Digital image post processing techniques for taxonomic publications with reference to insects

2020 ◽  
Vol 12 (1) ◽  
pp. 15173-15180
Author(s):  
Nikhil Joshi ◽  
Hemant Ghate ◽  
Sameer Padhye
Keyword(s):  
Digital Image ◽  
Digital Images ◽  
Original Image ◽  
Post Processing ◽  
Taxonomic Information ◽  
Processing Techniques

There exists substantial literature for capturing digital images of insect specimens for taxonomy purposes but very few papers are available on post processing of these images.  We present a few techniques for editing digital images of insects using Adobe® Photoshop® which can be performed in a relatively short amount of time.  The results clearly show that techniques using a combination of options like Curves, Dodge/Burn, Hue/Saturation and Lab Color mode in the software, enhance the quality of the original image without changing any taxonomic information.  These methods applied in different combinations can be used for taxonomy of any insect taxon.  We also caution the readers of the abuse of such techniques in context of taxonomy. 

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 The Segmentation of Neutron Digital Radiography Image through the Edge Detection Method

2020 ◽  
Vol 10 (1) ◽  
pp. 11
Author(s):  
Ayu Fitri Amalia ◽  
Widodo Budhi
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Digital Image Processing ◽  
Neutron Beam ◽  
Digital Radiography ◽  
Digital Image ◽  
Digital Images ◽  
Neutron Radiography ◽  
Detection Methods

The digital image processing is one way to manipulate one or more digital images. Image segmentation has an essential role in the field of image analysis. The aim of this study was to develop an application to perform digital image processing of neutron digital radiographic images, hoping to improve the image quality of the digital images produced. The quality of edge detection could be used for the introduction of neutron digital radiographic image patterns through artificial intelligence. Interaction of neutrons with the matter mainly by nuclear reaction, elastic, and inelastic scattering. A neutron can quickly enter into a nucleus of an atom and cause a reaction. It is because a neutron has no charge. Neutrons can be used for digital imaging due to high-resolution information from deep layers of the material. The attenuated neutron beam in neutron radiography are passing through the investigated object. The object in a uniform neutron beam is irradiated to obtain an image neutron. The technique used in segmenting the neutron radiography in this study was a digital technique using a camera with a charge-coupled device (CCD), which was deemed more efficient technique compared to the conventional one. Through this technique, images could be displayed directly on the monitor without going through the film washing process. Edge detection methods were implemented in the algorithm program. It was the first step to complement the image information where edges characterize object boundaries. It is useful for the process of segmenting and identifying objects in neutron digital radiography images. The edge detection methods used in this study were Sobel, Prewitt, Canny, and Laplacian of Gaussian. According to the results of the image that have been tested for edge detection, the best image was carried out by the Canny operator because the method is more explicit. The obtained edges were more connected than the other methods which are still broken. The Canny technique provided edge gradient orientation which resulted in a proper localization.

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.

Typical errors when performing intraoral periapical x-rays, and their effect on image quality

2020 ◽  
pp. 36-44
Author(s):  
A.P. Arzhantsev
Keyword(s):  
Computed Tomography ◽  
Image Quality ◽  
Digital Image ◽  
Cone Beam ◽  
X Rays ◽  
Post Processing ◽  
X Ray ◽  
Selection Of ◽  
Periapical Radiography

During the study, intraoral periapical images were analyzed in 300 patients. The possibilities of using the methods of radiography and their influence on the quality of the obtained x-ray images were studied. The intraoral periapical radiography was compared with the results of orthopantomography and cone beam computed tomography. To identify the features of the mapping of zones of periapical destruction, 47 experimental x-ray studies were performed on skeletonized jaws with artificial defects in cortical plates. Often encountered errors are: an arbitrary choice of angles of inclination and the centration of the x-ray tube, the wrong location of the x-ray receiver in the patient's mouth, inaccurate installation or poor fixation of the patient's head, inefficient selection of physical and technical conditions of shooting, non-compliance with the conditions of the photo process with analog radiography or post-processing and printing digital image. The characteristic projection distortions of images resulting from these errors are analyzed and illustrated.

scholarly journals Research on Germination Prediction and Varietal Classification for Rice Seed Quality

2019 ◽  
Vol 9 (1) ◽  
pp. 3956-3959
Keyword(s):  
Image Processing ◽  
Digital Image Processing ◽  
Digital Image ◽  
Seed Quality ◽  
Germination Rate ◽  
Rice Seed ◽  
Image Processing Techniques ◽  
Varietal Classification ◽  
Processing Techniques

