IEEE Transactions on Multimedia

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Low-light image enhancement is important for high-quality image display and other visual applications. However, it is a challenging task as the enhancement is expected to improve the visibility of an image while keeping its visual naturalness. Retinex-based methods have well been recognized as a representative technique for this task, but they still have the following limitations. First, due to less-effective image decomposition or strong imaging noise, various artifacts can still be brought into enhanced results.face of an object. These patches can be applied to multiple regions of the object, thereby making it resistant to various attacks such as cropping, local deformation, local surface degradation, or printing errors. 

We propose a new blind watermarking algorithm for 3D printed objects that has applications in metadata embedding, robotic grasping, counterfeit prevention, and crime investigation. Our method can be used on fused deposition modeling (FDM) 3D printers and works by modifying the printed layer thickness on small patches of the surface of an object. These patches can be applied to multiple regions of the object, thereby making it resistant to various attacks such as cropping, local deformation, local surface degradation, or printing errors. 

In this paper, we investigate the challenging task of removing haze from a single natural image. The analysis on the haze formation model shows that the atmospheric veil has much less relevance to chrominance than luminance, which motivates us to neglect the haze in the chrominance channel and concentrate on the luminance channel in the dehazing process. Besides, the experimental study illustrates that the YUV color space is most suitable for image dehazing.

Video summarization is an important technique to browse, manage and retrieve a large amount of videos efficiently. The main objective of video summarization is to minimize the information loss when selecting a subset of video frames from the original video, hence the summary video can faithfully represent the overall story of the original video. Recently developed unsupervised video summarization approaches are free of requiring tedious annotation on important frames to train a video summarization model and thus are practically attractive.

With the help of convolutional neural networks (CNNs), video-based human action recognition has made significant progress. CNN features that are spatial and channelwise can provide rich information for powerful image description. However, CNNs lack the ability to process the long-term temporal dependency of an entire video and further cannot well focus on the informative motion regions of actions.

Scene text plays a significant role in image and video understanding, which has made great progress in recent years. Most existing models on text detection in the wild have the assumption that all the texts are surrounded by a rotated rectangle or quadrangle. While there also exist lots of curved texts in the wild, which would not be bounded by a regular bounding box. 

It is a research hotspot to restore decoded videos with existing bitstreams by applying deep neural network to improve compression efficiency at decoder-end. Existing research has verified that the utilization of redundancy at decoder-end, which is underused by the encoder, can bring an increase of compression efficiency.

Wavelet transform is a powerful tool for multiresolution time-frequency analysis. It has been widely adopted in many image processing tasks, such as denoising, enhancement, fusion, and especially compression. Wavelets lead to the successful image coding standard JPEG-2000.

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