109,857 research outputs found
The Domain Transform Solver
We present a framework for edge-aware optimization that is an order of
magnitude faster than the state of the art while having comparable performance.
Our key insight is that the optimization can be formulated by leveraging
properties of the domain transform, a method for edge-aware filtering that
defines a distance-preserving 1D mapping of the input space. This enables our
method to improve performance for a variety of problems including stereo, depth
super-resolution, and render from defocus, while keeping the computational
complexity linear in the number of pixels. Our method is highly parallelizable
and adaptable, and it has demonstrable scalability with respect to image
resolution
Fast MPEG-CDVS Encoder with GPU-CPU Hybrid Computing
The compact descriptors for visual search (CDVS) standard from ISO/IEC moving
pictures experts group (MPEG) has succeeded in enabling the interoperability
for efficient and effective image retrieval by standardizing the bitstream
syntax of compact feature descriptors. However, the intensive computation of
CDVS encoder unfortunately hinders its widely deployment in industry for
large-scale visual search. In this paper, we revisit the merits of low
complexity design of CDVS core techniques and present a very fast CDVS encoder
by leveraging the massive parallel execution resources of GPU. We elegantly
shift the computation-intensive and parallel-friendly modules to the
state-of-the-arts GPU platforms, in which the thread block allocation and the
memory access are jointly optimized to eliminate performance loss. In addition,
those operations with heavy data dependence are allocated to CPU to resolve the
extra but non-necessary computation burden for GPU. Furthermore, we have
demonstrated the proposed fast CDVS encoder can work well with those
convolution neural network approaches which has harmoniously leveraged the
advantages of GPU platforms, and yielded significant performance improvements.
Comprehensive experimental results over benchmarks are evaluated, which has
shown that the fast CDVS encoder using GPU-CPU hybrid computing is promising
for scalable visual search
DPW-SDNet: Dual Pixel-Wavelet Domain Deep CNNs for Soft Decoding of JPEG-Compressed Images
JPEG is one of the widely used lossy compression methods. JPEG-compressed
images usually suffer from compression artifacts including blocking and
blurring, especially at low bit-rates. Soft decoding is an effective solution
to improve the quality of compressed images without changing codec or
introducing extra coding bits. Inspired by the excellent performance of the
deep convolutional neural networks (CNNs) on both low-level and high-level
computer vision problems, we develop a dual pixel-wavelet domain deep
CNNs-based soft decoding network for JPEG-compressed images, namely DPW-SDNet.
The pixel domain deep network takes the four downsampled versions of the
compressed image to form a 4-channel input and outputs a pixel domain
prediction, while the wavelet domain deep network uses the 1-level discrete
wavelet transformation (DWT) coefficients to form a 4-channel input to produce
a DWT domain prediction. The pixel domain and wavelet domain estimates are
combined to generate the final soft decoded result. Experimental results
demonstrate the superiority of the proposed DPW-SDNet over several
state-of-the-art compression artifacts reduction algorithms.Comment: CVPRW 201
Image Acquisition System Using On Sensor Compressed Sampling Technique
Advances in CMOS technology have made high resolution image sensors possible.
These image sensor pose significant challenges in terms of the amount of raw
data generated, energy efficiency and frame rate. This paper presents a new
design methodology for an imaging system and a simplified novel image sensor
pixel design to be used in such system so that Compressed Sensing (CS)
technique can be implemented easily at the sensor level. This results in
significant energy savings as it not only cuts the raw data rate but also
reduces transistor count per pixel, decreases pixel size, increases fill
factor, simplifies ADC, JPEG encoder and JPEG decoder design and decreases
wiring as well as address decoder size by half. Thus CS has the potential to
increase the resolution of image sensors for a given technology and die size
while significantly decreasing the power consumption and design complexity. We
show that it has potential to reduce power consumption by about 23%-65%
The STONE Transform: Multi-Resolution Image Enhancement and Real-Time Compressive Video
Compressed sensing enables the reconstruction of high-resolution signals from
under-sampled data. While compressive methods simplify data acquisition, they
require the solution of difficult recovery problems to make use of the
resulting measurements. This article presents a new sensing framework that
combines the advantages of both conventional and compressive sensing. Using the
proposed \stone transform, measurements can be reconstructed instantly at
Nyquist rates at any power-of-two resolution. The same data can then be
"enhanced" to higher resolutions using compressive methods that leverage
sparsity to "beat" the Nyquist limit. The availability of a fast direct
reconstruction enables compressive measurements to be processed on small
embedded devices. We demonstrate this by constructing a real-time compressive
video camera
Least-Squares FIR Models of Low-Resolution MR data for Efficient Phase-Error Compensation with Simultaneous Artefact Removal
Signal space models in both phase-encode, and frequency-encode directions are
presented for extrapolation of 2D partial kspace. Using the boxcar
representation of low-resolution spatial data, and a geometrical representation
of signal space vectors in both positive and negative phase-encode directions,
a robust predictor is constructed using a series of signal space projections.
Compared to some of the existing phase-correction methods that require
acquisition of a pre-determined set of fractional kspace lines, the proposed
predictor is found to be more efficient, due to its capability of exhibiting an
equivalent degree of performance using only half the number of fractional
lines. Robust filtering of noisy data is achieved using a second signal space
model in the frequency-encode direction, bypassing the requirement of a prior
highpass filtering operation. The signal space is constructed from Fourier
Transformed samples of each row in the low-resolution image. A set of FIR
filters are estimated by fitting a least squares model to this signal space.
