summarizes a Dirichlet-Net for pansharpening. Īnother chapter in this book by Qu et al. In recent studies, pansharpened images were observed to improve the performance of some applications. There are two recent survey papers in pansharpening. However, recent advancements have extended the approach to non-overlapping bands. Earlier pansharpening algorithms are limited to images where the panchromatic band overlaps with the MS bands. Pansharpening is an image fusion approach that fuses one high spatial resolution image with another low-resolution (LR) multispectral (MS) image. In many applications, we may have a high-resolution (HR) image with only a few bands and another image having low resolution but many bands. It was found that the reconstruction accuracy has been improved by close to 1 meter in one of the areas.ġ.1.2 Spatial-spectral resolution enhancement: Pansharpening Real satellite and ground-view images were used to demonstrate the proposed framework.

In Qin et al.’s chapter, the authors present a new framework for fusing results from cross view images for 3D mesh reconstruction. The chapter also pointed out some failure cases, which the authors knew the reasons and will further improve the results in the future. Experimental results showed that the proposed unsupervised approach is only 1–2 dBs inferior to state-of-the-art supervised algorithms. The key distinction from conventional GAN is that it is an unsupervised approach, meaning that no ground truth images are required for training. In Sidiya and Li’s chapter in this book, a generative adversarial network (GAN)-based approach was introduced to face image enhancement. Both the deep learning and dictionary approaches require many training images, which may be difficult to obtain. Another group is using dictionary-based approach. Vast amounts of training images are needed to train the algorithm. There is also a group of methods based on deep learning. The super-resolution algorithm in is based on edge interpolation. A notable one is the algorithm described in, which utilizes the PSF to improve the resolution of a single image. Recently, there are some new developments. A total of 16 neighbors are used to generate a prediction, and the performance is better than bilinear interpolation, which uses only four neighbors. The simplest method is the bicubic interpolation, which does not utilize any external information such as point spread function (PSF). Here, we focus on methods that use only a single image to improve the spatial resolution.

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