Image Restoration Based on Morphological Operations

Raid, A. M.; Khedr, Wael M.; El-Dosuky, M. A.; Aoud, Mona

doi:10.5121/ijcseit.2014.4302

Cited by 73 publications

(39 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In our approach, opening and closing operations are performed on an image before detecting the keypoints. This morphological operations are used in binary images [9]. They can be used in grayscale images [8] which processes a singlechannel image.…”

Section: B Opening and Closingmentioning

confidence: 99%

MREAK: Morphological Retina Keypoint Descriptor

Vaghela

Oza

Bagul

2019

2019 International Conference of Artificial Intelligence and Information Technology (ICAIIT)

View full text Add to dashboard Cite

A variety of computer vision applications depend on the efficiency of image matching algorithms used. Various descriptors are designed to detect and match features in images. Deployment of this algorithms in mobile applications creates a need for low computation time. Binary descriptors requires less computation time than float-point based descriptors because of the intensity comparison between pairs of sample points and comparing after creating a binary string. In order to decrease time complexity, quality of keypoints matched is often compromised. We propose a keypoint descriptor named Morphological Retina Keypoint Descriptor (MREAK) inspired by the function of human pupil which dilates and constricts responding to the amount of light. By using morphological operators of opening and closing and modifying the retinal sampling pattern accordingly, an increase in the number of accurately matched keypoints is observed. Our results show that matched keypoints are more efficient than FREAK descriptor and requires low computation time than various descriptors like SIFT, BRISK and SURF.

show abstract

Section: B Opening and Closingmentioning

confidence: 99%

MREAK: Morphological Retina Keypoint Descriptor

Vaghela

Oza

Bagul

2019

2019 International Conference of Artificial Intelligence and Information Technology (ICAIIT)

View full text Add to dashboard Cite

show abstract

“…where the pore width is 0 for the solid phase v s , and 11d for the void phase v v . This corresponds to a morphological dilation (Aoud, 2014) of the void space compared to the physical model. While the standard dilation algorithm smoothes small scale variations of the geometry and thus preserves the connectivity, it does not preserve the porosity.…”

Section: Morphological Subpixel Pore Modelmentioning

confidence: 99%

Downscaling‐Based Segmentation for Unresolved Images of Highly Heterogeneous Granular Porous Samples

Korneev

Yang

Zachara

et al. 2018

Water Resources Research

View full text Add to dashboard Cite

Numerical simulations of pore‐scale flow and transport in natural sediments require the knowledge of pore‐space topology. Limited resolution of X‐ray tomography is often insufficient to fully characterize pore‐space structure within fine‐grained regions. Single and multilevel threshold‐based segmentation approaches are customarily employed to identify solid, pore and porous‐solid regions by means of grey intensity thresholds. While the choice of cutoff thresholds is often arbitrary, it dramatically affects the effective properties and the dynamical response of the reconstructed porous structure. We propose an algorithm of downscaling, i.e., the process of increasing image resolution, followed by segmentation, i.e., the identification of different phases, to reconstruct the unresolved pore‐space from XCT images of natural geological porous media. The method, applicable to moderately unresolved, chemically homogeneous granular media, is based on a map between local pixel porosity and pore size that does not rely on the definition of arbitrary thresholds and it allows to generate a high‐resolution binary image of the porous medium from poorly resolved grey‐scale images. First, we validate the method on synthetic unresolved images and compare their known pore‐space distribution with the extracted one. Then, we consider a synthetic porous medium and compare the pore size distribution, conductivity, and tortuosity between the original and the reconstructed structures. Finally, we apply the method to extract the pore‐space distribution from unresolved XCT images of two natural sediment columns and use it (i) to parametrize a capillary‐bundle model and (ii) to estimate the hydraulic conductivity by matching breakthrough behavior of passive solute transport.

show abstract

“…Saini and Arora [15] explained in detail about edge based segmentation and region based segmentation. Raid et al [16] explained and implemented different morphological operations like erosion, dilation, opening, closing etc.…”

Section: Literature Surveymentioning

confidence: 99%

Development and Implementation of Graphical User Interface for Image Preprocessing using Matlab

Shedthi¹,

Shetty²,

Siddappa³

2017

IJCA

View full text Add to dashboard Cite

Image pre-processing is a basic step for any image based applications. It is a technique to enhance quality of the images captured from cameras or sensors, aircrafts and space probes or pictures taken for various applications in normal day-today life. The accuracy of this technique must be significantly high in order to ensure the success of the subsequent steps. This intended software has the intention to make image preprocessing more efficient and interactive so that the user has clear idea about the outcome of each manipulation. In the intended application, one can manipulate the corrupted image to get the meaningful, desired output by applying various techniques provided in the software. The outcome of the intended application is obtained immediately and reduces the queue time to perform manipulations for the experienced user. If the user is a beginner, then he can go for trial session to try all the techniques and understand their outcome.

show abstract

Image Restoration Based on Morphological Operations

Abstract: ABSTRACT

Cited by 73 publications

References 8 publications

MREAK: Morphological Retina Keypoint Descriptor

MREAK: Morphological Retina Keypoint Descriptor

Downscaling‐Based Segmentation for Unresolved Images of Highly Heterogeneous Granular Porous Samples

Development and Implementation of Graphical User Interface for Image Preprocessing using Matlab

Contact Info

Product

Resources

About