Partial kspace extrapolation using the FIR filters is shown to result in
artifact-free reconstruction, particularly in respect of Gibbs ringing and
streaking type artifacts
Spectral Representations for Convolutional Neural Networks
Discrete Fourier transforms provide a significant speedup in the computation
of convolutions in deep learning. In this work, we demonstrate that, beyond its
advantages for efficient computation, the spectral domain also provides a
powerful representation in which to model and train convolutional neural
networks (CNNs).
We employ spectral representations to introduce a number of innovations to
CNN design. First, we propose spectral pooling, which performs dimensionality
reduction by truncating the representation in the frequency domain. This
approach preserves considerably more information per parameter than other
pooling strategies and enables flexibility in the choice of pooling output
dimensionality. This representation also enables a new form of stochastic
regularization by randomized modification of resolution. We show that these
methods achieve competitive results on classification and approximation tasks,
without using any dropout or max-pooling.
Finally, we demonstrate the effectiveness of complex-coefficient spectral
parameterization of convolutional filters. While this leaves the underlying
model unchanged, it results in a representation that greatly facilitates
optimization. We observe on a variety of popular CNN configurations that this
leads to significantly faster convergence during training
Sequential Principal Curves Analysis
This work includes all the technical details of the Sequential Principal
Curves Analysis (SPCA) in a single document. SPCA is an unsupervised nonlinear
and invertible feature extraction technique. The identified curvilinear
features can be interpreted as a set of nonlinear sensors: the response of each
sensor is the projection onto the corresponding feature. Moreover, it can be
easily tuned for different optimization criteria; e.g. infomax, error
minimization, decorrelation; by choosing the right way to measure distances
along each curvilinear feature. Even though proposed in [Laparra et al. Neural
Comp. 12] and shown to work in multiple modalities in [Laparra and Malo
Frontiers Hum. Neuro. 15], the SPCA framework has its original roots in the
nonlinear ICA algorithm in [Malo and Gutierrez Network 06]. Later on, the SPCA
philosophy for nonlinear generalization of PCA originated substantially faster
alternatives at the cost of introducing different constraints in the model.
Namely, the Principal Polynomial Analysis (PPA) [Laparra et al. IJNS 14], and
the Dimensionality Reduction via Regression (DRR) [Laparra et al. IEEE TGRS
15]. This report illustrates the reasons why we developed such family and is
the appropriate technical companion for the missing details in [Laparra et al.,
NeCo 12, Laparra and Malo, Front.Hum.Neuro. 15]. See also the data, code and
examples in the dedicated sites http://isp.uv.es/spca.html and
http://isp.uv.es/after effects.htmlComment: 17 pages, 14 figs., 72 ref
PWLS-ULTRA: An Efficient Clustering and Learning-Based Approach for Low-Dose 3D CT Image Reconstruction
The development of computed tomography (CT) image reconstruction methods that
significantly reduce patient radiation exposure while maintaining high image
quality is an important area of research in low-dose CT (LDCT) imaging. We
propose a new penalized weighted least squares (PWLS) reconstruction method
that exploits regularization based on an efficient Union of Learned TRAnsforms
(PWLS-ULTRA). The union of square transforms is pre-learned from numerous image
patches extracted from a dataset of CT images or volumes. The proposed
PWLS-based cost function is optimized by alternating between a CT image
reconstruction step, and a sparse coding and clustering step. The CT image
reconstruction step is accelerated by a relaxed linearized augmented Lagrangian
method with ordered-subsets that reduces the number of forward and back
projections. Simulations with 2-D and 3-D axial CT scans of the extended
cardiac-torso phantom and 3D helical chest and abdomen scans show that for both
normal-dose and low-dose levels, the proposed method significantly improves the
quality of reconstructed images compared to PWLS reconstruction with a
nonadaptive edge-preserving regularizer (PWLS-EP). PWLS with regularization
based on a union of learned transforms leads to better image reconstructions
than using a single learned square transform. We also incorporate patch-based
weights in PWLS-ULTRA that enhance image quality and help improve image
resolution uniformity. The proposed approach achieves comparable or better
image quality compared to learned overcomplete synthesis dictionaries, but
importantly, is much faster (computationally more efficient).Comment: Accepted to IEEE Transaction on Medical Imagin
Haar Wavelet Based Approach for Image Compression and Quality Assessment of Compressed Image
With the increasing growth of technology and the entrance into the digital
age, we have to handle a vast amount of information every time which often
presents difficulties. So, the digital information must be stored and retrieved
in an efficient and effective manner, in order for it to be put to practical
use. Wavelets provide a mathematical way of encoding information in such a way
that it is layered according to level of detail. This layering facilitates
approximations at various intermediate stages. These approximations can be
stored using a lot less space than the original data. Here a low complex 2D
image compression method using wavelets as the basis functions and the approach
to measure the quality of the compressed image are presented. The particular
wavelet chosen and used here is the simplest wavelet form namely the Haar
Wavelet. The 2D discret wavelet transform (DWT) has been applied and the detail
matrices from the information matrix of the image have been estimated. The
reconstructed image is synthesized using the estimated detail matrices and
information matrix provided by the Wavelet transform. The quality of the
compressed images has been evaluated using some factors like Compression Ratio
(CR), Peak Signal to Noise Ratio (PSNR), Mean Opinion Score (MOS), Picture
Quality Scale (PQS) etc.Comment: 8 pages. arXiv admin note: text overlap with standard references on
JPEG without attributio
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