75 research outputs found
High-ISO long-exposure image denoising based on quantitative blob characterization
Blob detection and image denoising are fundamental, sometimes related tasks in computer vision. In this paper, we present a computational method to quantitatively measure blob characteristics using normalized unilateral second-order Gaussian kernels. This method suppresses non-blob structures while yielding a quantitative measurement of the position, prominence and scale of blobs, which can facilitate the tasks of blob reconstruction and blob reduction. Subsequently, we propose a denoising scheme to address high-ISO long-exposure noise, which sometimes spatially shows a blob appearance, employing a blob reduction procedure as a cheap preprocessing for conventional denoising methods. We apply the proposed denoising methods to real-world noisy images as well as standard images that are corrupted by real noise. The experimental results demonstrate the superiority of the proposed methods over state-of-the-art denoising methods
Advanced Restoration Techniques for Images and Disparity Maps
With increasing popularity of digital cameras, the field of Computa-
tional Photography emerges as one of the most demanding areas of
research. In this thesis we study and develop novel priors and op-
timization techniques to solve inverse problems, including disparity
estimation and image restoration.
The disparity map estimation method proposed in this thesis incor-
porates multiple frames of a stereo video sequence to ensure temporal
coherency. To enforce smoothness, we use spatio-temporal connec-
tions between the pixels of the disparity map to constrain our solution.
Apart from smoothness, we enforce a consistency constraint for the
disparity assignments by using connections between the left and right
views. These constraints are then formulated in a graphical model,
which we solve using mean-field approximation. We use a filter-based
mean-field optimization that perform efficiently by updating the dis-
parity variables in parallel. The parallel updates scheme, however, is
not guaranteed to converge to a stationary point. To compare and
demonstrate the effectiveness of our approach, we developed a new
optimization technique that uses sequential updates, which runs ef-
ficiently and guarantees convergence. Our empirical results indicate
that with proper initialization, we can employ the parallel update
scheme and efficiently optimize our disparity maps without loss of
quality. Our method ranks amongst the state of the art in common
benchmarks, and significantly reduces the temporal flickering artifacts
in the disparity maps.
In the second part of this thesis, we address several image restora-
tion problems such as image deblurring, demosaicing and super-
resolution. We propose to use denoising autoencoders to learn an
approximation of the true natural image distribution. We parametrize
our denoisers using deep neural networks and show that they learn
the gradient of the smoothed density of natural images. Based on
this analysis, we propose a restoration technique that moves the so-
lution towards the local extrema of this distribution by minimizing
the difference between the input and output of our denoiser. Weii
demonstrate the effectiveness of our approach using a single trained
neural network in several restoration tasks such as deblurring and
super-resolution. In a more general framework, we define a new
Bayes formulation for the restoration problem, which leads to a more
efficient and robust estimator. The proposed framework achieves state
of the art performance in various restoration tasks such as deblurring
and demosaicing, and also for more challenging tasks such as noise-
and kernel-blind image deblurring.
Keywords. disparity map estimation, stereo matching, mean-field
optimization, graphical models, image processing, linear inverse prob-
lems, image restoration, image deblurring, image denoising, single
image super-resolution, image demosaicing, deep neural networks,
denoising autoencoder
Dictionary Learning-based Inpainting on Triangular Meshes
The problem of inpainting consists of filling missing or damaged regions in
images and videos in such a way that the filling pattern does not produce
artifacts that deviate from the original data. In addition to restoring the
missing data, the inpainting technique can also be used to remove undesired
objects. In this work, we address the problem of inpainting on surfaces through
a new method based on dictionary learning and sparse coding. Our method learns
the dictionary through the subdivision of the mesh into patches and rebuilds
the mesh via a method of reconstruction inspired by the Non-local Means method
on the computed sparse codes. One of the advantages of our method is that it is
capable of filling the missing regions and simultaneously removes noise and
enhances important features of the mesh. Moreover, the inpainting result is
globally coherent as the representation based on the dictionaries captures all
the geometric information in the transformed domain. We present two variations
of the method: a direct one, in which the model is reconstructed and restored
directly from the representation in the transformed domain and a second one,
adaptive, in which the missing regions are recreated iteratively through the
successive propagation of the sparse code computed in the hole boundaries,
which guides the local reconstructions. The second method produces better
results for large regions because the sparse codes of the patches are adapted
according to the sparse codes of the boundary patches. Finally, we present and
analyze experimental results that demonstrate the performance of our method
compared to the literature
Reconstruction from Spatio-Spectrally Coded Multispectral Light Fields
In this work, spatio-spectrally coded multispectral light fields, as taken by a light field camera with a spectrally coded microlens array, are investigated. For the reconstruction of the coded light fields, two methods, one based on the principles of compressed sensing and one deep learning approach, are developed. Using novel synthetic as well as a real-world datasets, the proposed reconstruction approaches are evaluated in detail
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