scholarly journals Improving the Quality of Digital Images Using the Image Averaging Method

2020 ◽  
Vol 4 (1) ◽  
pp. 5
Author(s):  
Upa Yuandari
Keyword(s):  
Averaging Method ◽  
Digital Images ◽  
Image Data ◽  
Optical Devices ◽  
Digital Data ◽  
Color Intensity ◽  
Visual Appearance ◽  
Image Averaging

Image is a representation (picture), similarity, or imitation of an object. The use of digital images plays an important role as a form of information due to its advantages. Although it is rich in information, not all images have a good visual appearance. This can be due to interference in the form of noise, color intensity that is too contrasting or blurred. Noise itself is a disturbance caused by distorted digital data received by the image data receiver, starting from the movement during shooting by optical devices such as cameras, the use of optical devices that are not in focus, short lighting thereby reducing the number of photos captured by optical devices, or the weather when shooting. Noise can be removed by using the Image Averaging Method 

scholarly journals Improving the Quality of Digital Images Using the Median Filter Technique to Reduce Noise

SinkrOn ◽  
2019 ◽  
Vol 4 (1) ◽  
pp. 143 ◽  
Author(s):  
Annas Prasetio ◽  
Paska Marto Hasugian
Keyword(s):  
Digital Images ◽  
Color Image ◽  
Median Filter ◽  
Image Data ◽  
Original Image ◽  
Matrix Kernel ◽  
Filter Technique ◽  
Image Histogram ◽  
Point Line

The combination of point, line, shape and color elements combined to create a physical imitation of an object is called an image. The arrangement of the box elements in the image forms pixels or matrices. each image experiences degradation or loss of quality called noise. The effect of gaussian noise is the number of colored dots that are equal to the percentage of noise. This study raises the topic of improving the quality of digital images using median filter techniques to reduce noise. In this study using color image data (Red Green Blue) as test data and then converted into grayscale images to determine the gray degree of the image. The grayscale image is stored in the database. Then noise is generated by using random numbers. Noise in the form of impulse can be positive or negative in the form of adding pixel values to the original image, or it can reduce the value of the original image. The noise type used is salt & pepper. Gray degrees 0-255 spread. Can be calculated through image histograms. To reduce noise the median filter technique is used. Image histogram as a measure of the spread of numbers from the median filter. The result is a median filter can reduce noise salt and pepper by using a matrix kernel.

Improving the Security of Digital Images in Hadamard Transform Domain Using Digital Watermarking

Biometrics ◽  
2017 ◽  
pp. 1592-1618 ◽  
Author(s):  
V. Santhi ◽  
D. P. Acharjya
Keyword(s):  
Digital Watermarking ◽  
Computational Intelligence ◽  
Digital Images ◽  
Image Data ◽  
Multimedia Data ◽  
Hadamard Transform ◽  
Transform Domain ◽  
Scaling Parameter ◽  
Scaling Parameters

In recent days, due to the advancement in technology there are increasing numbers of threats to multimedia data which are floating around in the Internet especially in the form of image data. Many methods exist to provide security for digital images but transform domain based digital watermarking could be considered as a promising method. Many transformation techniques are used to insert watermark in cover data, but this chapter deals with watermarking approaches in Hadamard transform domain. In traditional watermarking approaches the scaling parameter is empirically considered for inserting watermark but to maintain the quality of underlying cover images it needs to be calculated based on the content of the cover images. In order to make the watermarking algorithm completely automated the embedding and scaling parameters are calculated using the content of cover images. Many methods are existing for calculating scaling parameter adaptively but this chapter discusses various approaches using computational intelligence to arrive at optimum value of scaling and embedding parameters.

Improving the Security of Digital Images in Hadamard Transform Domain Using Digital Watermarking

2016 ◽  
pp. 228-254
Author(s):  
V. Santhi ◽  
D. P. Acharjya
Keyword(s):  
Digital Watermarking ◽  
Computational Intelligence ◽  
Digital Images ◽  
Image Data ◽  
Multimedia Data ◽  
Hadamard Transform ◽  
Transform Domain ◽  
Scaling Parameter ◽  
Scaling Parameters

In recent days, due to the advancement in technology there are increasing numbers of threats to multimedia data which are floating around in the Internet especially in the form of image data. Many methods exist to provide security for digital images but transform domain based digital watermarking could be considered as a promising method. Many transformation techniques are used to insert watermark in cover data, but this chapter deals with watermarking approaches in Hadamard transform domain. In traditional watermarking approaches the scaling parameter is empirically considered for inserting watermark but to maintain the quality of underlying cover images it needs to be calculated based on the content of the cover images. In order to make the watermarking algorithm completely automated the embedding and scaling parameters are calculated using the content of cover images. Many methods are existing for calculating scaling parameter adaptively but this chapter discusses various approaches using computational intelligence to arrive at optimum value of scaling and embedding parameters.

