Thesis report on image denoising


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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.

Patch-based denoising methods have recently emerged as the state-of-the-art denoising approaches for various additive noise levels.

Dissertation/Thesis Abstract

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.

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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. Reference URL. Image and video enhancement through motion based interpolation and nonlocal-means denoising techniques. Large x max. Extra Large. Full Resolution. All PDF. This page. PDF Text search this item.

Latest Thesis Topics in Digital Image Processing

Text Search Object Description Title Image and video enhancement through motion based interpolation and nonlocal-means denoising techniques Author Thaipanich , Tanaphol Author email thaipani usc. Jay Advisor committee member Leahy, Richard M. Golubchik, Leana Abstract In this research, we investigate advanced image and video enhancement techniques based on motion based interpolation and nonlocal-means NL-means denoising.

The dissertation consists of three main results. Two video processing applications; namely, video error concealment EC and frame rate up-conversion FRUC , based on motion analysis have been examined. Then, an improved NL-means algorithm has been proposed for image denoising.


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They are detailed below. The boundary matching algorithm BMA is an attractive choice for video error concealment due to its low complexity.

Dissertation synopsis for imagedenoising(noise reduction )using non …

We first explain the superior performance of OBMA, and conclude that OBMA provides an excellent tradeoff between the complexity and the quality of concealed video for a wide range of test video sequences and error conditions. In addition, we present two extensions of OBMA, i. These extensions can be employed to enhance the performance of OBMA at slightly higher computational complexity. Finally, the effect of the flexible macroblock ordering FMO on the performance of several EC algorithms is examined.

Then, a low-complexity processing technique and robust FRUC algorithm are proposed to address these two issues. The proposed algorithm utilizes a translational motion vector model of the first- and the second-orders and detects the continuity of these motion vectors. Additionally, a spatial smoothness criterion is employed to improve perceptual quality of interpolated frames.

The superior performance of the proposed algorithm has been extensively tested and representative examples are given in this work. The proposed method employs the singular value decomposition SVD method and the K-means clustering K-means technique to achieve robust block classification in noisy images.

Then, a local window is adaptively adjusted to match the local property of a block. Finally, a rotated block matching algorithm based on the alignment of dominant orientation is adopted for similarity matching. In addition, the noise level can be accurately estimated using block classification and the Laplacian operator. Experimental results are given to demonstrate the superior denoising performance of the proposed adaptive NL-means ANL-means denoising technique over various image denoising benchmarks in term of both PSNR and perceptual quality comparison, where images corrupted by additive white Gaussian noise AWGN and Rician noise are both tested.

Keyword adaptive nonlocal-means ; frame rate up-conversion ; image denoising ; motion based interpolation ; video error concealment Language English Part of collection University of Southern California dissertations and theses Publisher of the original version University of Southern California Place of publication of the original version Los Angeles , California Publisher of the digital version University of Southern California.

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