A multiresolution framework for local similarity based image denoising

In this paper, we present a generic framework for denoising of images corrupted with additive white Gaussian noise based on the idea of regional similarity. The proposed framework employs a similarity function using the distance between pixels in a multidimensional feature space, whereby multiple feature maps describing various local regional characteristics can be utilized, giving higher weight to pixels having similar regional characteristics. An extension of the proposed framework into a multiresolution setting using wavelets and scale space is presented. It is shown that the resulting multiresolution multilateral (MRM) filtering algorithm not only eliminates the coarse-grain noise but can also faithfully reconstruct anisotropic features, particularly in the presence of high levels of noise

Graph Spectral Image Processing

Recent advent of graph signal processing (GSP) has spurred intensive studies of signals that live naturally on irregular data kernels described by graphs (e.g., social networks, wireless sensor networks). Though a digital image contains pixels that reside on a regularly sampled 2D grid, if one can design an appropriate underlying graph connecting pixels with weights that reflect the image structure, then one can interpret the image (or image patch) as a signal on a graph, and apply GSP tools for processing and analysis of the signal in graph spectral domain. In this article, we overview recent graph spectral techniques in GSP specifically for image / video processing. The topics covered include image compression, image restoration, image filtering and image segmentation

Automatic Denoising and Unmixing in Hyperspectral Image Processing

This thesis addresses two important aspects in hyperspectral image processing: automatic hyperspectral image denoising and unmixing. The first part of this thesis is devoted to a novel automatic optimized vector bilateral filter denoising algorithm, while the remainder concerns nonnegative matrix factorization with deterministic annealing for unsupervised unmixing in remote sensing hyperspectral images. The need for automatic hyperspectral image processing has been promoted by the development of potent hyperspectral systems, with hundreds of narrow contiguous bands, spanning the visible to the long wave infrared range of the electromagnetic spectrum. Due to the large volume of raw data generated by such sensors, automatic processing in the hyperspectral images processing chain is preferred to minimize human workload and achieve optimal result. Two of the mostly researched processing for such automatic effort are: hyperspectral image denoising, which is an important preprocessing step for almost all remote sensing tasks, and unsupervised unmixing, which decomposes the pixel spectra into a collection of endmember spectral signatures and their corresponding abundance fractions. Two new methodologies are introduced in this thesis to tackle the automatic processing problems described above. Vector bilateral filtering has been shown to provide good tradeoff between noise removal and edge degradation when applied to multispectral/hyperspectral image denoising. It has also been demonstrated to provide dynamic range enhancement of bands that have impaired signal to noise ratios. Typical vector bilateral filtering usage does not employ parameters that have been determined to satisfy optimality criteria. This thesis also introduces an approach for selection of the parameters of a vector bilateral filter through an optimization procedure rather than by ad hoc means. The approach is based on posing the filtering problem as one of nonlinear estimation and minimizing the Stein\u27s unbiased risk estimate (SURE) of this nonlinear estimator. Along the way, this thesis provides a plausibility argument with an analytical example as to why vector bilateral filtering outperforms band-wise 2D bilateral filtering in enhancing SNR. Experimental results show that the optimized vector bilateral filter provides improved denoising performance on multispectral images when compared to several other approaches. Non-negative matrix factorization (NMF) technique and its extensions were developed to find part based, linear representations of non-negative multivariate data. They have been shown to provide more interpretable results with realistic non-negative constrain in unsupervised learning applications such as hyperspectral imagery unmixing, image feature extraction, and data mining. This thesis extends the NMF method by incorporating deterministic annealing optimization procedure, which will help solve the non-convexity problem in NMF and provide a better choice of sparseness constrain. The approach is based on replacing the difficult non-convex optimization problem of NMF with an easier one by adding an auxiliary convex entropy constrain term and solving this first. Experiment results with hyperspectral unmixing application show that the proposed technique provides improved unmixing performance compared to other state-of-the-art methods

Effectiveness of filtering methods in enhancing pulmonary carcinoma image quality: a comparative analysis

In recent years, information technology has vastly improved. The quality of the image has been degraded by noise, which defeats the purpose of the noisy images. The major purpose of this paper is to find out which filters provide a better outcome while preprocessing medical images using computer tomography scans. The purpose of this paper is to remove noise from any images, whether they are real-time datasets or online datasets. To enhance an image for preprocessing, we have compared various filters; these filters are already available, but the major purpose is to identify the best filter. We compared the different parameters to find the best and finally found that the modified bilateral filtering provided a better result. The noise has been removed by using a bilateral filter, and the image clarity has not changed when using this filter. We have discussed the advantages and drawbacks of each approach. The effectiveness of these filters is compared using the peak signal-to-noise ratio, structural similarity index, contrast-to-noise ratio, and mean square error. The proposed algorithm is tested on 5 sample lung images. The results show that the modified bilateral filter produces better results