Specifically, a quality embedding network is adopted in a multimodal fashion, and corresponding stochastic gradient descent (SGD) optimization objective is derived with variational inference and conditional independence assumption. Observing that label uncertainty severely obstacles the progress of affective video content analysis, a deep denoising framework is proposed to infer true latent labels and annotation qualities from heavy label noise, fully utilizing the multimodal information contained in videos.

Therefore, it is difficult to get useful supervision signals to learn well-generalized patterns responsible for. However, it faces various challenges such as the gap between intrinsic visual-aural features and spontaneous human emotional response, as well as ubiquitously existed label noise in affective annotations. The experimental results show that at reasonable computational cost it achieves state-of-the-art denoising performance in terms of both peak signal-to-noise ratio and subjective visual quality.Īffective video content analysis has attracted a lot of attention recently. We develop an efficient realization of this video denoising algorithm. Significant improvement of this approach is achieved by using a two-step algorithm where an intermediate estimate is produced by grouping and collaborative hard-thresholding and then used both for improving the grouping and for applying collaborative empirical Wiener filtering. Since, in general, the obtained block estimates are mutually overlapping, we aggregate them by a weighted average in order to form a non-redundant estimate of the video. This filtering - that we term 'collaborative filtering'- exploits the correlation between grouped blocks and the corresponding highly sparse representation of the true signal in the transform domain.

Each formed 3D group is filtered by a 3D transform-domain shrinkage (hard-thresholding and Wiener filtering), the result of which are estimates of all grouped blocks. This grouping is realized as a spatio-temporal predictive-search block-matching, similar to techniques used for motion estimation. A noisy video is processed in blockwise manner and for each processed block we form a 3D data array that we call 'group' by stacking together blocks found similar to the currently processed one. We propose an effective video denoising method based on highly sparse signal representation in local 3D transform domain. Our extensive experiments demonstrate that our DnCNN model can not only exhibit high effectiveness in several general image denoising tasks, but also be efficiently implemented by benefiting from GPU computing. This property motivates us to train a single DnCNN model to tackle with several general image denoising tasks such as Gaussian denoising, single image super-resolution and JPEG image deblocking. With the residual learning strategy, DnCNN implicitly removes the latent clean image in the hidden layers. Different from the existing discriminative denoising models which usually train a specific model for additive white Gaussian noise (AWGN) at a certain noise level, our DnCNN model is able to handle Gaussian denoising with unknown noise level (i.e., blind Gaussian denoising). Specifically, residual learning and batch normalization are utilized to speed up the training process as well as boost the denoising performance. In this paper, we take one step forward by investigating the construction of feed-forward denoising convolutional neural networks (DnCNNs) to embrace the progress in very deep architecture, learning algorithm, and regularization method into image denoising.

Discriminative model learning for image denoising has been recently attracting considerable attentions due to its favorable denoising performance.

Video denoise