Editorial - Superresolution Image Reconstruction
2003.09.06 12:43
The spatial resolution which represents the number of pixels per unit area in an image is the principal factor in determining the quality of an image. With the development of image processing applications, there is a big demand for high-resolution (HR) images since HR images not only give the viewer a pleasing picture but also offer additional detail that is important for the analysis in many applications. The current technology to obtain HR images mainly depends on sensor manufacturing technology which attempts to increase the number of pixels per unit area by reducing the pixel size. However, the cost for high precision optics and sensors may be inappropriate for general purpose commercial applications, and there is a limitation to pixel size reduction due to shot noise encountered in the sensor itself. Therefore, a resolution enhancement approach using signal processing techniques has been a great concern in many areas, and it is called superresolution (or high-resolution) image reconstruction or simply resolution enhancement in the literature. In this issue, we use the term "superresolution (SR) image reconstruction" to refer to a signal processing approach toward resolution enhancement, because the term "super" in "super-resolution" represents very well the characteristics of the technique overcoming the inherent resolution limitation of low-resolution (LR) imaging systems.
The term superresolution was originally used in optics, and it refers to the algorithms which mainly operate on a single image to extrapolate the spectrum of an object beyond the diffraction limit (superresolution restoration). These two superresolution concepts (SR reconstruction and SR restoration) have a common focus in the aspect of recovering high frequency information which is lost or degraded during the image acquisition. However, the cause of the loss of high frequency information differs between these two concepts. Superresolution restoration in optics attempts to recover information beyond the diffraction cut-off frequency, while the superresolution reconstruction methods in engineering tries to recover high frequency components corrupted by aliasing. We hope that readers do not confuse the superresolution in this issue with the term superresolution used in optics.
SR image reconstruction algorithms investigate the relative motion information between multiple LR images (or a video sequence), and increase the spatial resolution by fusing them into a single frame. In doing so, it also removes the effect of possible blurring and noise in the LR images. In summary, the SR image reconstruction method estimates a HR image
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|---|---|---|---|---|
| 3 | Welcome to the Webboard of IEEE Signal Processing Magazine special issue | srip | 2003.09.06 | 5790 |
| 2 | Table Of Contents | srip | 2003.09.06 | 5502 |
| » | Editorial - Superresolution Image Reconstruction | srip | 2003.09.06 | 5707 |