Leslie N. Smith

Improving Dictionary Learning: Multiple Dictionary Updates and Coefficient Reuse

In this letter, we propose two improvements of the MOD and K-SVD dictionary learning algorithms, by modifying the two main parts of these algorithms-the dictionary update and the sparse coding stages. Our first contribution is a different dictionary-update stage that aims at finding both the dictionary and the representations while keeping the supports intact. The second contribution suggests to leverage the known representations from the previous sparse-coding in the quest for the updated representations. We demonstrate these two ideas in practice and show how they lead to faster training and better quality outcome.

A standard data set for performance analysis of advanced IR image processing techniques

Modern IR cameras are increasingly equipped with built-in advanced (often non-linear) image and signal processing algorithms (like fusion, super-resolution, dynamic range compression etc.) which can tremendously influence performance characteristics. Traditional approaches to range performance modeling are of limited use for these types of equipment. Several groups have tried to overcome this problem by producing a variety of imagery to assess the impact of advanced signal and image processing. Mostly, this data was taken from classified targets and/ or using classified imager and is thus not suitable for comparison studies between different groups from government, industry and universities. To ameliorate this situation, NATO SET-140 has undertaken a systematic measurement campaign at the DGA technical proving ground in Angers, France, to produce an openly distributable data set suitable for the assessment of fusion, super-resolution, local contrast enhancement, dynamic range compression and image-based NUC algorithm performance. The imagery was recorded for different target / background settings, camera and/or object movements and temperature contrasts. MWIR, LWIR and Dual-band cameras were used for recording and were also thoroughly characterized in the lab. We present a selection of the data set together with examples of their use in the assessment of super-resolution and contrast enhancement algorithms.

How to find real-world applications of compressive sensing

The potential of compressive sensing (CS) has spurred great interest in the research community and is a fast growing area of research. However, research translating CS theory into practical hardware and demonstrating clear and significant benefits with this hardware over current, conventional imaging techniques has been limited. This article helps researchers to find those niche applications where the CS approach provide substantial gain over conventional approaches by articulating guidelines for finding these niche CS applications. Furthermore, in this paper we utilized these guidelines to find one such new application for CS; sea skimming missile detection. As a proof of concept, it is demonstrated that a simplified CS missile detection architecture and algorithm provides comparable results to the conventional imaging approach but using a smaller FPA.

Improving sparse representation algorithms for maritime video processing

We present several improvements to published algorithms for sparse image modeling with the goal of improving processing of imagery of small watercraft in littoral environments. The first improvement is to the K-SVD algorithm for training over-complete dictionaries, which are used in sparse representations. It is shown that the training converges significantly faster by incorporating multiple dictionary (i.e., codebook) update stages in each training iteration. The paper also provides several useful and practical lessons learned from our experience with sparse representations. Results of three applications of sparse representation are presented and compared to the state-of-the-art methods; image compression, image denoising, and super-resolution.

A new blur kernel estimator and comparisons to state-of-the-art

This paper presents a simple, fast, and robust method to estimate the blur kernel model, support size, and its parameters directly from a blurry image. The edge profile method eliminates the need for searching the parameter space. In addition, this edge profile method is highly local and can provide a measure of asymmetry and spatial variation, which allows one to make an informed decision on whether to use a symmetric or asymmetric, spatially varying or non-varying blur kernel over an image. Furthermore, the edge profile method is relatively robust to image noise. We show how to utilize the concepts behind the statistical tools for fitting data distributions to analytically obtain an estimate of the blur kernel that incorporates blur from all sources, including factors inherent in the imaging system. Comparisons are presented of the deblurring results from this method to current common practices for real-world (VNIR, SWIR, MWIR, and active IR) imagery. The effect of image noise on this method is compared to the effect of noise on other methods.

Applications of Super-Resolution and Deblurring to Practical Sensors

In image formation and recording process, there are many factors that affect sensor performance and image quality that result in loss of high-frequency information. Two of these common factors are undersampled sensors and sensors blurring function. Two image processing algorithms, including super-resolution image reconstruction and deblur filtering, have been developed based on characterizing the sources of image degradation from image formation and recording process. In this paper, we discuss the applications of these two algorithms to three practical thermal imaging systems. First, super-resolution and deblurring are applied to a longwave uncooled sensor in a missile seeker. Target resolution is improved in the flight phase of the seeker operation. Second, these two algorithms are applied to a midwave target acquisition sensor for use in long-range target identification. Third, the two algorithms are applied to a naval midwave distributed aperture sensor (DAS) for infrared search and track (IRST) system that is dual use in missile detection and force protection/anti-terrorism applications. In this case, super-resolution and deblurring are used to improve the resolution of on-deck activity discrimination.

An approach to explainable deep learning using fuzzy inference

Deep Learning has proven to be an effective method for making highly accurate predictions from complex data sources. Convolutional neural networks continue to dominate image classification problems and recursive neural networks have proven their utility in caption generation and language translations. While these approaches are powerful, they do not offer explanation for how the output is generated. Without understanding how deep learning arrives at a solution there is no guarantee that these networks will transition from controlled laboratory environments to fieldable systems. This paper presents an approach for incorporating such rule based methodology into neural networks by embedding fuzzy inference systems into deep learning networks.

Discriminative dictionaries for automated target recognition

We present an approach for discriminating among dierent classes of imagery in a scene. Our intended application is the detection of small watercraft in a littoral environment where both targets and land- and sea-based clutter are present. The approach works by training dierent overcomplete dictionaries to model the dierent image classes. The likelihood ratio obtained by applying each model to the unknown image is then used as the discriminating test statistic. We rst demonstrate the approach on an illustrative test problem and then apply the algorithm to short-wave infrared imagery with known targets.