This thesis studies the problem of video denoising. In the first part we focus on patch-based video denoising methods. We study in details VBM3D, a popular video denoising method, to understand the mechanisms that made its success. We also present a real-time implementation on GPU of this method. We then study the impact of patch search in video denoising and in particular how searching for similar patches in the entire video, a global patch search, improves the denoising quality. Finally, we propose a novel causal and recursive method called NL-Kalman that produces very good temporal consistency.In the second part, we look at the newer trend of deep learning for image and video denoising. We present one of the first neural network architecture, using temporal self-similarity, competitive with state-of-the-art patch-based video denoising methods. We also show that deep learning offers new opportunities. In particular, it allows for denoising without knowing the noise model. We propose a framework that allows denoising of videos that have been through an unknown processing pipeline. We then look at the case of mosaicked data. In particular, we show that deep learning is undeniably superior to previous approaches for demosaicking. We also propose a novel training process for demosaicking without ground-truth based on multiple raw acquisition. This allows training for real case applications. In the third part we present different applications taking advantage of mechanisms similar those studied for denoising. The first problem studied is anomaly detection. We show that this problem can be reduced to detecting anomalies in noise. We also look at forgery detection and in particular copy-paste forgeries. Just like for patch-based denoising, solving this problem requires searching for similar patches. For that, we do an in-depth study of PatchMatch and see how it can be used for detecting forgeries. We also present an efficient method based on sparse patch matching.