scholarly journals Penapisan Derau Gaussian, Speckle dan Salt&Pepper Pada Citra Warna

2011 ◽  
Vol 5 (3) ◽  
pp. 29
Author(s):  
Ika Purwanti Ningrum ◽  
Agfianto Eko Putra ◽  
Dian Nursantika
Keyword(s):  
Image Quality ◽  
Digital Image ◽  
Color Image ◽  
Median Filter ◽  
Bilateral Filter ◽  
Filter Method ◽  
Noise Filtering ◽  
Original Image ◽  
Average Filter

Quality of digital image can decrease becouse some noises. Noise can come from lower quality of image recorder, disturb when transmission data process and weather. Noise filtering can make image better becouse will filtering that noise from the image and can improve quality of digital image. This research have aim to improve color image quality with filtering noise. Noise (Gaussian, Speckle, Salt&Pepper) will apply to original image, noise from image will filtering use Bilateral Filter method, Median Filter method and Average Filter method so can improve color image quality. To know how well this research do, we use PSNR (Peak Signal to Noise Ratio) criteria with compared original image and filtering image (image after using noise and filtering noise).This research result with noise filtering Gaussian (variance = 0.5), highest PSNR value found in the Bilateral Filter method is 27.69. Noise filtering Speckle (variance = 0.5), highest PSNR value found in the Average Filter method is 34.12. Noise filtering Salt&Pepper (variance = 0.5), highest PSNR value found in the Median Filter method is 31.27. Keywords— Bilateral Filter, image restoration, derau Gaussian, Speckle dan Salt&Pepper

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.

scholarly journals Computational Efficient Method for Denoising of Salt and Pepper Noise for Image Enhancement

2020 ◽  
Vol 8 (6) ◽  
pp. 538-543
Keyword(s):  
Image Processing ◽  
Image Quality ◽  
Median Filter ◽  
Noise Removal ◽  
Input Image ◽  
Filter Method ◽  
Pixel Intensity ◽  
Feature Information ◽  
Salt And Pepper

Digital Image processing is basically a computer-algorithm which is used to enhance the quality of image to understand the feature of image and exact the meaningful features information from image. Image processing has wider range of algorithms to be applied to the input image and can escape the difficulty as the signal distortion and add noise in input image at the time of processing of images. Noises affect the image visualization and degraded the image quality, sometimes chaotic variation in value of pixel intensity, lighting effect or because of poor contrast, image can’t be used directly because many time interest feature information not received as output that’s one reason image processing is significant for removal of noise from images, so noise removal is becomes trending field in image processing. Median filter method is one of most popular method to eradicate the effect of noise from image and it enhances the image quality to take meaningful feature easily from image. In this paper removing of noise using median filter to enhance the image quality is discussed, also the importance and applications of enhancement technique are covered. Parameter PSNR and MSE is also used to analysis the image quality along with the visualization of image. Simulation results show that Median filter gives good outcome for salt & pepper noise as compare to other filtering method. MATLAB software is used as simulation tool.

scholarly journals Color Image Quality in Presentation Software

2008 ◽  
Vol 2008 ◽  
pp. 1-6 ◽  
Author(s):  
Edison Valencia ◽  
María S. Millán
Keyword(s):  
Image Quality ◽  
Digital Image ◽  
Color Image ◽  
Color Difference ◽  
Presentation Software ◽  
Quality Of Presentation

The color image quality of presentation programs is evaluated and measured using S-CIELAB and CIEDE2000 color difference formulae. A color digital image in its original format is compared with the same image already imported by the program and introduced as a part of a slide. Two widely used presentation programs—Microsoft PowerPoint 2004 for Mac and Apple's Keynote 3.0.2—are evaluated in this work.

