1,679 research outputs found

    Partition based vector filtering technique for suppression of noise in digital color images

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    A partition-based adaptive vector filter is proposed for the restoration of corrupted digital color images. The novelty of the filter lies in its unique three-stage adaptive estimation. The local image structure is first estimated by a series of center-weighted reference filters. Then the distances between the observed central pixel and estimated references are utilized to classify the local inputs into one of preset structure partition cells. Finally, a weighted filtering operation, indexed by the partition cell, is applied to the estimated references in order to restore the central pixel value. The weighted filtering operation is optimized off-line for each partition cell to achieve the best tradeoff between noise suppression and structure preservation. Recursive filtering operation and recursive weight training are also investigated to further boost the restoration performance. The proposed filter has demonstrated satisfactory results in suppressing many distinct types of noise in natural color images. Noticeable performance gains are demonstrated over other prior-art methods in terms of standard objective measurements, the visual image quality and the computational complexity

    Partition-based vector filtering technique for suppression of noise in digital color images

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    Author name used in this publication: Dagan FengCentre for Multimedia Signal Processing, Department of Electronic and Information Engineering2006-2007 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

    Machine Learning And Image Processing For Noise Removal And Robust Edge Detection In The Presence Of Mixed Noise

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    The central goal of this dissertation is to design and model a smoothing filter based on the random single and mixed noise distribution that would attenuate the effect of noise while preserving edge details. Only then could robust, integrated and resilient edge detection methods be deployed to overcome the ubiquitous presence of random noise in images. Random noise effects are modeled as those that could emanate from impulse noise, Gaussian noise and speckle noise. In the first step, evaluation of methods is performed based on an exhaustive review on the different types of denoising methods which focus on impulse noise, Gaussian noise and their related denoising filters. These include spatial filters (linear, non-linear and a combination of them), transform domain filters, neural network-based filters, numerical-based filters, fuzzy based filters, morphological filters, statistical filters, and supervised learning-based filters. In the second step, switching adaptive median and fixed weighted mean filter (SAMFWMF) which is a combination of linear and non-linear filters, is introduced in order to detect and remove impulse noise. Then, a robust edge detection method is applied which relies on an integrated process including non-maximum suppression, maximum sequence, thresholding and morphological operations. The results are obtained on MRI and natural images. In the third step, a combination of transform domain-based filter which is a combination of dual tree – complex wavelet transform (DT-CWT) and total variation, is introduced in order to detect and remove Gaussian noise as well as mixed Gaussian and Speckle noise. Then, a robust edge detection is applied in order to track the true edges. The results are obtained on medical ultrasound and natural images. In the fourth step, a smoothing filter, which is a feed-forward convolutional network (CNN) is introduced to assume a deep architecture, and supported through a specific learning algorithm, l2 loss function minimization, a regularization method, and batch normalization all integrated in order to detect and remove impulse noise as well as mixed impulse and Gaussian noise. Then, a robust edge detection is applied in order to track the true edges. The results are obtained on natural images for both specific and non-specific noise-level

    Image and radio-frequency data compression for OPS-SAT using FAPEC

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    OPS-SAT is an ESA technology demonstration cubesat which includes a colour camera, a Software Defined Radio (SDR) receiver and a powerful ARM processor. One of the experiments executed there is FAPEC, a high-performance and versatile data compression software. Among others, it features image compression and linear prediction coding algorithms, suitable for multi-band and baseband radio-frequency (RF) samples, respectively. Since its deployment on-board OPS-SAT in late 2020, FAPEC has allowed for downloading a large set of Earth Observation images. Recently, thanks to ESA Open Space Innovation Platform funds, these two algorithms from FAPEC are being improved to get better compression ratios and speeds, add video compression capabilities, and higher quality levels in case of lossy compression. A smart lossy approach is being developed for radio-frequency data, identifying the time segments with signal presence and quantizing noise-only samples to further reduce the size. In order to identify the really useful files to be downloaded from the satellite, on-board data analysis capabilities are being developed as well. In this work we present in-orbit results, recent developments and preliminary results obtained with the new algorithms on real data.Peer ReviewedPostprint (published version
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