VIDEO DEHAZING/DEFOGGING USING ML

Registration ID: IJNRD_219219

Published ID: IJNRD2404601

DOI: Click Here to Get

Jatin Sharma , Vedant Bhati

ML Machine learning video dehazing

The presence of haze in the atmosphere degrades the quality of images captured by visible camera sensors. The removal of haze, called dehazing, is typically performed under the physical degradation model, which necessitates a solution of an ill-posed inverse problem[3]. To relieve the difficulty of the inverse problem, a novel prior called dark channel prior (DCP) was recently proposed and has received a great deal of attention. The DCP is derived from the characteristic of natural outdoor images that the intensity value of at least one color channel within a local window is close to zero[1]. Based on the DCP, the dehazing is accomplished through four major steps: atmospheric light estimation, transmission map estimation, transmission map refinement, and image reconstruction. This four-step dehazing process makes it possible to provide a step-by-step approach to the complex solution of the ill-posed inverse problem. This also enables us to shed light on the systematic contributions of recent researches related to the DCP for each step of the dehazing process[1]. Our detailed survey and experimental analysis on DCP-based methods will help readers understand the effectiveness of the individual step of the dehazing process and will facilitate development of advanced dehazing algorithms.

Published Paper   E-Certificate

Jatin Sharma & Vedant Bhati (April-2024). VIDEO DEHAZING/DEFOGGING USING ML. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(4), g1-g8. https://ijnrd.org/papers/IJNRD2404601.pdf

Volume 9 Issue 4, April-2024

Pages : g1-g8

Paper Reg. ID: IJNRD_219219

Published Paper Id: IJNRD2404601

Downloads: 000122005

Research Area: Computer Science & Technology 

Country: Delhi, Delhi, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2404601.pdf

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Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

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