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

Efficient, edge-aware, combined color quantization and dithering

Abstract—In this paper we present a novel algorithm to simultaneously accomplish color quantization and dithering of images. This is achieved by minimizing a perception-based cost function which considers pixel-wise differences between filtered versions of the quantized image and the input image. We use edge aware filters in defining the cost function to avoid mixing colors on opposite sides of an edge. The importance of each pixel is weighted according to its saliency. To rapidly minimize the cost function, we use a modified multi-scale iterative conditional mode (ICM) algorithm which updates one pixel a time while keeping other pixels unchanged. As ICM is a local method, careful initialization is required to prevent termination at a local minimum far from the global one. To address this problem, we initialize ICM with a palette generated by a modified median-cut method. Compared to previous approaches, our method can produce high quality results with fewer visual artifacts but also requires significantly less computational effort. Index Terms—Color quantization, dithering, optimization-based image processing. I

Kinect Range Sensing: Structured-Light versus Time-of-Flight Kinect

Recently, the new Kinect One has been issued by Microsoft, providing the next generation of real-time range sensing devices based on the Time-of-Flight (ToF) principle. As the first Kinect version was using a structured light approach, one would expect various differences in the characteristics of the range data delivered by both devices. This paper presents a detailed and in-depth comparison between both devices. In order to conduct the comparison, we propose a framework of seven different experimental setups, which is a generic basis for evaluating range cameras such as Kinect. The experiments have been designed with the goal to capture individual effects of the Kinect devices as isolatedly as possible and in a way, that they can also be adopted, in order to apply them to any other range sensing device. The overall goal of this paper is to provide a solid insight into the pros and cons of either device. Thus, scientists that are interested in using Kinect range sensing cameras in their specific application scenario can directly assess the expected, specific benefits and potential problem of either device.Comment: 58 pages, 23 figures. Accepted for publication in Computer Vision and Image Understanding (CVIU

Color Image Enhancement Based on Ant Colony Optimization Algorithm

In the collection, transmission, decoding process, the images are likely to produce noise. Noise makes the image color distorted and the articulation dropped, and also affects the image quality. Due to different causes, there are different types of noise, and the impulse noise is most common among them which exert great influence on the image quality. This paper, according to the characteristics of the color image, combines the ant colony algorithm and weighted vector median filter method to put forward an algorithm for the impulse noise removal and the color image enhancement. This method finds the optimal filter bank parameter by ant colony optimization (ACO) and processes image points polluted by the noise to achieve the purpose of image enhancement and protect the image details and edge information. Simulation experiment proves the correctness and validity of this method

Disparity-compensated view synthesis for s3D content correction

International audienceThe production of stereoscopic 3D HD content is considerably increasing and experience in 2-view acquisition is in progress. High quality material to the audience is required but not always ensured, and correction of the stereo views may be required. This is done via disparity-compensated view synthesis. A robust method has been developed dealing with these acquisition problems that introduce discomfort (e.g hyperdivergence and hyperconvergence...) as well as those ones that may disrupt the correction itself (vertical disparity, color difference between views...). The method has three phases: a preprocessing in order to correct the stereo images and estimate features (e.g. disparity range...) over the sequence. The second (main) phase proceeds then to disparity estimation and view synthesis. Dual disparity estimation based on robust block-matching, discontinuity-preserving filtering, consistency and occlusion handling has been developed. Accurate view synthesis is carried out through disparity compensation. Disparity assessment has been introduced in order to detect and quantify errors. A post-processing deals with these errors as a fallback mode. The paper focuses on disparity estimation and view synthesis of HD images. Quality assessment of synthesized views on a large set of HD video data has proved the effectiveness of our method

A New Hybridization of Bilateral and Wavelet Filters for Noisy De-Noisy Images

In this work we propose, a hybrid noise reduction algorithm that is a combination of a spatial field binary filter and a hybrid wave field threshold function. These two methods are used to stop Gaussian noise. The hybrid filter is a nonlinear filter that deals with spatial averaging of non-uniform edges. We found it to be an effective technique for image reduction. Determining filter parameters for the mixed filter is important to avoid large differences in results, besides the issue of acceleration velocity. This hybrid model, binary filtering, and Wavelet Thresholding have tried standard images, such as normal eyes, MRI, Roya Face, Ultrasound, X-Ray, and Rawa. Different Gaussian noise was added with different standard deviations σ = 10, 20, 35, 40, and 50. The peak-to-noise ratio (PSNR) signal, MSE, VIF, IQI, and the proposed model MSE between pixels were used as quantitative measures of performance of the relative noise reduction algorithms and then were compared to the models

