WITHDRAWN: Deep learning based image processing approaches for image deblurring

Gampala, Veerraju; Kumar, Mohit; Sushama, C.; Raj, E. Fantin Irudaya

doi:10.1016/j.matpr.2020.11.076

Cited by 23 publications

(5 citation statements)

References 7 publications

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“…As a result, we compute the boundaries voxels at each location given a shadow mask. The boundary elements are grouped according to the orientation of their coordinates (Li et al 2005;Raj and Balaji 2021;Gampala et al 2020). As a result, discrete border segments are formed to produce the shadow's border contour.…”

Section: Boundary Extractionmentioning

confidence: 99%

Escalating the resolution of an urban aerial image via novel shadow amputation algorithm

Rani¹,

Pushparaj²,

Raj³

2022

Earth Sci Inform

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In order to produce better images, darkness identification and detection are essential in remote sensing techniques. Throughout this method, shadow attributes are carefully considered during contrast enhancement, and suspicious darkness is always derived based on the aforementioned image features. Furthermore, a few dark artifacts that could still be misinterpreted as shadows are rejected. The whole feature engineering is completely dependent upon the size of the object as well as their relative positions. The precedent set of the inside and outside highlight profile line (IOHPL) can be used to consider removing reflective surfaces from object categories. IOHPL segmental synchronization results in modules that are relatively homogeneous. Following that, the relatively homogenous sections are used to find the percentage light diagnostic specifications between both the shadow as well as non-shadow regions. This process will result in the effective removal of excess shadows or darkness in satellite-derived aerial urban images.

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Section: Boundary Extractionmentioning

confidence: 99%

Escalating the resolution of an urban aerial image via novel shadow amputation algorithm

Rani¹,

Pushparaj²,

Raj³

2022

Earth Sci Inform

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show abstract

“…A comparison between the CNNbased method and the contour-support vector (SVM) machine was conducted [13] [31]. They have employed SLD, ASL, and ASL-FS, three separate standard information sites, in a range of situations including rotation and backdrop measurement [35] [36][47] [49]. The concert was purchased and coated as a convex shell using a cement method that was focused on width and convex angle.…”

Section: Literature Surveymentioning

confidence: 99%

Real time sign language detection system using deep learning techniques

2022

pnr

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Humans require communication in order to survive. It is a fundamental and effective method for communicating thoughts, feelings, and points of view. A greater number of babies are being born with hearing abnormalities, which puts them at a communication disadvantage with the rest of the world, according to data on physically challenged children during the last decade. People who are deaf or hard of hearing typically use sign languages to communicate. Hand gestures are used by Deaf or Mute persons to communicate; as a result, non-Deaf people have a hard time understanding their messages. Systems that can detect different signs and provide information to common people are thus necessary. To address this issue, we have developed an automated sign language detection system using deep learning, which helps deaf/mute people can communicate with normal people.

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“…Overall, their research has demonstrated the value of SVMs in distinguishing important information from irrelevant data. In active learning settings, the data was restored throughout trials for aiding in improving outcomes, the same holds true [18].…”

Section: Related Workmentioning

confidence: 99%

Investigation of Information gain and Chi test feature selection methods in dimensionality reduction using Machine learning for drug discovery

2022

pnr

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Despite all the recent improvements made in the pharmaceutical industry, especially in the area of cancer research, there is still much room for development. The process researchers use to find new drugs haven't really changed. It costs money to take a drug from its discovery to market availability. The Tufts Center conducted research that indicates it takes at least 13 years and costs about $2.6 billion to develop a new drug. To reduce drug discovery timeline, machine learning plays a major role. Machine learning typically uses feature selection as a preprocessing step. The performance of learning algorithms is frequently enhanced by the elimination of duplicate and unnecessary data. Comparison on Information gain and Chi test in drug discovery is presented in this paper, mainly concentrated on dimensionality reduction and used SVM classifier to categorize chemical compounds.

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WITHDRAWN: Deep learning based image processing approaches for image deblurring

Cited by 23 publications

References 7 publications

Escalating the resolution of an urban aerial image via novel shadow amputation algorithm

Escalating the resolution of an urban aerial image via novel shadow amputation algorithm

Real time sign language detection system using deep learning techniques

Investigation of Information gain and Chi test feature selection methods in dimensionality reduction using Machine learning for drug discovery

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