• Akansha Sharma
• Hitakshi Gupta
• Yashika Sharma

The method of combining significant details from two or more source images of a scene into a final fused image is known as Image Fusion. When compared to any of the other input images, the fused output image will have more detailed information in it. The objective of image fusion is to obtain the most desirable data from each image. Elementary requirement for image fusion is that fusion process should comprise all appropriate information with concentrated noise and it should not introduce any artifact in the fused image. The objective of this work deals with the edge detection approach for multi-focused images by means of complex wavelets-based image fusion. Many of the existing fusion algorithms extract the high-frequency and low-frequency information by designing some filters and then adopt different fusion rules to obtain the fused image. This study presumes that Discrete Wavelet Transform is one of the best and most effective algorithms for Image Fusion. So a wavelet is used for the multiscale decomposition of the source and fused images to obtain high-frequency and low-frequency images. To acquire more clear and complete fused image as result, deep convolutional neural network is used to learn the direct mapping between high-frequency and low-frequency images of the source and fused images.

Image Fusion, Deep Learning, Wavelet Transform, CNN, DWT, Multi-focus image

"Image Fusion with Deep Learning using Wavelet Transformation", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 3, page no.2826-2834, March-2021, Available :http://www.jetir.org/papers/JETIR2103359.pdf

"Image Fusion with Deep Learning using Wavelet Transformation", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 3, page no. pp2826-2834, March-2021, Available at : http://www.jetir.org/papers/JETIR2103359.pdf