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Two key points of
pixel-level multi-focus image fusion are the clarity measure and the pixel
coeffi- cients fusion rule. Along with different improvements on these two
points, various fusion schemes have been proposed in literatures. However, the traditional
clarity measures are not designed for compressive imaging measurements which
are maps of source sense with random or likely ran- dom measurements matrix. This
paper presents a novel efficient multi-focus image fusion frame- work for
compressive imaging sensor network. Here the clarity measure of the raw
compressive measurements is not obtained from the random sampling data itself
but from the selected Hada- mard coefficients which can also be acquired from
compressive imaging system efficiently. Then, the compressive measurements with
different images are fused by selecting fusion rule. Finally, the block-based
CS which coupled with iterative projection-based reconstruction is used to re- cover
the fused image. Experimental results on common used testing data demonstrate
the effectiveness of the proposed method.
In the paper, an improved algorithm is presented for Delaunay triangulation of the point-set in the plain. Based on the original algorithm, we propose the notion of removing circle. During the process of triangulation, and the circle dynamically moves, the algorithm which is simple and practical, therefore evidently accelerates the process of searching a new point, while generating a new triangle. Then it shows the effect of the algorithm in the finite element mesh.
Total-Variation algorithm has a good result to de-noise for noise image of small
scale details, but it easily losses the details for the image with rich texture
and tiny boundary. In order to solve this problem, this paper proposes a Sobel-TV
model algorithm for image denoising. It uses TV model to de-noise and uses Sobel
algorithm to control smoothness of image, which not only efficiently removes image
noise but also simultaneously retail information, such as edge and texture. The
experiments demonstrate that the proposed algorithm is simple, practical and generates better SNR, which is an important value to preprocess image.
image caused by the tool eccentricity can often present two pieces of vertical
black strips in the Casing Well. To solve this problem, this paper proposes a correction
algorithm of time eccentricity image based
on ellipse fitting algorithm. This algorithm firstly utilizes borehole diameter data to fit ellipse and compute ellipse’s center, major axis, minor axis and inclination angle and other parameters, and then uses these
parameters to correct eccentrical ultrasonovision time
image. The tested results show that the algorithm can accurately fit ellipse
and correct the eccentrical ultrasonovision time image, which is very important
practical significance on processing the well logging.