One of the fundamental challenges in the field of image processing and computer vision is image denoising, where the underlying goal is to estimate the original image by suppressing noise from a noise-contaminated version of the image. Image noise may be caused by different intrinsic (i.e., sensor) and extrinsic (i.e., environment) conditions which are often not possible to avoid in practical situations. Therefore, image denoising plays an important role in a wide range of applications such as image restoration, visual tracking, image registration, image segmentation, and image classification, where obtaining the original image content is crucial for strong performance. While many algorithms have been proposed for the purpose of image denoising, the problem of image noise suppression remains an open challenge, especially in situations where the images are acquired under poor conditions where the noise level is very high.

In the VIP lab, we investigate an alternative approach to the problem of image denoising based on data-adaptive stochastic optimization via Markov-Chain Monte Carlo sampling. By formulating the problem as a Bayesian optimization problem and taking a nonparametric stochastic strategy to solving this problem, such a Markov-Chain Monte Carlo denoising (MCMCD) strategy dynamically adapts to the underlying image and noise statistics in a flexible manner to provide high denoising performance while maintaining relatively low computational complexity.

Examples of the results produced using the MCMCD strategy are shown below:

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Image Segmentation/Classification

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