2-D sinusoidal components when the signal is affected by noise. Numerical simulation are performed for different sample size, and various level of standard deviation to observe the performance of this method in estimate the parameters of 2-D sinusoidal signal model , This model was used for modeling the Symmetric gray scale texture image and estimating by using

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Publication Date

Sat Oct 01 2022

Journal Name

Baghdad Science Journal

Offline Signature Biometric Verification with Length Normalization using Convolution Neural Network

Biometrics

Deep-learning

Handwritten signature

Histogram Oriented Gradient (HOG)

Image Processing

Length-normalization

Signature verification

Zahraa Mazin

Ghada Mohammad Tahir

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Offline handwritten signature is a type of behavioral biometric-based on an image. Its problem is the accuracy of the verification because once an individual signs, he/she seldom signs the same signature. This is referred to as intra-user variability. This research aims to improve the recognition accuracy of the offline signature. The proposed method is presented by using both signature length normalization and histogram orientation gradient (HOG) for the reason of accuracy improving. In terms of verification, a deep-learning technique using a convolution neural network (CNN) is exploited for building the reference model for a future prediction. Experiments are conducted by utilizing 4,000 genuine as well as 2,000 skilled forged signatu

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(1)

Publication Date

Tue Jun 20 2023

Journal Name

Baghdad Science Journal

Assessment of image quality of cervical spine complications using Three Magnetic Resonance Imaging Sequences

Cervical Spine Disease

Contrast to Noise Ratio

Image Quality

Magnetic Resonance Imaging

Signal to Noise Ratio

Nabaa A. Rasheed

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Examining and comparing the image quality of degenerative cervical spine diseases through the application of three MRI sequences; the Two-Dimension T2 Weighed Turbo Spin Echo (2D T2W TSE), the Three-Dimension T2 Weighted Turbo Spin Echo (3D T2W TSE), and the T2 Turbo Field Echo (T2_TFE). Thirty-three patients who were diagnosed as having degenerative cervical spine diseases were involved in this study. Their age range was 40-60 years old. The images were produced via a 1.5 Tesla MRI device using (2D T2W TSE, 3D T2W TSE, and T2_TFE) sequences in the sagittal plane. The image quality was examined by objective and subjective assessments. The MRI image characteristics of the cervical spines (C4-C5, C5-C6, C6-C7) showed significant difference

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(3)

Publication Date

Sat Oct 31 2020

Journal Name

International Journal Of Intelligent Engineering And Systems

Speech Emotion Recognition Using MELBP Variants of Spectrogram Image

Speech emotion

Spectrogram image

Multi-block extended local binary pattern (MELBP)

Deep beliefnetwork (DBN)

Short term fourier transform (STFT)

Suhaila N.

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Publication Date

Tue Aug 10 2021

Journal Name

Design Engineering

Lossy Image Compression Using Hybrid Deep Learning Autoencoder Based On kmean Clusteri

Image compression

Convolutional Autoencoder (CAE)

k-mean algorithm

PSNR

Compression Rate (CR)

MSE

Clustering

CLIC

Kodak

deep learning

lossy

Mohammed

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Image compression plays an important role in reducing the size and storage of data while increasing the speed of its transmission through the Internet significantly. Image compression is an important research topic for several decades and recently, with the great successes achieved by deep learning in many areas of image processing, especially image compression, and its use is increasing Gradually in the field of image compression. The deep learning neural network has also achieved great success in the field of processing and compressing various images of different sizes. In this paper, we present a structure for image compression based on the use of a Convolutional AutoEncoder (CAE) for deep learning, inspired by the diversity of human eye

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