Data Mining Practical Machine Learning Tools and Techniques

Pixel-level image fusion

FusionGAN: A generative adversarial network for infrared and visible image fusion

Infrared and visible image fusion methods and applications: A survey

In this paper, we present a novel deep learning architecture for infrared and visible images fusion problems. In contrast to conventional convolutional networks, our encoding network is combined with convolutional layers, a fusion layer, and dense block in which the output of each layer is connected to every other layer. We attempt to use this architecture to get more useful features from source images in the encoding process, and two fusion layers (fusion strategies) are designed to fuse these features. Finally, the fused image is reconstructed by a decoder. Compared with existing fusion methods, the proposed fusion method achieves the state-of-the-art performance in objective and subjective assessment.

DenseFuse: A Fusion Approach to Infrared and Visible Images

Two-scale image fusion of visible and infrared images using saliency detection

Image fusion is a process of generating a more informative image from a set of source images. Major applications of image fusion are in navigation and military. Here, infrared and visible sensors are used to capture complementary images of the targeted scene. The complementary information of these source images has to be integrated into a single image using some fusion algorithms. The aim of any fusion method is to transfer maximum information from the source images to the fused image with a minimum information loss. It has to minimize the artifacts in the fused image. In this paper, we propose a new edge preserving image fusion method for infrared and visible sensor images. Anisotropic diffusion is used to decompose the source images into approximation and detail layers. Final detail and approximation layers are calculated with the help of Karhunen-Loeve transform and weighted linear superposition, respectively. A fused image is generated from the linear combination of final detail and approximation layers. Performance of the proposed algorithm is assessed with the help of petrovic metrics. The results of the proposed algorithm are compared with the traditional and recent image fusion algorithms. Results reveal that the proposed method outperforms the existing methods.

Fusion of Infrared and Visible Sensor Images Based on Anisotropic Diffusion and Karhunen-Loeve Transform

Classification of imbalanced ECG beats using re-sampling techniques and AdaBoost ensemble classifier

In this paper, we propose a general purpose, simple and fast fusion algorithm based on guided image filter. 
The proposed method can well combine useful source image information into the fused image supported by multi-scale image decomposition, structure transferring property, visual saliency detection, and weight map construction. Multi-scale image decomposition is appropriate to represent and manipulate image features at various scales. Structure transferring property enabled by our algorithm can induce structures of one source image into the other. A new visual saliency detection based on guided image filter introduced in this paper is able to extract significant regions from visually different images of the same scene. The choice of weight maps helped to integrate the complementary information pixel by pixel at each scale. Experimental outcomes of the proposed method are compared and analyzed with traditional and recent guided image filter-based fusion algorithms in terms of visual quality, fusion metrics and run time. In addition, to enhance fusion results further we made an effort to find a suitable image and video enhancement algorithm. The fusion performance analysis clearly indicates that the proposed method is very promising along with less run time.

Ravindra Dhuli

Papers

Two-scale image fusion of visible and infrared images using saliency detection

Fusion of Infrared and Visible Sensor Images Based on Anisotropic Diffusion and Karhunen-Loeve Transform

Classification of imbalanced ECG beats using re-sampling techniques and AdaBoost ensemble classifier

Multi-scale Guided Image and Video Fusion: A Fast and Efficient Approach

Classification of ECG heartbeats using nonlinear decomposition methods and support vector machine