As a challenging problem, image haze removal plays an important role in computer vision applications. The dark channel prior has been widely studied for haze removal since it is simple and effective; however, it still suffers from oversaturation, artefacts and dark-look. To resolve these problems, this study proposes a method of single image haze removal using content-adaptive dark channel and post enhancement. The main contributions of this work are as follows: first, an associative filter, which can transfer the structures of a reference image and the grey levels of a coarse image to the filtering output, is employed to compute the dark channel efficiently and effectively. Secondly, the dark channel confidence is utilised to restrict the dark channel based on the content of the image. Finally, a post enhancement method is devised to map the luminance of the restored haze-free image with the preservation of local contrast. Experimental results demonstrate that the proposed method significantly improves the visibility of the hazy image.
In this paper, we demonstrate a perceptual-based 3D skeleton motion data refinement method based on a bidirectional recurrent autoencoder, called BRA-P. Three main technical contributions are made by the proposed network. First, the proposed BRA-P can address noisy data with different noise types and amplitudes using one network, and this attribute makes the approach more suitable for raw motion data with heterogeneous mixed noise. Second, due to the usage of perceptual loss, which measures the difference in high-level features extracted by a pretrained perceptual autoencoder, BRA-P improves the perceptual similarity between refined motion data and clean motion data, especially for the case where the noisy data and target clean data have different topologies. Third, BRA-P further improves the bone-length consistency and smoothness of the refined motion using the perceptual autoencoder as a postprocessing network. Ablation experiments verify the effect of the three technical contributions of our approach. The results of the experiments on synthetic noise data and raw motion data captured by Kinect demonstrate that our method outperforms several state-of-the-art methods in the cleaning of mixed-noise data by one network.
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