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Most existing patch-based image denoising algorithms filter overlapping image patches and aggregate multiple estimates for the same pixel via weighting. Current weighting approaches always assume the restored estimates as independent random variables, which is inconsistent with the reality. In this letter, we analyze the correlation among the estimates and propose a bias-variance model to estimate...
Image denoising plays a fundamental role in many image processing applications. Utilizing sparse representation and nonlocal averaging together is such a successful framework that leads to considerable progress in denoising. Almost all the newly proposed denoising algorithms are built base on it, different in detailed implementation, and the denoising performance seems converging. What is the denoising...
Sparse signal representation based on overcomplete dictionaries has recently been extensively investigated, rendering the state-of-the-art results in signal, image and video processing. We propose a novel dictionary learning algorithm—the PK-SVD algorithm—which assumes prior probabilities on the dictionary atoms and learns a sparse dictionary under a popularity-based model. The prior distribution...
The lately popularized patch-based nonlocal (NL) image processing approach is cast into a framework of statistical context modeling, a thoroughly studied topic in data compression and information theory. The adaptation of image patch (context) to local waveform is crucial to the performance of NL-type of image processing but yet lacks a rigorous study. In this paper we propose a minimum description...
We propose to use Block-based Bilateral Filter (BBF), which extends the classical Bilateral Filter (BF) through operating in block-wise manner, to conceal missing image blocks in the application of compressed image transmission over wireless channels. We show that the problem of error-concealment using BBF can be considered as a superset of image denoising using BF. The BBF has the ability to capture...
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