329 research outputs found
COMPARISON OF DENOISING FILTERS ON COLOUR TEM IMAGE FOR DIFFERENT NOISE
TEM (Transmission Electron Microscopy) is an important morphological characterization tool for Nanomaterials. Quite often a microscopy image gets corrupted by noise, which may arise in the process of acquiring the image, or during its transmission, or even during reproduction of the image. Removal of noise from an image is one of the most important tasks in image processing. Denoising techniques aim at reducing the statistical perturbations and recovering as well as possible the true underlying signal. Depending on the nature of the noise, such as additive or multiplicative type of noise, there are several approaches towards removing noise from an image. Image De-noising improves the quality of images acquired by optical, electro-optical or electronic microscopy. This paper compares five filters on the measures of mean of image, signal to noise ratio, peak signal to noise ratio & mean square error. In this paper four types of noise (Gaussian noise, Salt & Pepper noise, Speckle noise and Poisson noise) is used and image de-noising performed for different noise by various filters (WFDWT, BF, HMDF, FDE, DVROFT). Further results have been compared for all noises. It is observed that for Gaussian Noise WFDWT & for other noises HMDF has shown the better performance results
Image and Texture Independent Deep Learning Noise Estimation using Multiple Frames
In this study, a novel multiple-frame based image and texture independent
convolutional Neural Network (CNN) noise estimator is introduced. The estimator
works
Image Restoration for Remote Sensing: Overview and Toolbox
Remote sensing provides valuable information about objects or areas from a
distance in either active (e.g., RADAR and LiDAR) or passive (e.g.,
multispectral and hyperspectral) modes. The quality of data acquired by
remotely sensed imaging sensors (both active and passive) is often degraded by
a variety of noise types and artifacts. Image restoration, which is a vibrant
field of research in the remote sensing community, is the task of recovering
the true unknown image from the degraded observed image. Each imaging sensor
induces unique noise types and artifacts into the observed image. This fact has
led to the expansion of restoration techniques in different paths according to
each sensor type. This review paper brings together the advances of image
restoration techniques with particular focuses on synthetic aperture radar and
hyperspectral images as the most active sub-fields of image restoration in the
remote sensing community. We, therefore, provide a comprehensive,
discipline-specific starting point for researchers at different levels (i.e.,
students, researchers, and senior researchers) willing to investigate the
vibrant topic of data restoration by supplying sufficient detail and
references. Additionally, this review paper accompanies a toolbox to provide a
platform to encourage interested students and researchers in the field to
further explore the restoration techniques and fast-forward the community. The
toolboxes are provided in https://github.com/ImageRestorationToolbox.Comment: This paper is under review in GRS
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
Realtime image noise reduction FPGA implementation with edge detection
The purpose of this dissertation was to develop and implement, in a Field
Programmable Gate Array (FPGA), a noise reduction algorithm for real-time
sensor acquired images. A Moving Average filter was chosen due to its
fulfillment of a low demanding computational expenditure nature, speed, good
precision and low to medium hardware resources utilization. The technique is
simple to implement, however, if all pixels are indiscriminately filtered, the result
will be a blurry image which is undesirable.
Since human eye is more sensitive to contrasts, a technique was
introduced to preserve sharp contour transitions which, in the author’s opinion,
is the dissertation contribution. Synthetic and real images were tested.
Synthetic, composed both with sharp and soft tone transitions, were generated
with a developed algorithm, while real images were captured with an 8-kbit
(8192 shades) high resolution sensor scaled up to 10 × 103 shades.
A least-squares polynomial data smoothing filter, Savitzky-Golay, was
used as comparison. It can be adjusted using 3 degrees of freedom ─ the
window frame length which varies the filtering relation size between pixels’
neighborhood, the derivative order, which varies the curviness and the
polynomial coefficients which change the adaptability of the curve. Moving
Average filter only permits one degree of freedom, the window frame length.
Tests revealed promising results with 2 and 4â„Ž polynomial orders. Higher
qualitative results were achieved with Savitzky-Golay’s better signal
characteristics preservation, especially at high frequencies.