Blind Digital Image Watermarking Algorithm Based on the Chain Code

2012 ◽  
Vol 546-547 ◽  
pp. 410-415
Author(s):  
Chun Ge Tang ◽  
Tie Sheng Fan ◽  
Lei Liu ◽  
Zhi Hui Li
Keyword(s):  
Image Quality ◽  
Digital Watermarking ◽  
Digital Image ◽  
Image Watermarking ◽  
Boundary Representation ◽  
Experimental Results ◽  
Digital Image Watermarking ◽  
Original Image ◽  
Chain Code ◽  
Decision Method

A new blind digital watermarking algorithm based on the chain code is proposed. The chain code is obtained by the characteristics of the original image -the edge contour. The feather can reflect the overall correlation of the vector image, and chain code expression can significantly reduce the boundary representation of the amount of data required. For the watermarking embedding, the original vector image is divided into sub-block images, and two bits of the watermarking information are embedded into sub-block images repeatedly by quantization. For watermarking extracting, the majority decision method is employed to determine the size of the extracted watermark. Experimental results show that the image quality is not significantly lowered after watermarking. The algorithm can resist the basic conventional attacks and has good robustness on the shear attacks.

scholarly journals Enhancement of Satellite Imagesusing Image Sharpening Technique

2019 ◽  
Vol 8 (6S2) ◽  
pp. 351-354
Keyword(s):  
Image Processing ◽  
Color Image ◽  
Histogram Equalization ◽  
Grayscale Image ◽  
Original Image ◽  
High Quality ◽  
Wide Range ◽  
Equalization Technique ◽  
The Difference

In many image processing applications, a wide range of image enhancement techniques are being proposed. Many of these techniques demanda lot of critical and advance steps, but the resultingimage perception is not satisfactory. This paper proposes a novel sharpening method which is being experimented with additional steps. In the first step, the color image is transformed into grayscale image, then edge detection process is applied using Laplacian technique. Then deduct this image from the original image. The resulting image is as expected; After performing the enhancement process,the high quality of the image can be indicated using the Tenengrad criterion. The resulting image manifested the difference in certain areas, the dimension and the depth as well. Histogram equalization technique can also be applied to change the images color.

scholarly journals Implementasi Metode Super Resolusi Untuk Meningkatkan Kualitas Citra Hasil Screenshot

2020 ◽  
Vol 7 (3) ◽  
pp. 432
Author(s):  
Windi Astuti
Keyword(s):  
Image Processing ◽  
Image Quality ◽  
Median Filter ◽  
Super Resolution ◽  
Linear Interpolation ◽  
Resolution Method ◽  
Image Zooming ◽  
Image Enlargement ◽  
Three Stages

Various types of image processing that can be done by computers, such as improving image quality is one of the fields that is quite popular until now. Improving the quality of an image is necessary so that someone can observe the image clearly and in detail without any disturbance. An image can experience major disturbances or errors in an image such as the image of the screenshot is used as a sample. The results of the image from the screenshot have the smallest sharpness and smoothness of the image, so to get a better image is usually done enlargement of the image. After the screenshot results are obtained then, the next process is cropping the image and the image looks like there are disturbances such as visible blur and cracked. To get an enlarged image (Zooming image) by adding new pixels or points. This is done by the super resolution method, super resolution has three stages of completion, first Registration, Interpolation, and Reconstruction. For magnification done by linear interpolation and reconstruction using a median filter for image refinement. This method is expected to be able to solve the problem of improving image quality in image enlargement applications. This study discusses that the process carried out to implement image enlargement based on the super resolution method is then built by using R2013a matlab as an editor to edit programs

Comparative Analysis on Noise Processing of Image

2013 ◽  
Vol 718-720 ◽  
pp. 2159-2162
Author(s):  
Hua Jun Dong ◽  
Xue Mei Jiang ◽  
Chen Xu Niu
Keyword(s):  
Image Processing ◽  
Comparative Analysis ◽  
Digital Image Processing ◽  
Digital Image ◽  
Median Filter ◽  
Wiener Filter ◽  
Image Noise ◽  
Two Dimensional ◽  
Wavelet Filter ◽  
Average Filter

