Dense Network-Based Spectral-Polarization Image Fusion: Multispectral Data Enhancement via Encoder-Decoder Approach

Images are obtained by perceiving the sunlight reflected by objects or scenes, and due to limited solar irradiance, spatial resolution certainly decreases. In contrast, multispectral sensors hold the spatial information of ground objects even though the obtained images only have a few bands, which means that the obtained images have high spatial but insufficient spectral resolution. To overcome these limitations, we have developed a spectral and polarization image fusion technique that improves several perspectives of image fusion, such as intensity, edges, textures, and substantial information. Our proposed method utilizes an encoder-decoder approach, which contains dense and convolutional blocks to simulate the source images. The polarization images have been used to develop DoLP images that help to improve fused images' quality. We can perceive the visual quality of fused images from the experimental results.