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Published online by Cambridge University Press: 06 December 2018
Increasing the spatial resolution of a given test image is of interest to the image processing community since the enhanced resolution of the image has better details when compared to the corresponding low resolution image. Super-resolution (SR) is an algorithmic approach in which a high spatial resolution image is obtained by using single/multiple low resolution observations or by using a database of LR–HR pairs. The linear image formation model discussed for image fusion in Chapter 4 is extended here to obtain an SR image for a given LR test observation. In the image fusion problem, the available Pan image was used in obtaining a high resolution fused image. Similar to the fusion problem, SR is also concerned with the enhancement of spatial resolution. However, we do not have a high resolution image such as a Pan image as an additional observation. Hence, we make use of a database of LR–HR pairs in order to obtain the SR for the given LR observation. Here, we use contourlet based learning to obtain the initial SR estimate which is then used in obtaining the degradation as well as the MRF parameter. Similar to the fusion problem discussed in Chapter 4, an MAP–MRF framework is used to obtain the final SR image. Note that we are not using the self-learning and sparse representation based approach proposed in Chapter 5 to obtain the fused image since the objective of this chapter is to illustrate a new approach for SR using the data model used in fusion.
Related Work
The low cost and ease of operation have significantly contributed to the growing popularity of digital imaging systems. Low cost cameras are fitted with low precision optics and lesser density detectors. Images captured using such a camera suffer from the drawback of reduced spatial resolution compared to traditional film cameras. Images captured using a camera fitted with high precision optics and image sensors comprising high density detectors provide better details that are essential in many imaging applications such as medical imaging, remote sensing and surveillance. However, the cost of such a camera is prohibitively high and obtaining a high resolution image is an important concern in many commercial applications requiring HR imaging. Images captured using a low cost camera represent the under-sampled images of a scene containing aliasing, blur and noise.
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