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Section: New Results

On the Fly Hybrid Video Denoising

Participants : Hicham Badri, Hussein Yahia.

Video denoising is a standard pre-processing step in many imaging applications. Non-local methods such as the BM3D method adapted to videos have shown to produce good quality results, but these methods require multiple frames to produce a temporally coherent result, especially when the amount of noise is high. On the other hand, using a hybrid camera, we can get clean images of the scene. However, these images suffer from low-temporal coherence. We present a new approach to video denoising which consists in learning a mapping between the clean images and their corresponding noisy frames and propagate denoising to intermediate frames. To improve temporal coherency, we use a fast method method to sparsify the temporal gradient. Experiments on high-resolution videos show that the proposed method produces good quality on the fly video denoising while being computationally efficient.