We present an optical flow based method for noise reduction in image sequences. To prevent artefacts caused by optical flow imperfections, we propose a method to estimate these imperfections. We use the estimation to adaptively choose either a temporal or a spatial based noise reduction algorithm to be applied in different image zones. Our results have shown that an important noise reduction can be achieved with the proposed method, without the drawbacks of the simpler methods. The method has provided important noise reductions even with complex image sequences.
Color mismatch in stereoscopic 3D (S3D) images can create visual discomfort and affect the performance of S3D image processing algorithms, e.g., for depth estimation. In this paper, we propose a new deep learning-based solution for the problem of color mismatch correction. The proposed solution consists of a multi-task convolutional neural network, where color correction is the primary task and correspondence estimation is the secondary task. For the training and evaluation of the proposed network, a new S3D image dataset with color mismatch was created. Based on this dataset, experiments were conducted showing the effectiveness of our solution.
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