Robust image data hiding method based on multiple backups and pixel bit weight

2021 ◽  
pp. 1-14
Author(s):  
Yu Dong ◽  
Xianquan Zhang ◽  
Chunqiang Yu ◽  
Zhenjun Tang ◽  
Guoen Xia
Keyword(s):  
Data Hiding ◽  
Digital Images ◽  
Image Data ◽  
Experimental Results ◽  
Cover Image ◽  
Secret Image ◽  
Robust Image ◽  
Reference Matrix ◽  
Least Significant Bits

Digital images are easily corrupted by attacks during transmission and most data hiding methods have limitations in resisting cropping and noise attacks. Aiming at this problem, we propose a robust image data hiding method based on multiple backups and pixel bit weight (PBW). Especially multiple backups of every pixel bit are pre-embedded into a cover image according to a reference matrix. Since different pixel bits have different weights, the most significant bits (MSBs) occupy more weights on the secret image than those of the least significant bits (LSBs). Accordingly, some backups of LSBs are substituted by the MSBs to increase the backups of MSBs so that the quality of the extracted secret image can be improved. Experimental results show that the proposed algorithm is robust to cropping and noise attacks for secret image.

Analysis of color Image Enhancement Using DWT, Wavelet Shrinkageand FHE Methods

2017 ◽  
Vol 7 (8) ◽  
pp. 56
Author(s):  
Ashish Dwivedi ◽  
Nirupma Tiwari
Keyword(s):  
Image Enhancement ◽  
Color Image ◽  
Human Perception ◽  
Histogram Equalization ◽  
Wavelet Shrinkage ◽  
Visual Appearance ◽  
Enhancement Method ◽  
Fuzzy Histogram ◽  
Rgb Image

Image enhancement (IE) is very important in the field where visual appearance of an image is the main. Image enhancement is the process of improving the image in such a way that the resulting or output image is more suitable than the original image for specific task. With the help of image enhancement process the quality of image can be improved to get good quality images so that they can be clear for human perception or for the further analysis done by machines.Image enhancement method enhances the quality, visual appearance, improves clarity of images, removes blurring and noise, increases contrast and reveals details. The aim of this paper is to study and determine limitations of the existing IE techniques. This paper will provide an overview of different IE techniques commonly used. We Applied DWT on original RGB image then we applied FHE (Fuzzy Histogram Equalization) after DWT we have done the wavelet shrinkage on Three bands (LH, HL, HH). After that we fuse the shrinkage image and FHE image together and we get the enhance image.

scholarly journals Fig Plant Segmentation from Aerial Images Using a Deep Convolutional Encoder-Decoder Network

2019 ◽  
Vol 11 (10) ◽  
pp. 1157 ◽  
Author(s):  
Jorge Fuentes-Pacheco ◽  
Juan Torres-Olivares ◽  
Edgar Roman-Rangel ◽  
Salvador Cervantes ◽  
Porfirio Juarez-Lopez ◽  
...  
Keyword(s):  
Precision Agriculture ◽  
Image Data ◽  
Ground Truth ◽  
Aerial Images ◽  
Aerial Image ◽  
Data Set ◽  
Visual Appearance ◽  
Aerial Robots ◽  
Lighting Conditions ◽  
Convolutional Encoder

Crop segmentation is an important task in Precision Agriculture, where the use of aerial robots with an on-board camera has contributed to the development of new solution alternatives. We address the problem of fig plant segmentation in top-view RGB (Red-Green-Blue) images of a crop grown under open-field difficult circumstances of complex lighting conditions and non-ideal crop maintenance practices defined by local farmers. We present a Convolutional Neural Network (CNN) with an encoder-decoder architecture that classifies each pixel as crop or non-crop using only raw colour images as input. Our approach achieves a mean accuracy of 93.85% despite the complexity of the background and a highly variable visual appearance of the leaves. We make available our CNN code to the research community, as well as the aerial image data set and a hand-made ground truth segmentation with pixel precision to facilitate the comparison among different algorithms.

scholarly journals Fast Multi-Focus Fusion Based on Deep Learning for Early-Stage Embryo Image Enhancement

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 863
Author(s):  
Vidas Raudonis ◽  
Agne Paulauskaite-Taraseviciene ◽  
Kristina Sutiene
Keyword(s):  
Deep Learning ◽  
Image Fusion ◽  
Early Stage ◽  
Image Data ◽  
Cell Detection ◽  
Processing Times ◽  
Fused Image ◽  
Stage Embryo ◽  
Early Stage Embryo

Background: Cell detection and counting is of essential importance in evaluating the quality of early-stage embryo. Full automation of this process remains a challenging task due to different cell size, shape, the presence of incomplete cell boundaries, partially or fully overlapping cells. Moreover, the algorithm to be developed should process a large number of image data of different quality in a reasonable amount of time. Methods: Multi-focus image fusion approach based on deep learning U-Net architecture is proposed in the paper, which allows reducing the amount of data up to 7 times without losing spectral information required for embryo enhancement in the microscopic image. Results: The experiment includes the visual and quantitative analysis by estimating the image similarity metrics and processing times, which is compared to the results achieved by two wellknown techniques—Inverse Laplacian Pyramid Transform and Enhanced Correlation Coefficient Maximization. Conclusion: Comparatively, the image fusion time is substantially improved for different image resolutions, whilst ensuring the high quality of the fused image.

Sign in / Sign up

Export Citation Format

Share Document