IMAGE DEHAZING USING HSV COLOR SPACE

IMAGE DEHAZING USING HSV COLOR SPACE

Dr. P Shanmugapriya1, V S Roshana2, M S Sneka3, J Sravani Sowmya Shri4 , V Vinodhini5
1Electronics and Communication Engineering, Saranathan College of Engineering. 2Electronics and Communication Engineering, Saranathan College of Engineering. 3Electronics and Communication Engineering, Saranathan College of Engineering. 4Electronics and Communication Engineering, Saranathan College of Engineering. 5Electronics and Communication Engineering, Saranathan College of Engineering.

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Abstract - The captured images are impaired in cloudy or foggy situations, resulting in decreased visibility, contrast, and colour fidelity. The air particles that attenuate and scatter the source radiation are to blame for this image deterioration. The degree of degradation is affected by several circumstances, including different air particle densities, their wavelengths, and distances from the acquisition instrument. For visible-band images, the currently employed image dehazing techniques either directly estimate the dehazed image or are dependent on prior assumptions to rebuild the transmission map. Traditionally, blurry images are recovered using the atmospheric scattering model (ATSM). Airlight and scene transmission are two unknown aspects or characteristics in ATSM that need to be calculated. The quality of the dehazed image is significantly influenced by how accurate these estimates are. The first parameter is the main topic of this essay. It adds a fresh method based on the HSV colour space for calculating the airlight. The haziest opaque region of the image is located using the HSV colour space. As a result, the amount of airlight in the chosen area is determined.

Keywords-Dehazing, Atmospheric Scattering Model, Color Spaces, HSV, Haze