458 research outputs found
Representation Learning: A Review and New Perspectives
The success of machine learning algorithms generally depends on data
representation, and we hypothesize that this is because different
representations can entangle and hide more or less the different explanatory
factors of variation behind the data. Although specific domain knowledge can be
used to help design representations, learning with generic priors can also be
used, and the quest for AI is motivating the design of more powerful
representation-learning algorithms implementing such priors. This paper reviews
recent work in the area of unsupervised feature learning and deep learning,
covering advances in probabilistic models, auto-encoders, manifold learning,
and deep networks. This motivates longer-term unanswered questions about the
appropriate objectives for learning good representations, for computing
representations (i.e., inference), and the geometrical connections between
representation learning, density estimation and manifold learning
A flexible and accurate total variation and cascaded denoisers-based image reconstruction algorithm for hyperspectrally compressed ultrafast photography
Hyperspectrally compressed ultrafast photography (HCUP) based on compressed
sensing and the time- and spectrum-to-space mappings can simultaneously realize
the temporal and spectral imaging of non-repeatable or difficult-to-repeat
transient events passively in a single exposure. It possesses an incredibly
high frame rate of tens of trillions of frames per second and a sequence depth
of several hundred, and plays a revolutionary role in single-shot ultrafast
optical imaging. However, due to the ultra-high data compression ratio induced
by the extremely large sequence depth as well as the limited fidelities of
traditional reconstruction algorithms over the reconstruction process, HCUP
suffers from a poor image reconstruction quality and fails to capture fine
structures in complex transient scenes. To overcome these restrictions, we
propose a flexible image reconstruction algorithm based on the total variation
(TV) and cascaded denoisers (CD) for HCUP, named the TV-CD algorithm. It
applies the TV denoising model cascaded with several advanced deep
learning-based denoising models in the iterative plug-and-play alternating
direction method of multipliers framework, which can preserve the image
smoothness while utilizing the deep denoising networks to obtain more priori,
and thus solving the common sparsity representation problem in local similarity
and motion compensation. Both simulation and experimental results show that the
proposed TV-CD algorithm can effectively improve the image reconstruction
accuracy and quality of HCUP, and further promote the practical applications of
HCUP in capturing high-dimensional complex physical, chemical and biological
ultrafast optical scenes.Comment: 25 pages, 5 figures and 1 tabl
Removing striping artifacts in light-sheet fluorescence microscopy: a review
In recent years, light-sheet fluorescence microscopy (LSFM) has found a broad application for imaging of diverse biological samples, ranging from sub-cellular structures to whole animals, both in-vivo and ex-vivo, owing to its many advantages relative to point-scanning methods. By providing the selective illumination of sample single planes, LSFM achieves an intrinsic optical sectioning and direct 2D image acquisition, with low out-of-focus fluorescence background, sample photo-damage and photo-bleaching. On the other hand, such an illumination scheme is prone to light absorption or scattering effects, which lead to uneven illumination and striping artifacts in the images, oriented along the light sheet propagation direction. Several methods have been developed to address this issue, ranging from fully optical solutions to entirely digital post-processing approaches. In this work, we present them, outlining their advantages, performance and limitations
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