This paper develops distortion metrics for compressed synthetic aperture radar (SAR) imagery from change detection test statistics. These metrics are used to predict lossy image compression's impact on change detection performance. The metrics do not require the intended change detection comparison image to provide these benefits. An SAR compression system leveraging the distortion metrics is proposed. The system generates a bad-pixel mask highlighting potential false alarms that are generated due to compression and are subsequently discarded in the change detection process. The proposed system's performance is demonstrated through noncoherent change detection analysis after JPEG2000 and JPEG image compression. Similarly, a coherent change detection system is evaluated after JPEG2000 image compression. For noncoherent change detection at large compression ratios (CRs) using JPEG2000, the proposed system provides a 33% reduction in false alarms at a 0.1 probability of detection as well as the ability to maintain near-distortionless false alarm rates across a wide range of CRs. At a 0.1 probability of detection for coherent change detection, the system provides a 37% reduction in false alarms at modest CRs. The coherent change detection system is also demonstrated to maintain low false alarm rates across a range of CRs.

In this paper a Dual Ratio change detection method is proposed. This method involves taking two ratios for both real color and false color imagery in order to maximize detected changes and minimize false alarms. The proposed Dual Ratio method outperforms other methods tested in terms of Area Under the Curve (AUC) performance by an average of 18 percent at low false alarm rates and an average of 10.5 percent across the entire false alarm rate sweep.

In this paper, an integer-based Cohen-Daubechies-Feauvea (CDF) 9/7 wavelet transform as well as an integer quantization method used in a lossy JPEG2000 compression engine is presented. The conjunction of both an integer transform and quantization step allows for a complete integer computation of lossy JPEG2000 compression. The lossy method of compression utilizes the CDF 9/7 wavelet filter, which transforms integer input pixel values into floating-point wavelet coefficients that are then quantized back into integers and finally compressed by the embedded block coding with optimal truncation tier-1 encoder. Integer computation of JPEG2000 allows a reduction in computational complexity of the wavelet transform as well as ease of implementation in embedded systems for higher computational performance. The results of the integer computation show an equivalent rate/distortion curve to the JasPer JPEG2000 compression engine, as well as a 30% reduction in computation time of the wavelet transform and a 56% reduction in computation time of the quantization processing on an average.

A combined spatial- and temporal-domain wavelet shrinkage algorithm for video denoising is presented in this paper. The spatial-domain denoising technique is a selective wavelet shrinkage method which uses a two-threshold criteria to exploit the geometry of the wavelet subbands of each video frame, and each frame of the image sequence is spatially denoised independently of one another. The temporal-domain denoising technique is a selective wavelet shrinkage method which estimates the level of noise corruption as well as the amount of motion in the image sequence. The amount of noise is estimated to determine how much filtering is needed in the temporal-domain, and the amount of motion is taken into consideration to determine the degree of similarity between consecutive frames. The similarity affects how much noise removal is possible using temporal-domain processing. Using motion and noise level estimates, a video denoising technique is established which is robust to various levels of noise corruption and various levels of motion.

This investigation uses a genetic algorithm to optimize coefficient sets describing inverse transforms that significantly reduce mean squared error of reconstructed images. Quantization error introduced during image compression and reconstruction is one of the worst noise sources, due to the fact that information is always permanently lost during the transformation process. Our approach establishes an adaptive filtering methodology for evolving transforms that outperform discrete wavelet inverse transforms for the reconstruction of images subjected to quantization error. Inverse transforms evolved against a single training image consistently generalize to exhibit superior performance against other images from the test set.

A selective wavelet shrinkage algorithm for digital image denoising is presented. The performance of this method is an improvement upon other methods proposed in the literature and is algorithmically simple for large computational savings. The improved performance and computational speed of the proposed wavelet shrinkage algorithm is presented and experimentally compared with established methods. The denoising method incorporated in the proposed algorithm involves a two-threshold validation process for real-time selection of wavelet coefficients. The two-threshold criteria selects wavelet coefficients based on their absolute value, spatial regularity, and regularity across multiresolution scales. The proposed algorithm takes image features into consideration in the selection process. Statistically, most images have regular features resulting in connected subband coefficients. Therefore, the resulting subbands of wavelet transformed images in large part do not contain isolated coefficients. In the proposed algorithm, coefficients are selected due to their magnitude, and only a subset of those selected coefficients which exhibit a spatially regular behavior remain for image reconstruction. Therefore, two thresholds are used in the coefficient selection process. The first threshold is used to distinguish coefficients of large magnitude and the second is used to distinguish coefficients of spatial regularity. The performance of the proposed wavelet denoising technique is an improvement upon several other established wavelet denoising techniques, as well as being computationally efficient to facilitate real-time image-processing applications.