نتایج جستجو برای: shearlet group
تعداد نتایج: 979538 فیلتر نتایج به سال:
abstract the present study tried to investigate the effect of visual aids (films) on the development of the speech act of disagreement among iranian efl intermediate learners. to this end, the researcher selected 40 homogeneous intermediate learners based on their scores on oxford placement test. .the subjects then divided into control group and experimental group. both classes were tested by ...
SAR images have been widely used in many fields such as military and remote sensing. So the suppression of the speckle has been an important research issues. To improve the visual effect of non-local means, generalized non-local (GNL) means with optimized pixel-wise weighting is applied to shrink the coefficients of non-subsample Shearlet transform (NSST) of SAR image. The new method can optimi...
this study investigated the effect of prior knowledge on the listening comprehension performance of fl learners. twenty students, male and female, drawn from the two intact groups of the senior and junior english language and literature majors were chosen as the participants of this study . the two groups were then randomly assigned into the experimental and control group. the experimental gr...
Since shearlets provide nearly optimally sparse representations for a large class of functions that are useful to model natural images, many image processing methods benefit from their use. In particular, the error rates of data estimation from noise are highly dependent on the sparsity properties of the representation, so that many successful applications of shearlets center around restoration...
ultrasound images suffer of multiplicative noise named speckle. different de-speckling algorithms run either in spatial domain or in transformed domain. in this paper, an adaptive filter in spatial domain according to assume the nakagami distribution as the statistic of log-compressed ultrasound images is used. for de-speckling in transformed domain, the non-sub sampled shearlet transform is us...
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