Multitask deep label distribution learning for blood pressure prediction

Qin, Keke; Huang, Wu; Zhang, Tao

doi:10.1016/j.inffus.2023.02.019

Cited by 11 publications

(1 citation statement)

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“…If the standard convolution of 3 × 3 kernel size is used in our model, the receptive field would only reach up to 41 × 41. To improve performance with a limited number of layers, we adopt dilated convolution [23,28] in our model to enlarge the receptive field. By adjusting a dilated coefficient to extend the receptive field with the same kernel size, the convolution achieves a larger receptive field.…”

Section: Single Image Restoration Methodsmentioning

confidence: 99%

Improving Compressed Video Using Single Lightweight Model with Temporal Fusion Module

Kuo

Wei

et al. 2023

Sensors

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Video compression algorithms are commonly used to reduce the number of bits required to represent a video with a high compression ratio. However, this can result in the loss of content details and visual artifacts that affect the overall quality of the video. We propose a learning-based restoration method to address this issue, which can handle varying degrees of compression artifacts with a single model by predicting the difference between the original and compressed video frames to restore video quality. To achieve this, we adopted a recursive neural network model with dilated convolution, which increases the receptive field of the model while keeping the number of parameters low, making it suitable for deployment on a variety of hardware devices. We also designed a temporal fusion module and integrated the color channels into the objective function. This enables the model to analyze temporal correlation and repair chromaticity artifacts. Despite handling color channels, and unlike other methods that have to train a different model for each quantization parameter (QP), the number of parameters in our lightweight model is kept to only about 269 k, requiring only about one-twelfth of the parameters used by other methods. Our model applied to the HEVC test model (HM) improves the compressed video quality by an average of 0.18 dB of BD-PSNR and −5.06% of BD-BR.

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Section: Single Image Restoration Methodsmentioning

confidence: 99%