144 research outputs found
Adaptive frequency prior for frequency selective reconstruction of images from non-regular subsampling
Image signals typically are defined on a rectangular two-dimensional grid.
However, there exist scenarios where this is not fulfilled and where the image
information only is available for a non-regular subset of pixel position. For
processing, transmitting or displaying such an image signal, a re-sampling to a
regular grid is required. Recently, Frequency Selective Reconstruction (FSR)
has been proposed as a very effective sparsity-based algorithm for solving this
under-determined problem. For this, FSR iteratively generates a model of the
signal in the Fourier-domain. In this context, a fixed frequency prior inspired
by the optical transfer function is used for favoring low-frequency content.
However, this fixed prior is often too strict and may lead to a reduced
reconstruction quality. To resolve this weakness, this paper proposes an
adaptive frequency prior which takes the local density of the available samples
into account. The proposed adaptive prior allows for a very high reconstruction
quality, yielding gains of up to 0.6 dB PSNR over the fixed prior,
independently of the density of the available samples. Compared to other
state-of-the-art algorithms, visually noticeable gains of several dB are
possible
Multiple Selection Extrapolation for Improved Spatial Error Concealment
This contribution introduces a novel signal extrapolation algorithm and its
application to image error concealment. The signal extrapolation is carried out
by iteratively generating a model of the signal suffering from distortion.
Thereby, the model results from a weighted superposition of two-dimensional
basis functions whereas in every iteration step a set of these is selected and
the approximation residual is projected onto the subspace they span. The
algorithm is an improvement to the Frequency Selective Extrapolation that has
proven to be an effective method for concealing lost or distorted image
regions. Compared to this algorithm, the novel algorithm is able to reduce the
processing time by a factor larger than three, by still preserving the very
high extrapolation quality
On Versatile Video Coding at UHD with Machine-Learning-Based Super-Resolution
Coding 4K data has become of vital interest in recent years, since the amount
of 4K data is significantly increasing. We propose a coding chain with spatial
down- and upscaling that combines the next-generation VVC codec with machine
learning based single image super-resolution algorithms for 4K. The
investigated coding chain, which spatially downscales the 4K data before
coding, shows superior quality than the conventional VVC reference software for
low bitrate scenarios. Throughout several tests, we find that up to 12 % and 18
% Bjontegaard delta rate gains can be achieved on average when coding 4K
sequences with VVC and QP values above 34 and 42, respectively. Additionally,
the investigated scenario with up- and downscaling helps to reduce the loss of
details and compression artifacts, as it is shown in a visual example.Comment: Originally published as conference paper at QoMEX 202
Hyperspectral Image Reconstruction from Multispectral Images Using Non-Local Filtering
Using light spectra is an essential element in many applications, for
example, in material classification. Often this information is acquired by
using a hyperspectral camera. Unfortunately, these cameras have some major
disadvantages like not being able to record videos. Therefore, multispectral
cameras with wide-band filters are used, which are much cheaper and are often
able to capture videos. However, using multispectral cameras requires an
additional reconstruction step to yield spectral information. Usually, this
reconstruction step has to be done in the presence of imaging noise, which
degrades the reconstructed spectra severely. Typically, same or similar pixels
are found across the image with the advantage of having independent noise. In
contrast to state-of-the-art spectral reconstruction methods which only exploit
neighboring pixels by block-based processing, this paper introduces non-local
filtering in spectral reconstruction. First, a block-matching procedure finds
similar non-local multispectral blocks. Thereafter, the hyperspectral pixels
are reconstructed by filtering the matched multispectral pixels collaboratively
using a reconstruction Wiener filter. The proposed novel procedure even works
under very strong noise. The method is able to lower the spectral angle up to
18% and increase the peak signal-to-noise-ratio up to 1.1dB in noisy scenarios
compared to state-of-the-art methods. Moreover, the visual results are much
more appealing
Jointly Resampling and Reconstructing Corrupted Images for Image Classification using Frequency-Selective Mesh-to-Grid Resampling
Neural networks became the standard technique for image classification
