Medical images often consist of low-contrast objects corrupted by random noise arising in the image acquisition
process. Thus, image denoising is one of the fundamental tasks required by medical imaging analysis. In this work, we
investigate an adaptive denoising scheme based on the nonlocal (NL)-means algorithm for medical imaging applications.
In contrast with the traditional NL-means algorithm, the proposed adaptive NL-means (ANL-means) denoising scheme
has three unique features. First, it employs the singular value decomposition (SVD) method and the K-means clustering
(K-means) technique for robust classification of blocks in noisy images. Second, the local window is adaptively adjusted
to match the local property of a block. Finally, a rotated block matching algorithm is adopted for better similarity
matching. Experimental results from both additive white Gaussian noise (AWGN) and Rician noise are given to
demonstrate the superior performance of the proposed ANL denoising technique over various image denoising
benchmarks in term of both PSNR and perceptual quality comparison.
Low-complexity error concealment techniques for missing macroblock (MB) recovery in mobile video delivery based on
the boundary matching principle is extensively studied and evaluated in this work. We first examine the boundary
matching algorithm (BMA) and the outer boundary matching algorithm (OBMA) due to their excellent trade-off in
complexity and visual quality. Their good performance is explained, and additional experiments are given to identify
their strengths and weaknesses. Then, two more extensions of OBMA are presented. One is obtained by extending the
search pattern for performance improvement at the cost of additional complexity. The other is based on the use of
multiple overlapped outer boundary layers.
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