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  4. A Fusion Approach to Infrared and Visible Images with DenseFuse
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Category: MTech DIP Projects
By MTech Projects
MTech Projects
24.Nov
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A Fusion Approach to Infrared and Visible Images with DenseFuse

PROJECT TITLE :

DenseFuse A Fusion Approach to Infrared and Visible Images

ABSTRACT:

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.

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  • Joint Adaptive Regularization and Thresholding on Manifolds for Depth Restoration from RGB-D Data
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  • Inner and Inter Label Propagation: Salient Object Detection in the Wild - 2015
  • Color Correction Using Root-Polynomial Regression - 2015
  • Toward a Unified Color Space for Perception-Based Image Processing - 2012
  • Radiologists' performance in breast cancer screening is improved by deep neural networks.
  • Automatic hookworm detection in wireless capsule endoscopy images - 2016
  • Encoding mode selection in HEVC with the use of noise reduction - 2017
Previous article: Gated Fusion for Deformable Object Tracking Gated Fusion for Deformable Object Tracking Next article: Joint Adaptive Regularization and Thresholding on Manifolds for Depth Restoration from RGB-D Data Joint Adaptive Regularization and Thresholding on Manifolds for Depth Restoration from RGB-D Data
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