• Y.Rakesh
• V Prasanthi
• D Hemambika
• K Gosai Manikanta

Here we propose a multi-exposure image fusion (MEF) algorithm that optimizes a novel objective quality measure, namely the color MEF structural similarity (MEF-SSIM) index. The design methodology is substantially different approach to design MEF algorithms. In particular, we directly operate in the space of all images, iteratively searching for the image that optimizes MEF -SSIM. Specifically, we first construct the MEF-SSIM index by improving upon and expanding the application scope of the existing MEF-SSIM algorithm. We then describe a gradient ascent-based algorithm, which starts from any initial point in the space of all possible images and iteratively moves towards the direction that improves MEF-SSIM until convergence. The proposed algorithm consistently produces better quality fused images both visually and in terms of MEF-SSIM. The final high quality fused image appears to have little dependence on the initial image. The proposed optimization framework is readily extensible to construct better MEF algorithms when better objective quality models for MEF are available.

Multi-exposure image fusion (MEF), gradient ascent, structural similarity (SSIM), perceptual Optimization.

"Optimization of MEF-SSIM by Gradient Ascent Method", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.7, Issue 4, page no.1859-1864, April-2020, Available :http://www.jetir.org/papers/JETIR2004453.pdf

"Optimization of MEF-SSIM by Gradient Ascent Method", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 4, page no. pp1859-1864, April-2020, Available at : http://www.jetir.org/papers/JETIR2004453.pdf