18,936 research outputs found
Improved Total Variation based Image Compressive Sensing Recovery by Nonlocal Regularization
Recently, total variation (TV) based minimization algorithms have achieved
great success in compressive sensing (CS) recovery for natural images due to
its virtue of preserving edges. However, the use of TV is not able to recover
the fine details and textures, and often suffers from undesirable staircase
artifact. To reduce these effects, this letter presents an improved TV based
image CS recovery algorithm by introducing a new nonlocal regularization
constraint into CS optimization problem. The nonlocal regularization is built
on the well known nonlocal means (NLM) filtering and takes advantage of
self-similarity in images, which helps to suppress the staircase effect and
restore the fine details. Furthermore, an efficient augmented Lagrangian based
algorithm is developed to solve the above combined TV and nonlocal
regularization constrained problem. Experimental results demonstrate that the
proposed algorithm achieves significant performance improvements over the
state-of-the-art TV based algorithm in both PSNR and visual perception.Comment: 4 Pages, 1 figures, 3 tables, to be published at IEEE Int. Symposium
of Circuits and Systems (ISCAS) 201
Can deep learning help you find the perfect match?
Is he/she my type or not? The answer to this question depends on the personal
preferences of the one asking it. The individual process of obtaining a full
answer may generally be difficult and time consuming, but often an approximate
answer can be obtained simply by looking at a photo of the potential match.
Such approximate answers based on visual cues can be produced in a fraction of
a second, a phenomenon that has led to a series of recently successful dating
apps in which users rate others positively or negatively using primarily a
single photo. In this paper we explore using convolutional networks to create a
model of an individual's personal preferences based on rated photos. This
introduced task is difficult due to the large number of variations in profile
pictures and the noise in attractiveness labels. Toward this task we collect a
dataset comprised of pictures and binary labels for each. We compare
performance of convolutional models trained in three ways: first directly on
the collected dataset, second with features transferred from a network trained
to predict gender, and third with features transferred from a network trained
on ImageNet. Our findings show that ImageNet features transfer best, producing
a model that attains accuracy on the test set and is moderately
successful at predicting matches
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