Multi-waveband infrared (IR) sensors capture more spectral information of atmospheric particles and may provide better penetration thru dust under dynamically changing conditions. Therefore, enhancing the visibility of multi-waveband infrared images obtained in degraded visual environment (DVE) is an important way to improve the perception of the environment under DVE conditions. In this paper, we present a system to enhance visibility in DVE conditions by modifying the wavelet coefficients of multi-waveband IR images. In the proposed system, input multi-waveband IR images are transferred into the wavelet domain using an integer lifting wavelet transformation. The low-frequency wavelet coefficients in each waveband are independently modified by an adaptive histogram equalization technique for improving the contrast of the images. To process high-frequency wavelet coefficients, a joint edge-mapping filter is applied to the multi-waveband high-frequency wavelet coefficients to find an edge map for each subband of wavelet coefficients; then a nonlinear filter is used to remove noise and enhance edge coefficients. Finally, the inverse lifting wavelet transformation is applied to the modified multi-waveband wavelet coefficients to obtain enhanced multiwaveband IR images. We tested the proposed system with degraded multi-waveband IR images obtained from a helicopter landing in brownout conditions. Our experimental results show that the proposed system is effective for enhancing the visibility of multi-waveband IR images under DVE conditions.
Enhancing the visibility of infrared images obtained in a degraded visibility environment is very important for many applications such as surveillance, visual navigation in bad weather, and helicopter landing in brownout conditions. In this paper, we present an IR image visibility enhancement system based on adaptively modifying the wavelet coefficients of the images. In our proposed system, input images are first filtered by a histogram-based dynamic range filter in order to remove sensor noise and convert the input images into 8-bit dynamic range for efficient processing and display. By utilizing a wavelet transformation, we modify the image intensity distribution and enhance image edges simultaneously. In the wavelet domain, low frequency wavelet coefficients contain original image intensity distribution while high frequency wavelet coefficients contain edge information for the original images. To modify the image intensity distribution, an adaptive histogram equalization technique is applied to the low frequency wavelet coefficients while to enhance image edges, an adaptive edge enhancement technique is applied to the high frequency wavelet coefficients. An inverse wavelet transformation is applied to the modified wavelet coefficients to obtain intensity images with enhanced visibility. Finally, a Gaussian filter is used to remove blocking artifacts introduced by the adaptive techniques. Since wavelet transformation uses down-sampling to obtain low frequency wavelet coefficients, histogram equalization of low-frequency coefficients is computationally more efficient than histogram equalization of the original images. We tested the proposed system with degraded IR images obtained from a helicopter landing in brownout conditions. Our experimental results show that the proposed system is effective for enhancing the visibility of degraded IR images.
Proc. SPIE. 8404, Wireless Sensing, Localization, and Processing VII
KEYWORDS: Digital signal processing, Digital image processing, Image processing, Digital filtering, Receivers, Linear filtering, Signal processing, Analog electronics, Electronic filtering, Frequency conversion
Software defined radio (SDR) hardware platform is in high demand for ultra-wideband digital EW receiver to carry
out different mission requirements. Due to the limitations of current Analog-to-Digital conversion (ADC)
techniques, the ideal receiver structure of SDR, with digital RF frequency conversion, cannot be achieved. In this
article, a new channelization technique called ADC polyphase fast Fourier transformation (ADC PFFT) filter bank
channelization is demonstrated. The key concept is to separate the speed at which the two functional units of an
ADC - the sample and hold and the quantizer - operate. The sample and hold unit operates at the sampling
frequency fs and the quantizer (the speed limiting factor in ADCs) can operate at a much slower rate, fs/M, where M
is the decimation factor for digital filter bank. By integrated this ADC PFFT technique in ultra-wideband digital
channelized EW receivers, directly digital RF down conversion can be achieved. With the ADC PFFT
channelization for RF down conversion and polyphase FFT channelization for IF down conversion, 2-18 GHz
frequency coverage can be accomplished in such ultra-wideband digital channelized EW receivers without the
requirement of EW receivers being time-shared among outputs from many subbands due to bandwidth limitation in
digital IF channelized EW receivers. Because the frequency down conversion from RF to BB are all processed
digitally, issues such as image rejection and I/Q imbalance due to analog mixing will be eliminated in the ultrawideband
digital channelized EW receivers.
