DenseFuse A Fusion Approach to Infrared and Visible Images


For infrared and visible image fusion challenges, we have developed a revolutionary Deep Learning architecture. With our encoder, the outputs of each layer are connected to the outputs of all the other layers, unlike standard convolutional networks that use only convolutional layers and a fusion layer. Two fusion layers (fusion methods) have been devised as part of this architecture in order to combine the beneficial information extracted from source images during encoding. Finally, a decoder reconstructs the merged image. The suggested fusion method outperforms existing fusion methods in both objective and subjective assessment.

Did you like this research project?

To get this research project Guidelines, Training and Code... Click Here

PROJECT TITLE : NCF: A Neural Context Fusion Approach to Raw Mobility Annotation ABSTRACT: Improving business intelligence in mobile environments requires a thorough comprehension of human mobility patterns on a point-of-interest
PROJECT TITLE : Small Low-Contrast Target Detection Data-Driven Spatiotemporal Feature Fusion and Implementation ABSTRACT: An essential and difficult task in the airspace is the detection of low-contrast targets that are relatively
PROJECT TITLE : A Multi-Stream Feature Fusion Approach for Traffic Prediction ABSTRACT: The ability to predict traffic flow that is both accurate and timely is essential for intelligent transportation systems (ITS). Recent
PROJECT TITLE : Deep Guided Learning for Fast Multi-Exposure Image Fusion ABSTRACT: MEF-Net is a rapid multi-exposure image fusion (MEF) approach for static image sequences of adjustable spatial resolution and exposure number
PROJECT TITLE : Variational Osmosis for Non-Linear Image Fusion ABSTRACT: Non-linear image fusion can be improved by using a novel variational model proposed by us. Osmosis energy terms that are similar to those explored in Vogel

Ready to Complete Your Academic MTech Project Work In Affordable Price ?

Project Enquiry