The existence of noises have great interference on image processing, so the elimination of image noise is of great importance. In this paper, based on the digital image processing, the methods of average filter, wiener filter, median filter, two-dimensional wavelet filter, maximum and minimum filter are used to eliminate the salt & pepper noise of image. Then we analysis and compare the results of the five methods to find the best way to eliminate the image noise.

scholarly journals Digital imaging in pathology: the case for standardization

2005 ◽  
Vol 11 (3) ◽  
pp. 109-116 ◽  
Author(s):  
Yukako Yagi ◽  
John R Gilbertson
Keyword(s):  
Image Quality ◽  
Sample Preparation ◽  
Digital Image ◽  
Digital Imaging ◽  
Public Organization ◽  
Extensive Literature ◽  
Direct Examination ◽  
Transmission Of Information ◽  
Complete Process

The process of digital imaging in microscopy is a series of operations, each contributing to the quality of the final image that is displayed on the computer monitor. The operations include sample preparation and staining by histology, optical image formation by the microscope, digital image sampling by the camera sensor, postprocessing and compression, transmission across the network and display on the monitor. There is an extensive literature about digital imaging and each step of the process is fairly well understood. However, the complete process is very hard to standardize or even to understand fully. The important concepts for pathology imaging standards are: (1) systems should be able to share image files, (2) the standards should allow the transmission of information on baseline colours and recommended display parameters, (3) the images should be useful to the pathologist, not necessarily better or worse than direct examination of a slide under the microscope, (4) a mechanism to evaluate image quality objectively should be present, (5) a mechanism to adjust and correct the minor errors of tissue processing should be developed, (6) a public organization should support pathologists in the development of standards.

scholarly journals Improved Image Denoising Methodology using Deep CNN Bilateral Filter Compared to Additional Methods

2021 ◽  
Vol 10 (5) ◽  
pp. 191-196
Author(s):  
Pallavi Bora ◽  
Kapil Chaudhary
Keyword(s):  
Standard Deviation ◽  
Comparative Study ◽  
Image Denoising ◽  
Gaussian Noise ◽  
Bilateral Filter ◽  
Filter Method ◽  
Novel Approach ◽  
Deep Cnn ◽  
Test Result

Image Denoising techniques are widely used to remove the noise from the images. Due to the ease of the bilateral filter, it is used very often to remove the noise from the images. In this paper, a novel approach has been proposed to enhanced bilateral filter in conjunction with CNN as a booster to eliminate Gaussian noise from Grey images. Studies reveal that standard CNN using a bilateral filter is the best technique to eliminate Gaussian noise from images along with high PSNR values. This paper also performs a comparative study of the various existing techniques for image denoising with the CNN technique and the applied Bilateral filter Method as a de facto to improve the results in terms of enhanced PSNR values. ECND Net (Enhanced CNN) applied to noisy images with standard deviation σ = 15 gives PSNR values up to 32.81 In comparison to this when both bilateral filter and deep CNN applied, in conjunction produces improved PSNR values up to 34.73 along with the equivalent standard deviation. The results in this work reveal better performance in terms of PSNR as compared to other methods. The test result proves that the bilateral filter Method along with CNN can improve the quality of restored images significantly better.

scholarly journals Pengaruh Pencahayaan terhadap Kinerja Segmentasi

2014 ◽  
Vol 5 (1) ◽  
pp. 466
Author(s):  
Iman H. Kartowisatro
Keyword(s):  
Image Quality ◽  
Light Intensity ◽  
Digital Image ◽  
Segmentation Process ◽  
Reflection Of Light

 Light intensity may affect the image quality of an object that can cause barriers in doing preprocessing to improve the image quality of a digital image. Furthermore, this would also be an impact on subsequent processes, such as sorting images according to the needs. Light intensity influences the reflection of light which translates into an image. This gives an impact on successful segmentation process. Examples of the success and failure of segmentation are presented in this paper.

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