We describe a method for searching videos in large video databases based on the activity contents present in the videos.
Being able to search videos based on the contents (such as human activities) has many applications such as security,
surveillance, and other commercial applications such as on-line video search. Conventional video content-based retrieval
(CBR) systems are either feature based or semantics based, with the former trying to model the dynamics video contents
using the statistics of image features, and the latter relying on automated scene understanding of the video contents.
Neither approach has been successful. Our approach is inspired by the success of visual vocabulary of "Video Google"
by Sivic and Zisserman, and the work of Nister and Stewenius who showed that building a visual vocabulary tree can
improve the performance in both scalability and retrieval accuracy for 2-D images. We apply visual vocabulary and
vocabulary tree approach to spatio-temporal video descriptors for video indexing, and take advantage of the
discrimination power of these descriptors as well as the scalability of vocabulary tree for indexing. Furthermore, this
approach does not rely on any model-based activity recognition. In fact, training of the vocabulary tree is done off-line
using unlabeled data with unsupervised learning. Therefore the approach is widely applicable. Experimental results using
standard human activity recognition videos will be presented that demonstrate the feasibility of this approach.
Computing the similarity between videos is a challenging problem in many applications of video processing such as
video retrieval and video-based action recognition. The main difficulty in evaluating similarity between videos is the
lack of an effective distance measure. In this paper, we propose a clustering based distance measure to solve this
problem. In our approach, a video sequence is represented by a set of spatiotemporal descriptors. Therefore computing
the similarity between two video sequences can be achieved by estimating the distance between two sets of descriptors
extracted from the videos. To compute the distance measure, we use clustering to obtain distributions and "constrained
distributions" of the two sets of descriptors, and use Kullback-Leibler (KL) divergence  with the distributions. We
apply our distance measure to the problems of distinguishing different human actions and content-based video retrieval.
Our experimental results show that the proposed distance measure is an effective metric for these applications.
The theory of compressed sensing (CS) has shown that compressible signals can be accurately reconstructed from a very
small set of randomly projected measurements. Sparse representation of the signals plays an important role in the signal
reconstruction of compressed sensing. In this paper, we propose to use signal modulation information to obtain a better
sparse representation for communication signals in compressed sensing. In our approach, a tree-structured modulation
classification system is used to classify five types of signal modulations: Amplitude Modulation (AM), Frequency
Modulation (FM), Amplitude Shift Keying (ASK), Frequency Shift Keying (FSK) and Phase Shift Keying (PSK). The
tree-structured classification system uses four signal features to classify the five modulation types, and all features are
computable in the analog domain. To select a sparse transformation for the input signal, we propose a pre-trained
Karhunen-Loeve transform (KLT) based CS, in which a set of KLT transformation matrices is obtained by an offline
learning process for all modulation types. In an online real-time process, the modulation information of the input signal
is classified and then used to select one of the pre-trained KLT matrices for providing a better sparse representation of
the signal for CS-based signal reconstruction. Our experimental results show that our modulation classification technique
is effective in identifying the five modulation types of noisy input signals, and our KLT based CS reconstruction has
much better performances than Fourier and wavelet packet based CS for the communication signals we tested.
A stereo sequence coding algorithm is presented and evaluated in this paper. The left image stream is coded independently by an MPEG-type coding scheme. In the right image stream, only reference frames are coded by the subspace projection technique. The rest of frames in the right image stream are not coded and transmitted at the encoder; they are reconstructed from reference frames at the decoder. A frame estimation and interpolation technique is developed to exploit the great redundancy within stereo sequences to reconstruct some frames of the right image stream at the decoder. In the reconstructed frames, uncovered occlusions regions are filled by a disparity-based techniques. The intra coding and residual coding are based on subband coding techniques. The motion and disparity fields are estimated by block-based matching with a multiresolution structure, and coded by an entropy coding technique. Two stereo sequences are used to test our coding algorithm. Experimental results show that the frame estimation and interpolation technique works well perceptively and our stereo sequence coding scheme is effective to achieve high compression ratio.