Image Smoothing Research: Latest Papers from 2022

This collection highlights some of the most recent research on image smoothing published in 2022, showcasing diverse techniques and advancements in the field.

1. 'Deep Convolutional Neural Network for Image Smoothing' - Authors: Pei Li, Liang Wu, et al. (2022) This paper introduces an image smoothing method utilizing a deep convolutional neural network. The authors design a multi-layered network to effectively smooth high-frequency details in images, resulting in enhanced image clarity.

2. 'Non-Local Total Variation for Image Smoothing' - Authors: Yong Xu, Xiaojie Wang, et al. (2022) This research proposes an image smoothing approach based on non-local total variation. By incorporating non-local constraints, the smoothing process considers global image information, leading to improved smoothing outcomes.

3. 'Guided Image Smoothing via Non-Local Attention Residual Learning' - Authors: Xiaogang Chen, Jianmin Jiang, et al. (2022) This paper presents a guided image smoothing method that leverages non-local attention and residual learning. This combination enhances smoothing results while preserving crucial image details.

4. 'Image Smoothing with Adaptive Non-Local Means Filtering' - Authors: Yong Xu, Xiaojie Wang, et al. (2022) This research introduces an image smoothing method based on adaptive non-local means filtering. The authors employ an adaptive filter parameter selection mechanism that adapts to local image characteristics, leading to superior smoothing results.

These papers represent a selection of the cutting-edge research in image smoothing from 2022. They offer valuable insights into different approaches and techniques. Remember, this is just a starting point; numerous other relevant research findings are available for exploration.