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Fast Fourier Transforms have been used since the early 1960s as a method of processing signals. Since the 1990s wavelet transformation has also been routinely used as method for signal processing. Their limitations include the inability to detect contours, curves and directional information of a signal. In the past few years, new approaches such as multi-scale and multi-resolution transformations...
Super-resolution imaging is a technique that can be used to construct high-resolution imagery from low-resolution images. Low resolution images are often of the same scene, but contain aliasing and different subpixel shifts, which when combined can increase high frequency components while removing blurring. Superresolution reconstruction techniques include methods such as the Interpolation Approach...
In applications such as airborne imagery, target tracking, remote sensing, and medical imaging; it is helpful to have an image set where all of the images lie on one fixed coordinate system. However, frequently a set of images cannot be captured from a fixed perspective using the same sensor or different sensors at the same time. Image registration presents a solution by mapping points from one image...
Perfect image registration is an unsolved challenge that has been attempted in a multitude of different ways. This paper presents an approach for single-modal, multi-view registration of aerial imagery data that uses bandelets in the preprocessing phase to extract key geometric features and limit the amount of details in the image that must be considered during the feature matching process. Applying...
Image fusion is a process that allows for the synthesis of information from multiple source images into a single image. There are many applications for image fusion including night vision, medical imaging, and remote sensing. Over the many applications, numerous image fusion algorithms have been explored from averaging pixel intensities to fusion through multi-resolution decomposition transforms such...
Image fusion has many applications in which a reference image is not always available including image registration, medical imaging, and fusion between visible and infrared imagery. For these no-reference applications, it is important that there are objective and efficient methods for validating fusion performance, as subjective image fusion evaluation is time consuming and non-scalable. There have...
As digital media and internet use grow, imagery and video are prevalent in many areas of life. Many sensing methods such as Full Motion Video (FMV), Hyperspectral Imagery (HSI), and medical imaging have been developed to accumulate data for diagnostics. Analyzing imagery data to detect and identify specific objects is an essential phase of comprehending visual imagery. Content-based image retrieval...
Over the past decade the digital camera has become widely available in many devices such as cell phones, computers, etc. Therefore, the perceptual quality of digital images is an important and necessary requirement to evaluate digital images. To improve the quality of images captured with camera, we must identify and measure the artifacts that cause blur within the images. Blur is mainly caused by...
In the past decade, the number and popularity of digital cameras has increased many fold, increasing the demand for a blur metric and quality assessment techniques to evaluate digital images. There is still no widely accepted industry standard by which an image's blur content may be assessed so it is imperative that better, more reliable, no-reference metrics be created to fill this gap. In this paper,...
In recent years, digital cameras have been widely used for image capturing. These devices are equipped in cell phones, laptops, tablets, webcams, etc. Image quality is an important characteristic for any digital image analysis. Historically, techniques to assess image quality for these mobile products require a standard image to be used as a reference image. In this case, Root Mean Square Error and...
For the past two decades, the Discrete Wavelet Transformation (DWT) has been successfully applied to many fields. For image processing applications, the DWT can produce non-redundant representations of an input image with greater performance than other wavelet methods. Further, the DWT provides a better spatial and spectral localization of image representation, capable of revealing smaller changes,...
Fast Fourier Transforms (FFTs) and Discrete Wavelet Transformations (DWTs) have been routinely used as methods of denoising signals. DWT limitations include the inability to detect contours, curves and directional information of multi-dimensional signals. In the past decade, two new approaches have surfaced: curvelets, developed by Candès; and contourlets, developed by Do et al. The typical applications...
Content Based Image Retrieval (CBIR) is a technical area focused on answering “Who, What, Where and When,” questions associated with the imagery. A multi-scale feature extraction scheme based on wavelet and Contourlet transforms is proposed to reliably extract objects in images. First, we explore Contourlet transformation in association with Pulse Coupled Neural Network (PCNN) while the second technique...
In this paper, we present a robust technique for predicting anomalies in the near future of an observed signal. First, wavelet de-noising is applied to the signal. Next, peak-finding algorithms search for smaller anomalies that appear frequently throughout the signal. Then the data from the peak-finding algorithm is fed into a feed-forward neural which predicts the likelihood of an anomalous event...
In this paper, we introduce a technique for predicting anomalies in a signal by observing relationships between multiple meaningful transformations of the signal called perspectives. In particular, we use the Fourier transform to provide a holistic view of the frequencies present in a signal, along with a wavelet denoised signal that is filtered to locate anomalous peaks. Then we input these perspectives...
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