Enhanced Image Fusion Methodology for Low Light Visible and Infrared Images
Praveen Kumar K1, Megha P Arakeri2

1Praveen Kumar K, Student in Information Science Engineering Department of Ramaiah Institute of Technology.
2Megha P Arakeri, Associate Professor in Information Science Engineering Department of Ramaiah Institute of Technology.

Manuscript received on 19 June 2019 | Revised Manuscript received on 05 July 2019 | Manuscript published on 30 July 2019 | PP: 1145-1149 | Volume-8 Issue-9, July 2019 | Retrieval Number: I8002078919/19©BEIESP | DOI: 10.35940/ijitee.I8002.078919

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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Image fusion is the mechanism in which at least two images are consolidated into a single image holding the imperative features from each one of the first images. Emerging images are upgraded and the image content is been enhanced in the entire context, this out coming image is much more preferable than the base images. Certain circumstances in image processing need both high dimensional and high spectral information in a solitary image, which is crucial in remote sensing. Image fusion procedure incorporates intensifying, filtering, and moulding the images for better results. Efficient and imperative approaches for image fusion are enforced here. The image fusion method comprises two discrete types of images, the visible image and the infrared image. The Single Scale Retinex (SSR) is applied to the visible image to obtain an upgraded image, simultaneously Principal Component Analysis (PCA) is been applied to infrared image to obtain an image with superior contrast and colour. Further these treated images are decomposed into a multilayer image by using Laplacian Pyramid algorithm. To end with Weighted Average fusion method aids in fusing the images to reproduce the augmented fused image.
Index Terms: Liver Disorder, Deep Learning Algorithms, Rprop, SAG, CNN, K-Fold Validation

Scope of the Article: Deep Learning