UC San Diego

With the explosion in the number of digital images taken every day, people are demanding more accurate and visually pleasing images. However, the captured images by modern cameras are inevitably degraded by noise. Besides deteriorating image visual quality, noise also degrades the performance of high-level vision tasks such as object recognition and tracking. Therefore, image denoising is a critical preprocessing step. This thesis presents novel contributions to the field of image denoising.

Image denoising is a highly ill-posed inverse problem. To alleviate the ill-posedness, an effective prior plays an important role and is a key factor for successful image denoising. With abundance of images available online, we propose to obtain priors from external image databases. In this thesis, we perform statistical analyses and rigorous derivations on how to obtain effective priors by utilizing external databases. For three denoising applications under different external settings, we show how we can explore effective priors and accordingly we present adaptive patch-based image denoising algorithms. In specific, we propose three adaptive algorithms: (1) adaptive non-local means for multiview image denoising; (2) adaptive image denoising by targeted databases; (3) adaptive image denoising by mixture adaption.

In (1), we present how to improve the non-local prior by finding more relevant patches in the multiview image denoising setting. We propose a method that uses a robust joint-view distance metric to measure the similarity of patches and derive an adaptive procedure to determine the optimal number of patches for final non-local means denoising. In (2), we propose to switch from generic database to targeted database, i.e., for specific objects to be denoised, only targeted databases with relevant images should be used. We explore both the group sparsity prior and the localized Bayesian prior, and show how a near optimal and adaptive denoising filter can be designed so that the targeted database can be maximally utilized. In (3), we propose an adaptive learning procedure called Expectation-Maximization (EM) adaptation. The adaptive process takes a generic prior learned from a generic database and transfers it to the image of interest to create a specific prior. This adapted prior better captures the distribution of the image of interest and is consistently better than the un-adapted one. For all the three denoising applications, we conduct various denoising experiments. Our proposed adaptive algorithms have some superior denoising performance than some state-of-the-art algorithms.