1,068 research outputs found
High Dynamic Range Spectral Imaging Pipeline For Multispectral Filter Array Cameras
Spectral filter arrays imaging exhibits a strong similarity with color filter arrays. This permits
us to embed this technology in practical vision systems with little adaptation of the existing
solutions. In this communication, we define an imaging pipeline that permits high dynamic range
(HDR)-spectral imaging, which is extended from color filter arrays. We propose an implementation
of this pipeline on a prototype sensor and evaluate the quality of our implementation results on
real data with objective metrics and visual examples. We demonstrate that we reduce noise, and, in
particular we solve the problem of noise generated by the lack o
Trying to break new ground in aerial archaeology
Aerial reconnaissance continues to be a vital tool for landscape-oriented archaeological research. Although a variety of remote sensing platforms operate within the earth’s atmosphere, the majority of aerial archaeological information is still derived from oblique photographs collected during observer-directed reconnaissance flights, a prospection approach which has dominated archaeological aerial survey for the past century. The resulting highly biased imagery is generally catalogued in sub-optimal (spatial) databases, if at all, after which a small selection of images is orthorectified and interpreted. For decades, this has been the standard approach. Although many innovations, including digital cameras, inertial units, photogrammetry and computer vision algorithms, geographic(al) information systems and computing power have emerged, their potential has not yet been fully exploited in order to re-invent and highly optimise this crucial branch of landscape archaeology. The authors argue that a fundamental change is needed to transform the way aerial archaeologists approach data acquisition and image processing. By addressing the very core concepts of geographically biased aerial archaeological photographs and proposing new imaging technologies, data handling methods and processing procedures, this paper gives a personal opinion on how the methodological components of aerial archaeology, and specifically aerial archaeological photography, should evolve during the next decade if developing a more reliable record of our past is to be our central aim. In this paper, a possible practical solution is illustrated by outlining a turnkey aerial prospection system for total coverage survey together with a semi-automated back-end pipeline that takes care of photograph correction and image enhancement as well as the management and interpretative mapping of the resulting data products. In this way, the proposed system addresses one of many bias issues in archaeological research: the bias we impart to the visual record as a result of selective coverage. While the total coverage approach outlined here may not altogether eliminate survey bias, it can vastly increase the amount of useful information captured during a single reconnaissance flight while mitigating the discriminating effects of observer-based, on-the-fly target selection. Furthermore, the information contained in this paper should make it clear that with current technology it is feasible to do so. This can radically alter the basis for aerial prospection and move landscape archaeology forward, beyond the inherently biased patterns that are currently created by airborne archaeological prospection
Landsat Imagery from a CubeSat: Results and Operational Lessons from the R3 Satellite\u27s First 18 Months in Space
R3 is a 3-U CubeSat launched on a RocketLab Electron into a 500 km circular orbit at 85° inclination on December 16th, 2018. The spacecraft flies a multispectral sensor that takes data in the six Landsat visible and near infrared bands. The R3 sensor mates a custom refractive telescope with a Materion Precision Optics Landsat filter, and an ON Semiconductor fast-framing high-sensitivity Si CMOS array, to produce 50-km wide, 44-m resolution Landsat-like image strips. Data are taken in push-broom mode and are downlinked via a 100Mbps compact lasercom system. Frames are then co-added on the ground in time-delay-integration (TDI) fashion to increase signal-to-noise ratio and create multi-spectral Earth images from the compact sensor. The system is an engineering concept demonstration of a compact multispectral sensor in CubeSat form. We describe our ConOps, flight operations, sensor focus and alignment, initial imaging check out, and initial comparisons of R3 data to Landsat-8 imagery of the same Earth locations. RGB, color infrared, and normalized differential vegetation index (NDVI) products are compared between CUMULOS and Landsat-8. Results show good multispectral image quality from the CubeSat sensor, and illustrate the ability of R3 to detect vegetation and other features in a manner similar to Landsat, as well as the challenge in perfectly exposing all 6 VIS/NIR Landsat bands using our commercial 10-bit CMOS array. We also highlight the performance of the compact laser communications system which enabled the successful performance of this mission
Non-parametric Methods for Automatic Exposure Control, Radiometric Calibration and Dynamic Range Compression
Imaging systems are essential to a wide range of modern day
applications. With the continuous advancement in imaging systems,
there is an on-going need to adapt and improve the imaging
pipeline running inside the imaging systems.
In this thesis, methods are presented to improve the imaging
pipeline of digital cameras. Here we present three methods to
improve important phases of the imaging process, which are (i)
``Automatic exposure adjustment'' (ii) ``Radiometric
calibration'' (iii) ''High dynamic range compression''. These
contributions touch the initial, intermediate and final stages of
imaging pipeline of digital cameras.
For exposure control, we propose two methods. The first makes use
of CCD-based equations to formulate the exposure control problem.
To estimate the exposure time, an initial image was acquired for
each wavelength channel to which contrast adjustment techniques
were applied. This helps to recover a reference cumulative
distribution function of image brightness at each channel. The
second method proposed for automatic exposure control is an
iterative method applicable for a broad range of imaging systems.
It uses spectral sensitivity functions such as the photopic
response functions for the generation of a spectral power image
of the captured scene. A target image is then generated using the
spectral power image by applying histogram equalization. The
exposure time is hence calculated iteratively by minimizing the
squared difference between target and the current spectral power
image. Here we further analyze the method by performing its
stability and controllability analysis using a state space
representation used in control theory. The applicability of the
proposed method for exposure time calculation was shown on real
world scenes using cameras with varying architectures.
Radiometric calibration is the estimate of the non-linear mapping
of the input radiance map to the output brightness values. The
radiometric mapping is represented by the camera response
function with which the radiance map of the scene is estimated.
Our radiometric calibration method employs an L1 cost function by
taking advantage of Weisfeld optimization scheme. The proposed
calibration works with multiple input images of the scene with
varying exposure. It can also perform calibration using a single
input with few constraints. The proposed method outperforms,
quantitatively and qualitatively, various alternative methods
found in the literature of radiometric calibration.
Finally, to realistically represent the estimated radiance maps
on low dynamic range display (LDR) devices, we propose a method
for dynamic range compression. Radiance maps generally have
higher dynamic range (HDR) as compared to the widely used display
devices. Thus, for display purposes, dynamic range compression is
required on HDR images. Our proposed method generates few LDR
images from the HDR radiance map by clipping its values at
different exposures. Using contrast information of each LDR
image generated, the method uses an energy minimization approach
to estimate the probability map of each LDR image. These
probability maps are then used as label set to form final
compressed dynamic range image for the display device. The
results of our method were compared qualitatively and
quantitatively with those produced by widely cited and
professionally used methods
Physics vs. Learned Priors: Rethinking Camera and Algorithm Design for Task-Specific Imaging
Cameras were originally designed using physics-based heuristics to capture
aesthetic images. In recent years, there has been a transformation in camera
design from being purely physics-driven to increasingly data-driven and
task-specific. In this paper, we present a framework to understand the building
blocks of this nascent field of end-to-end design of camera hardware and
algorithms. As part of this framework, we show how methods that exploit both
physics and data have become prevalent in imaging and computer vision,
underscoring a key trend that will continue to dominate the future of
task-specific camera design. Finally, we share current barriers to progress in
end-to-end design, and hypothesize how these barriers can be overcome
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
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