By Dr David Capel (auth.)
This booklet investigates units of pictures such as many overlapping viewsofa scene, and the way the knowledge contained inside of them could be mixed to supply unmarried pictures of more advantageous caliber. The known identify for such innovations is body fusion. utilizing body fusion, it's attainable to increase the fieldof view past that ofany unmarried photograph, to lessen noise, to revive high-frequency content material, or even to extend spatial answer and dynamic diversity. the purpose during this e-book is to advance effective, strong and automatic body fusion algorithms that could be utilized to genuine snapshot sequences. a vital step required to let body fusion is snapshot registration: computing the point-to-point mapping among pictures of their overlapping quarter. This sub challenge is taken into account intimately, and a powerful and effective resolution is proposed and its accuracy evaluated. different types of body fusion are then thought of: photograph mosaic ing and super-resolution. photo mosaicing is the alignment of a number of photographs right into a huge composition which represents a part of a 3D scene. Super-resolution is a extra refined process which goals to revive poor-quality video sequences by way of mod elling and elimination the degradations inherent within the imaging method, corresponding to noise, blur and spatial-sampling. A key point during this booklet is the idea of a totally uncalibrated cam period. No previous wisdom of the digicam parameters, its movement, optics or photometric features is believed. the ability of the equipment is illustrated with many actual photograph series examples.
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Additional resources for Image Mosaicing and Super-resolution
However, at many pixels, the difference in intensities, i 1 - i2 , cannot be explained by the linear photometric model. These model-outliers arise from various sources : saturation at the low or high end of the pixel intensity range, specularities or occlusions, shadows . Just as in the geometric estimation algorithm, it is vital that the line-fitting algorithm be robust to these model-outliers. The algorithm used for this purpose is Torr and Zisserman 's MSAC (M-estimator SAmple Consensus) algorithm , a variation ofthe previously described RANSAC algorithm.
Second , the use of robust statistics is essential to the success of the algorithm: in the example above, more than 40% of the putative matches between the interest points (obtained by the best cross-correlation score and proximity) are incorrect. It is the RANSAC algorithm that identifies the correct correspondences. 5 Chapter 3 Registration: Geometric and Photometric Assessing the accuracy of registration Super-resolution requires point-to-point mappings which are accurate to the sub-pixel level.
12: Although almost impossible to see in the evidence image, the difference image clearly shows a partial fingerprint. 13: Images of a plastic floor covering captured with a digital camera. The evidence image contains a partial shoe-mark, although it is extremely difficult to see. 14: After registration , the control image is aligned with the evidence image to sub-pixel accuracy. , J II . 15: Although very hard to see in the eviden ce image, the difference image clearly shows two partial marks made by the tread of a shoe.
Image Mosaicing and Super-resolution by Dr David Capel (auth.)