In agriculture most of the task done manually by experienced persons. They made decision on the basis of what they feel and see. The prediction result also not giving expected results. For getting the best yield the selection of quality seed is mandatory. But the manual analysis cannot assure the best quality seed. Rice Seed quality estimation can be done by considering the textural features of rice seed image. For this we are going to propose Digital Image processing Techniques to classify and grade the quality of the seed. There are number of digital image processing techniques proposed for classifying the variety of seed and predicting the germination rate of seed. In this paper we are going to summarize the hardware setup, varieties, features extracted, methods or algorithms used and result they obtained. In future we are going to propose a simple grading system for the rice seed quality system can be used by formers.

scholarly journals Multifocus Images Fusion Based On Homogenity and Edges Measures

2014 ◽  
Vol 11 (2) ◽  
pp. 660-672
Author(s):  
Baghdad Science Journal
Keyword(s):  
Digital Image ◽  
Digital Images ◽  
Signal To Noise Ratio ◽  
Evaluation Process ◽  
Statistical Techniques ◽  
Signal To Noise ◽  
High Quality ◽  
Simulation Process ◽  
Statistical Criteria

Image fusion is one of the most important techniques in digital image processing, includes the development of software to make the integration of multiple sets of data for the same location; It is one of the new fields adopted in solve the problems of the digital image, and produce high-quality images contains on more information for the purposes of interpretation, classification, segmentation and compression, etc. In this research, there is a solution of problems faced by different digital images such as multi focus images through a simulation process using the camera to the work of the fuse of various digital images based on previously adopted fusion techniques such as arithmetic techniques (BT, CNT and MLT), statistical techniques (LMM, RVS and WT) and spatial techniques (HPFA, HFA and HFM). As these techniques have been developed and build programs using the language MATLAB (b 2010). In this work homogeneity criteria have been suggested for evaluation fused digital image's quality, especially fine details. This criterion is correlation criteria to guess homogeneity in different regions within the image by taking a number of blocks of different regions in the image and different sizes and work shifted blocks per pixel. As dependence was on traditional statistical criteria such as (mean, standard deviation, and signal to noise ratio, mutual information and spatial frequency) and compared with the suggested criteria to the work. The results showed that the evaluation process was effective and well because it took into measure the quality of the homogenous regions.

scholarly journals Perbaikan Citra Digital dengan Menggunakan Filtering Technique dan Similarity Measurement

d'CARTESIAN ◽  
2013 ◽  
Vol 2 (2) ◽  
pp. 1
Author(s):  
Gybert Saselah ◽  
Winsy Weku ◽  
Luther Latumakulita
Keyword(s):  
Digital Image ◽  
Gaussian Noise ◽  
Digital Images ◽  
Wiener Filter ◽  
Reduction Process ◽  
Input Image ◽  
Similarity Measurement ◽  
Gaussian Filter ◽  
Original Image ◽  
Noise Filter

Abstract Often the digital image can be contaminated with noise,  which usually occurs in the process of retrieval or storage of digital images and delivery process either via satellite or  cable . By using the technique of filtering noise reduction process will be performed on a digital image that has previously been given Gaussian noise and followed by a Similarity Measurement to identify similarities between  image filtered and original image. This study was conducted to determine the appropriate filtering techniques to reduce the Gaussian noise. Image processing in this study composed by the input image and read the image matrix, converting images, adding noise, denoising digital images by applying filters performed using Matlab R2012a software ( version 7.14.0.739) . Application of Gaussian filter with a value of = 1.0 produce a digital image that is closest to the original image than the application of a Gaussian filter with another value, for  . As for the application of the Wiener filter is seen that the greater the value, the resulting digital image will be closer to the original image. For further research can be done on other types of noise or to a combination of two or more noise. Keywords : Digital Image , Noise , Filter , Similarity Measurement. Abstrak Seringkali citra digital dapat terkontaminasi derau (noise), yang biasanya terjadi pada proses pengambilan ataupun penyimpanan citra digital serta proses pengiriman citra digital baik melalui satelit maupun melalui kabel juga. Dengan menggunakan teknik filtering akan dilakukan proses pengurangan noise pada suatu citra digital yang sebelumnya telah diberi Gaussian noise dan dilanjutkan dengan Similarity Measurement untuk mengidentifikasi kesamaan citra digital hasil filtering dengan citra original. Penelitian ini dilakukan untuk menentukan teknik filtering yang tepat untuk mengurangi Gaussian noise. Proses pengolahan citra dalam penelitian ini terdiri dengan proses input gambar dan membaca matriks citra, konversi citra, menambahkan noise, denoising citra digital dengan menerapkan filter yang dilakukan dengan menggunakan software Matlab R2012a (versi 7.14.0.739). Penerapan Gaussian filter dengan nilai = 1,0 menghasilkan citra digital yang paling mendekati citra original dibandingkan dengan penerapan Gaussian filter dengan nilai  lain, dimana . Sedangkan untuk penerapan Wiener filter terlihat bahwa semakin besar nilai , maka citra digital yang dihasilkan akan semakin mendekati citra original. Untuk penelitian selanjutnya dapat dilakukan pada jenis noise lain ataupun untuk gabungan dua noise atau lebih. Kata kunci: Citra digital, Noise, Filter, Similarity Measurement

