Enhancing Image Quality: A Comparative Study of Spatial, Frequency Domain, and Deep Learning Methods

Image restoration and noise reduction methods have been created to restore deteriorated images and improve their quality. These methods have garnered substantial significance in recent times, mainly due to the growing utilization of digital imaging across diverse domains, including but not limited to medical imaging, surveillance, satellite imaging, and numerous others. In this paper, we conduct a comparative analysis of three distinct approaches to image restoration: the spatial method, the frequency domain method, and the deep learning method. The study was conducted on a dataset of 10,000 images, and the performance of each method was evaluated using the accuracy and loss metrics. The results show that the deep learning method outperformed the other two methods, achieving a validation accuracy of 72.68% after 10 epochs. The spatial method had the lowest accuracy of the three, achieving a validation accuracy of 69.98% after 10 epochs. The FFT frequency domain method had a validation accuracy of 52.87% after 10 epochs, significantly lower than the other two methods. The study demonstrates that deep learning is a promising approach for image classification tasks and outperforms traditional methods such as spatial and frequency domain techniques

Solving Inverse Problems with Piecewise Linear Estimators: From Gaussian Mixture Models to Structured Sparsity

A general framework for solving image inverse problems is introduced in this paper. The approach is based on Gaussian mixture models, estimated via a computationally efficient MAP-EM algorithm. A dual mathematical interpretation of the proposed framework with structured sparse estimation is described, which shows that the resulting piecewise linear estimate stabilizes the estimation when compared to traditional sparse inverse problem techniques. This interpretation also suggests an effective dictionary motivated initialization for the MAP-EM algorithm. We demonstrate that in a number of image inverse problems, including inpainting, zooming, and deblurring, the same algorithm produces either equal, often significantly better, or very small margin worse results than the best published ones, at a lower computational cost.Comment: 30 page

Methods for improved mapping of brain lesion connectivity

Recent advances over the past two decades in neuroimaging methods have enabled us to map the connectivity of the brain. In parallel, pathophysiological models of brain disease have shifted from an emphasis on understanding pathology in specific brain regions to characterizing disruptions to interconnected neural networks. Nevertheless, these recent methods for mapping brain connectivity are still under development. Every step of the mapping process becomes a potential source for additional error due to noise or artifacts that could impact final analyses. Segmentation, parcellation, registration, and tractography are some of the steps where this occurs. Moreover, mapping the connectivity in a brain lesion is even more susceptible to errors in these steps. In this body of work, I describe multiple new methods for improving the accuracy of mapping lesion connectivity by reducing errors at the tractography stage which is the most error prone stage. First, we develop an approach for directly normalizing streamlines into a template space that avoids performing tractography in the normalized template space, reducing the error of connectomes constructed in the template space with respect to the ground truth native space connectome. Second, we develop a rapid approach for performing shortest path tractography and constructing shortest path probability weighted connectomes which increases the connection specificity relative to local streamline tracking approaches. We then demonstrate how our shortest path tractography approach can be used construct a disconnectome, a connectivity map of the proportion of connections lost due to intersecting a lesion. We then develop a fast, greedy graph-theoretic algorithm that extracts the maximally disconnected subgraph containing brain regions with the greatest shared loss of connectivity. Finally, we demonstrate how combining methods from diffusion based image inpainting and optimal estimation can be used to restore or inpaint corrupted fiber diffusion models in lesioned white matter tissue, enabling tractography and the study of lesion connectivity and modeling of microstructural measures in the patient’s native space

Development, design, fabrication and evaluation of a real-time video compression system

This is the final report on the work done by David Hein at the NASA-AMES Research Center. The main emphasis is on the work done on the Conditional Replenishment Emulator. The progress for May and a description of the emulator are given. Brief summaries of the work that was done in the other areas covered by the contract over the entire contract period are also provided

Image-Adaptive GAN based Reconstruction

In the recent years, there has been a significant improvement in the quality of samples produced by (deep) generative models such as variational auto-encoders and generative adversarial networks. However, the representation capabilities of these methods still do not capture the full distribution for complex classes of images, such as human faces. This deficiency has been clearly observed in previous works that use pre-trained generative models to solve imaging inverse problems. In this paper, we suggest to mitigate the limited representation capabilities of generators by making them image-adaptive and enforcing compliance of the restoration with the observations via back-projections. We empirically demonstrate the advantages of our proposed approach for image super-resolution and compressed sensing.Comment: Accepted to AAAI 2020. Code available at https://github.com/shadyabh/IAGA

The Reddest Quasars

In a survey of quasar candidates selected by matching the FIRST and 2MASS catalogs, we have found two extraordinarily red quasars. FIRST J013435.7-093102 is a 1 Jy source at z=2.216 and has B-K > 10, while FIRST J073820.1+275045 is a 2.5 mJy source at z=1.985 with B-K = 8.4. FIRST J073820.1+275045 has strong absorption lines of MgII and CIV in the rest frame of the quasar and is highly polarized in the rest frame ultraviolet, strongly favoring the interpretation that its red spectral energy distribution is caused by dust reddening local to the quasar. FIRST J073820.1+275045 is thus one of the few low radio-luminosity, highly dust-reddened quasars known. The available observational evidence for FIRST J013435.7-093102 leads us to conclude that it too is reddened by dust. We show that FIRST J013435.7-093102 is gravitationally lensed, increasing the number of known lensed, extremely dust-reddened quasars to at least three, including MG0414-0534 and PKS1830-211. We discuss the implications of whether these objects are reddened by dust in the host or lensing galaxies. If reddened by their local environment, then we estimate that between 10 and 20% of the radio-loud quasar population is reddened by dust in the host galaxy. The discovery of FIRST J073820.1+275045 and objects now emerging from X-ray surveys suggests the existence of an analogous radio-quiet red quasar population. Such objects will be entirely missed by standard radio or optical quasar surveys. If dust in the lensing galaxies is primarily responsible for the extreme redness of the lensed quasars, then an untold number of gravitationally lensed quasars are being overlooked.Comment: AASTEX 24 pp., 7 figs; accepted by ApJ. See also the preprint astro-ph/0107435 by Winn et al., who independently discovered that J013435.7-093102 is gravitationally lense