Abstract
Image super-resolution is a popular technique for increasing the resolution of a given image. Its most common application is to provide better visual effect after resizing a digital image for display or printing. In recent years, due to consumer multimedia products being in vogue, imaging and display device become ubiquitous, and image super-resolution is becoming more and more important. There are mainly three categories of approaches for this problem: interpolation-based methods, reconstruction-based methods, and learning-based methods.
This chapter is aimed, first, to explain the objective of image super-resolution, and then to describe the existing methods with special emphasis on color super-resolution. Finally, the performance of these methods is studied by carrying on objective and subjective image quality assessment on the super-resolution images.
The perfumes, the colors and the sounds are answered
Charles Baudelaire
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Maalouf, A., Larabi, MC. (2013). Image Super-Resolution, a State-of-the-Art Review and Evaluation. In: Fernandez-Maloigne, C. (eds) Advanced Color Image Processing and Analysis. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-6190-7_7
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DOI: https://doi.org/10.1007/978-1-4419-6190-7_7
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