Digital Image Documentation for Quality Assessment

2001 ◽  
Vol 125 (11) ◽  
pp. 1430-1435
Author(s):  
Domingos Cruz ◽  
Carla Valentí ◽  
Aureliano Dias ◽  
Mário Seixas ◽  
Fernando Schmitt
Keyword(s):  
Quality Assessment ◽  
Digital Image ◽  
Feasible Solution ◽  
Digital Images ◽  
Region Of Interest ◽  
Routine Practice ◽  
Diagnostic Quality ◽  
Higher Power ◽  
The Impact

Abstract Objective.—To demonstrate the feasibility of the use of digital images to document routine cases and to perform diagnostic quality assessment. Methods.—Pathologists documented cases by acquiring up to 12 digital images per case. The images were sampled at 25:1, 50:1, 100:1, 200:1, or 400:1 magnifications, according to adequacy in aiding diagnosis. After each acquisition, the referral pathologist marked a region of interest within each acquired image in order to evaluate intrinsic redundancy. The extrinsic redundancy was determined by counting the unnecessary images. Cases were randomly selected and reviewed by one pathologist. The quality of each image, the possibility of accomplishing a diagnosis based on images, and the degree of agreement was evaluated. Results.—During routine practice, 1469 cases were documented using 3902 images. Most of the images were acquired at higher power magnifications. From all acquired cases, 143 cases and their 373 related images were randomly selected for review. In 88.1% (126/143) of reviewed cases, it was possible to accomplish the diagnosis based on images. In 30.2% (38/126) of these cases, the reviewer considered that the diagnosis could be accomplished with fewer images. The referral pathologist and the reviewer found intrinsic redundancy in 57.8% and 54.5% of images, respectively. Conclusions.—Our results showed that digital image documentation to perform diagnostic quality assessment is a feasible solution. However, owing to the impact on routine practice, guidelines for acquisition and documentation of cases may be needed.

Robust Watermarking Method using QIM with VSS for Image Copyright Protection

2021 ◽  
Vol 2021 (1) ◽  
pp. 28-40
Author(s):  
Minh Thanh Tạ
Keyword(s):  
Digital Image ◽  
Secret Sharing ◽  
Copyright Protection ◽  
Digital Images ◽  
Experimental Results ◽  
Watermark Image ◽  
Original Image ◽  
Visual Secret Sharing ◽  
Robust Watermarking ◽  
Copyright Information

This paper proposes a new watermarking method for digital image by composing the DWT-QIM based embedding with visual secret sharing (VSS) method. Firstly, the watermark image is separated into $n$ shares by using the $k-out-of-n$ method, called $(k,n)$ visual secret sharing. One of share is employed in order to embed into the original image for copyright protection. Another $(n-1)$ of shares are registered with Vietnam Copyright Department. When the dispute happens, the verifier can extract the watermark information from the watermarked image, then, decode it with $(k-1)$ shares chosen from $(n-1)$ shares to achieve the copyright information. Our experimental results show that our proposed method works efficiently on the digital images.

scholarly journals Pattern Recognition in Digital Images using Fractals

2019 ◽  
Vol 9 (2) ◽  
pp. 3180-3183
Keyword(s):  
Pattern Recognition ◽  
Digital Images ◽  
General Pattern ◽  
Post Processing ◽  
Fractal Image Compression ◽  
Pattern Recognition Problem ◽  
Design And Implementation ◽  
Fractal Image ◽  
Processing Techniques ◽  
First Time

Pattern recognition in digital images is a conjoint problem with application in remote sensing, electron microscopy, medical imaging and astrophysics, still no general solution which can be rivalled with the human cognitive system in which a pattern can be conceded subject to random positioning and scale. This research has stemmed in the design and implementation of a new algorithm for general pattern recognition based on the use of fractal image compression. This approach has for the first time allowed the pattern recognition problem to be solved in a way that is invariant of rotation and scale. It allows both ANNs and correlation to be used subject to appropriate pre-and post-processing techniques for digital image processing.

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