throughout the last years. They are extracting image features from a large
number of images in a training phase. In a following test phase, the network is
applied to the problem it was trained for and its performance is measured. In
this paper, we focus on image classification. The amount of visual data that is
interpreted by neural networks grows with the increasing usage of neural
networks. Mostly, the visual data is transmitted from the application side to a
central server where the interpretation is conducted. If the transmission is
disturbed, losses occur in the transmitted images. These losses have to be
reconstructed using postprocessing. In this paper, we incorporate the widely
applied bilinear and bicubic interpolation and the high-quality reconstruction
Frequency-Selective Reconstruction (FSR) for the reconstruction of corrupted
images. However, we propose to use Frequency-Selective Mesh-to-Grid Resampling
(FSMR) for the joint reconstruction and resizing of corrupted images. The
performance in terms of classification accuracy of EfficientNetB0, DenseNet121,
DenseNet201, ResNet50 and ResNet152 is examined. Results show that the
reconstruction with FSMR leads to the highest classification accuracy for most
networks. Average improvements of up to 6.7 percentage points are possible for
DenseNet121.Comment: IEEE 24th International Workshop on Multimedia Signal Processing 202
Optimal Filter Selection for Multispectral Object Classification Using Fast Binary Search
When designing multispectral imaging systems for classifying different
spectra it is necessary to choose a small number of filters from a set with
several hundred different ones. Tackling this problem by full search leads to a
tremendous number of possibilities to check and is NP-hard. In this paper we
introduce a novel fast binary search for optimal filter selection that
guarantees a minimum distance metric between the different spectra to classify.
In our experiments, this procedure reaches the same optimal solution as with
full search at much lower complexity. The desired number of filters influences
the full search in factorial order while the fast binary search stays constant.
Thus, fast binary search allows to find the optimal solution of all
combinations in an adequate amount of time and avoids prevailing heuristics.
Moreover, our fast binary search algorithm outperforms other filter selection
techniques in terms of misclassified spectra in a real-world classification
problem
Frequency-Selective Geometry Upsampling of Point Clouds
The demand for high-resolution point clouds has increased throughout the last
years. However, capturing high-resolution point clouds is expensive and thus,
frequently replaced by upsampling of low-resolution data. Most state-of-the-art
methods are either restricted to a rastered grid, incorporate normal vectors,
or are trained for a single use case. We propose to use the frequency
selectivity principle, where a frequency model is estimated locally that
approximates the surface of the point cloud. Then, additional points are
inserted into the approximated surface. Our novel frequency-selective geometry
upsampling shows superior results in terms of subjective as well as objective
quality compared to state-of-the-art methods for scaling factors of 2 and 4. On
average, our proposed method shows a 4.4 times smaller point-to-point error
than the second best state-of-the-art PU-Net for a scale factor of 4.Comment: 5 pages, 3 figures, International Conference on Image Processing
(ICIP) 202
Conditional Residual Coding: A Remedy for Bottleneck Problems in Conditional Inter Frame Coding
Conditional coding is a new video coding paradigm enabled by
neural-network-based compression. It can be shown that conditional coding is in
theory better than the traditional residual coding, which is widely used in
video compression standards like HEVC or VVC. However, on closer inspection, it
becomes clear that conditional coders can suffer from information bottlenecks
in the prediction path, i.e., that due to the data processing inequality not
all information from the prediction signal can be passed to the reconstructed
signal, thereby impairing the coder performance. In this paper we propose the
conditional residual coding concept, which we derive from information
theoretical properties of the conditional coder. This coder significantly
reduces the influence of bottlenecks, while maintaining the theoretical
performance of the conditional coder. We provide a theoretical analysis of the
coding paradigm and demonstrate the performance of the conditional residual
coder in a practical example. We show that conditional residual coders
alleviate the disadvantages of conditional coders while being able to maintain
their advantages over residual coders. In the spectrum of residual and
conditional coding, we can therefore consider them as ``the best from both
worlds''.Comment: 12 pages, 8 figure
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