FPGA algorithms were implemented in 64-bit integer registers serving
two purposes: increase precision, hence, reducing the error comparatively as if
it were done in floating-point registers; accommodate the registers’ growing
cumulative multiplications. Results were then compared with MATLAB’s double
precision 64-bit floating-point computations to verify the error difference
between both. Used comparison parameters were Mean Squared Error, Signalto-Noise Ratio and Similarity coefficient.O objetivo desta dissertação foi desenvolver e implementar, em FPGA,
um algoritmo de redução de ruÃdo para imagens adquiridas em tempo real.
Optou-se por um filtro de Média Deslizante por não exigir uma elevada
complexidade computacional, ser rápido, ter boa precisão e requerer moderada
utilização de recursos. A técnica é simples, mas se abordada como filtragem
monotónica, o resultado é uma indesejável imagem desfocada.
Dado o olho humano ser mais sensÃvel ao contraste, introduziu-se uma
técnica para preservar os contornos que, na opinião do autor, é a sua principal
contribuição. Utilizaram-se imagens sintéticas e reais nos testes. As sintéticas,
compostas por fortes e suaves contrastes foram geradas por um algoritmo
desenvolvido. As reais foram capturadas com um sensor de alta resolução de
8-kbit (8192 tons) e escalonadas a 10 × 103 tons.
Um filtro com suavização polinomial de mÃnimos quadrados, SavitzkyGolay, foi usado como comparação. Possui 3 graus de liberdade: o tamanho da
janela, que varia o tamanho da relação de filtragem entre os pixels vizinhos; a
ordem da derivada, que varia a curvatura do filtro e os coeficientes polinomiais,
que variam a adaptabilidade da curva aos pontos a suavizar. O filtro de Média
Deslizante é apenas ajustável no tamanho da janela. Os testes revelaram-se
promissores nas 2ª e 4ª ordens polinomiais. Obtiveram-se resultados
qualitativos com o filtro Savitzky-Golay que detém melhores caracterÃsticas na
preservação do sinal, especialmente em altas frequências.
Os algoritmos em FPGA foram implementados em registos de vÃrgula
fixa de 64-bits, servindo dois propósitos: aumentar a precisão, reduzindo o erro
comparativamente ao terem sido em vÃrgula flutuante; acomodar o efeito
cumulativo das multiplicações. Os resultados foram comparados com os
cálculos de 64-bits obtidos pelo MATLAB para verificar a diferença de erro
entre ambos. Os parâmetros de medida foram MSE, SNR e coeficiente de
Semelhança
A Tutorial on Speckle Reduction in Synthetic Aperture Radar Images
Speckle is a granular disturbance, usually modeled as a multiplicative noise, that affects synthetic aperture radar (SAR) images, as well as all coherent images. Over the last three decades, several methods have been proposed for the reduction of speckle, or despeckling, in SAR images. Goal of this paper is making a comprehensive review of despeckling methods since their birth, over thirty years ago, highlighting trends and changing approaches over years. The concept of fully developed speckle is explained. Drawbacks of homomorphic filtering are pointed out. Assets of multiresolution despeckling, as opposite to spatial-domain despeckling, are highlighted. Also advantages of undecimated, or stationary, wavelet transforms over decimated ones are discussed. Bayesian estimators and probability density function (pdf) models in both spatial and multiresolution domains are reviewed. Scale-space varying pdf models, as opposite to scale varying models, are promoted. Promising methods following non-Bayesian approaches, like nonlocal (NL) filtering and total variation (TV) regularization, are reviewed and compared to spatial- and wavelet-domain Bayesian filters. Both established and new trends for assessment of despeckling are presented. A few experiments on simulated data and real COSMO-SkyMed SAR images highlight, on one side the costperformance tradeoff of the different methods, on the other side the effectiveness of solutions purposely designed for SAR heterogeneity and not fully developed speckle. Eventually, upcoming methods based on new concepts of signal processing, like compressive sensing, are foreseen as a new generation of despeckling, after spatial-domain and multiresolution-domain method
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