By Dr David Capel (auth.)
This ebook investigates units of pictures such as many overlapping viewsofa scene, and the way the data contained inside them might be mixed to provide unmarried photographs of improved caliber. The primary identify for such thoughts is body fusion. utilizing body fusion, it's attainable to increase the fieldof view past that ofany unmarried picture, to minimize noise, to revive high-frequency content material, or even to extend spatial answer and dynamic diversity. the purpose during this publication is to enhance effective, powerful and automatic body fusion algorithms that may be utilized to actual snapshot sequences. a necessary step required to let body fusion is photo registration: computing the point-to-point mapping among photographs of their overlapping quarter. This sub challenge is taken into account intimately, and a strong and effective answer is proposed and its accuracy evaluated. kinds of body fusion are then thought of: snapshot mosaic ing and super-resolution. snapshot 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 strategy which goals to revive poor-quality video sequences by means of mod elling and elimination the degradations inherent within the imaging strategy, resembling noise, blur and spatial-sampling. A key aspect during this publication is the idea of a very uncalibrated cam period. No previous wisdom of the digicam parameters, its movement, optics or photometric features is believed. the facility of the tools is illustrated with many genuine picture series examples.
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7. 3. Attenuation Using the estimated photometric parameters, transform the colour channels of the control image. The two images should now be exactly alike modulo the latent mark. Subtract the control image from the evidence image to obtain the separated image. Often, such as is the case with bank notes imaged using a flat-bed scanner, a full 8 dof homography is unnecessary for aligning the two images. In these cases, greater accuracy can be achieved by modifying the geometric registration algorithm to compute a lower order transformation, such as a 6 dof affine transformation or 4 dof similarity transformation.
AAA • • • • • • • • • ~~~~A~AAAAAAAAAAA ~A • •••••••• " • " • tI • " " ••••••••• . . . . . . . . . . . . . III • III , " " " III III III , , , .. 4 .... ••••••• • 4 " .. ,"'\~~ ,""'\\\~ ~44 • • • • • • • • • • • , " , .. ,~ .. , ' " ~~44 , , .. 9: Forward and backward pixel-transfererror fields for the homography between images I and 2 in the "Mosaic" set, computedover a 500 x 500 pixel region in the centre of each image. Note that the vectors are magnified 100 times.
If not corrected, this can give rise to unsightly seams in rendered mosaics. The image blending function can be chosen to ameliorate this effect: common methods include simple averaging of intensity values, feathering and temporal median filtering. Better results may be obtained however, if photometric registration and correction is performed prior to rendering the mosaic. 3. 1. 2 Basic method registration HOl Hl2 reprojeclion 1 blending Figure 4. 1: The three basic steps in forming a mosaic representation of an image sequence and renderin g a novel view.