Interactive removal and ground truth for difficult shadow scenes

A user-centric method for fast, interactive, robust, and high-quality shadow removal is presented. Our algorithm can perform detection and removal in a range of difficult cases, such as highly textured and colored shadows. To perform detection, an on-the-fly learning approach is adopted guided by two rough user inputs for the pixels of the shadow and the lit area. After detection, shadow removal is performed by registering the penumbra to a normalized frame, which allows us efficient estimation of nonuniform shadow illumination changes, resulting in accurate and robust removal. Another major contribution of this work is the first validated and multiscene category ground truth for shadow removal algorithms. This data set containing 186 images eliminates inconsistencies between shadow and shadow-free images and provides a range of different shadow types such as soft, textured, colored, and broken shadow. Using this data, the most thorough comparison of state-of-the-art shadow removal methods to date is performed, showing our proposed algorithm to outperform the state of the art across several measures and shadow categories. To complement our data set, an online shadow removal benchmark website is also presented to encourage future open comparisons in this challenging field of research

Patch-based Denoising Algorithms for Single and Multi-view Images

In general, all single and multi-view digital images are captured using sensors, where they are often contaminated with noise, which is an undesired random signal. Such noise can also be produced during transmission or by lossy image compression. Reducing the noise and enhancing those images is among the fundamental digital image processing tasks. Improving the performance of image denoising methods, would greatly contribute to single or multi-view image processing techniques, e.g. segmentation, computing disparity maps, etc. Patch-based denoising methods have recently emerged as the state-of-the-art denoising approaches for various additive noise levels. This thesis proposes two patch-based denoising methods for single and multi-view images, respectively. A modification to the block matching 3D algorithm is proposed for single image denoising. An adaptive collaborative thresholding filter is proposed which consists of a classification map and a set of various thresholding levels and operators. These are exploited when the collaborative hard-thresholding step is applied. Moreover, the collaborative Wiener filtering is improved by assigning greater weight when dealing with similar patches. For the denoising of multi-view images, this thesis proposes algorithms that takes a pair of noisy images captured from two different directions at the same time (stereoscopic images). The structural, maximum difference or the singular value decomposition-based similarity metrics is utilized for identifying locations of similar search windows in the input images. The non-local means algorithm is adapted for filtering these noisy multi-view images. The performance of both methods have been evaluated both quantitatively and qualitatively through a number of experiments using the peak signal-to-noise ratio and the mean structural similarity measure. Experimental results show that the proposed algorithm for single image denoising outperforms the original block matching 3D algorithm at various noise levels. Moreover, the proposed algorithm for multi-view image denoising can effectively reduce noise and assist to estimate more accurate disparity maps at various noise levels

Fuzzy metrics and fuzzy logic for colour image filtering

El filtrado de imagen es una tarea fundamental para la mayoría de los sistemas de visión por computador cuando las imágenes se usan para análisis automático o, incluso, para inspección humana. De hecho, la presencia de ruido en una imagen puede ser un grave impedimento para las sucesivas tareas de procesamiento de imagen como, por ejemplo, la detección de bordes o el reconocimiento de patrones u objetos y, por lo tanto, el ruido debe ser reducido. En los últimos años el interés por utilizar imágenes en color se ha visto incrementado de forma significativa en una gran variedad de aplicaciones. Es por esto que el filtrado de imagen en color se ha convertido en un área de investigación interesante. Se ha observado ampliamente que las imágenes en color deben ser procesadas teniendo en cuenta la correlación existente entre los distintos canales de color de la imagen. En este sentido, la solución probablemente más conocida y estudiada es el enfoque vectorial. Las primeras soluciones de filtrado vectorial, como por ejemplo el filtro de mediana vectorial (VMF) o el filtro direccional vectorial (VDF), se basan en la teoría de la estadística robusta y, en consecuencia, son capaces de realizar un filtrado robusto. Desafortunadamente, estas técnicas no se adaptan a las características locales de la imagen, lo que implica que usualmente los bordes y detalles de las imágenes se emborronan y pierden calidad. A fin de solventar este problema, varios filtros vectoriales adaptativos se han propuesto recientemente. En la presente Tesis doctoral se han llevado a cabo dos tareas principales: (i) el estudio de la aplicabilidad de métricas difusas en tareas de procesamiento de imagen y (ii) el diseño de nuevos filtros para imagen en color que sacan provecho de las propiedades de las métricas difusas y la lógica difusa. Los resultados experimentales presentados en esta Tesis muestran que las métricas difusas y la lógica difusa son herramientas útiles para diseñar técnicas de filtrado,Morillas Gómez, S. (2007). Fuzzy metrics and fuzzy logic for colour image filtering [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/1879Palanci