1,955 research outputs found
Fast Separable Non-Local Means
We propose a simple and fast algorithm called PatchLift for computing
distances between patches (contiguous block of samples) extracted from a given
one-dimensional signal. PatchLift is based on the observation that the patch
distances can be efficiently computed from a matrix that is derived from the
one-dimensional signal using lifting; importantly, the number of operations
required to compute the patch distances using this approach does not scale with
the patch length. We next demonstrate how PatchLift can be used for patch-based
denoising of images corrupted with Gaussian noise. In particular, we propose a
separable formulation of the classical Non-Local Means (NLM) algorithm that can
be implemented using PatchLift. We demonstrate that the PatchLift-based
implementation of separable NLM is few orders faster than standard NLM, and is
competitive with existing fast implementations of NLM. Moreover, its denoising
performance is shown to be consistently superior to that of NLM and some of its
variants, both in terms of PSNR/SSIM and visual quality
Stacked Denoising Autoencoders and Transfer Learning for Immunogold Particles Detection and Recognition
In this paper we present a system for the detection of immunogold particles
and a Transfer Learning (TL) framework for the recognition of these immunogold
particles. Immunogold particles are part of a high-magnification method for the
selective localization of biological molecules at the subcellular level only
visible through Electron Microscopy. The number of immunogold particles in the
cell walls allows the assessment of the differences in their compositions
providing a tool to analise the quality of different plants. For its
quantization one requires a laborious manual labeling (or annotation) of images
containing hundreds of particles. The system that is proposed in this paper can
leverage significantly the burden of this manual task.
For particle detection we use a LoG filter coupled with a SDA. In order to
improve the recognition, we also study the applicability of TL settings for
immunogold recognition. TL reuses the learning model of a source problem on
other datasets (target problems) containing particles of different sizes. The
proposed system was developed to solve a particular problem on maize cells,
namely to determine the composition of cell wall ingrowths in endosperm
transfer cells. This novel dataset as well as the code for reproducing our
experiments is made publicly available.
We determined that the LoG detector alone attained more than 84\% of accuracy
with the F-measure. Developing immunogold recognition with TL also provided
superior performance when compared with the baseline models augmenting the
accuracy rates by 10\%
Self-Tuned Deep Super Resolution
Deep learning has been successfully applied to image super resolution (SR).
In this paper, we propose a deep joint super resolution (DJSR) model to exploit
both external and self similarities for SR. A Stacked Denoising Convolutional
Auto Encoder (SDCAE) is first pre-trained on external examples with proper data
augmentations. It is then fine-tuned with multi-scale self examples from each
input, where the reliability of self examples is explicitly taken into account.
We also enhance the model performance by sub-model training and selection. The
DJSR model is extensively evaluated and compared with state-of-the-arts, and
show noticeable performance improvements both quantitatively and perceptually
on a wide range of images
A Bayesian Hyperprior Approach for Joint Image Denoising and Interpolation, with an Application to HDR Imaging
Recently, impressive denoising results have been achieved by Bayesian
approaches which assume Gaussian models for the image patches. This improvement
in performance can be attributed to the use of per-patch models. Unfortunately
such an approach is particularly unstable for most inverse problems beyond
denoising. In this work, we propose the use of a hyperprior to model image
patches, in order to stabilize the estimation procedure. There are two main
advantages to the proposed restoration scheme: Firstly it is adapted to
diagonal degradation matrices, and in particular to missing data problems (e.g.
inpainting of missing pixels or zooming). Secondly it can deal with signal
dependent noise models, particularly suited to digital cameras. As such, the
scheme is especially adapted to computational photography. In order to
illustrate this point, we provide an application to high dynamic range imaging
from a single image taken with a modified sensor, which shows the effectiveness
of the proposed scheme.Comment: Some figures are reduced to comply with arxiv's size constraints.
Full size images are available as HAL technical report hal-01107519v5, IEEE
Transactions on Computational Imaging, 201
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