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This work exploits the basic denoising autoencoding (DAE) as enhanced priori for color image restoration (IR). The proposed method consists of two steps: enhanced DAE network learning and iterative restoration. To be special, at the training phase, a denoising network taking 6-dimensional variable as input is trained. Then, the network-driven high-dimensional prior information embedded DAE priori is utilized in the iterative restoration procedure. We first map the intermediate color image to be 6 dimensional and employ the higher-dimensional network to handle its corrupted version.

High-dynamic-range (HDR) video streams have been delivered through high-efficiency video coding (HEVC). HDR video tone mapping is required additionally but is operated separately to adjust the content’s dynamic range for each display device. HDR video tone mapping and HEVC encoding share common computational processes for spatial and temporal coherence in a video stream; however, they have been developed and implemented independently with their own computational budgets.

Among the many known type of intra-class variations, facial expressions are considered particularly challenging, as witnessed by the large number of methods that have been proposed to cope with them. The idea inspiring this work is that dynamic facial features (DFF) extracted from facial expressions while a sentence is pronounced, could possibly represent a salient and inherently safer biometric identifier, due to the greater difficulty in forging a time variable descriptor instead of a static one.

Occlusion removal is an interesting application of image enhancement, for which, existing work suggests manually-

Children's exposure to violence has become a severe problem with the rapid development of Internet. Recognizing violent video and estimating violence extent become crucial. Most researches focus on violent scene or violent action detection, lacking overall violence extent information. In this paper, we propose a violence rating prediction approach and build a novel violent video dataset.

The ability to test methods based on simulated data is an important component in the development of intelligent localization systems at the present time. This includes the replication of realistic environments and sensors on the one hand. On the other hand, an implementation of a correct movement profile of the target mobile platform is required. This is complicated by the large variety of possible platform variants. We propose a method to transfer movement profiles that have been recorded in real world into a simulation environment.

Efficient classification of short videos is of a great challenge in industry due to their large amounts and diverse semantics. In this paper, we present a real-time multi-label classification system to attain it. Specifically, a frame-level preprocessing strategy is first proposed to efficiently decode the videos for useful information. Then an image-based model is developed to achieve the final video-level classification.

Transcranial magnetic stimulation (TMS) is a non-invasive clinical technique used for treatment of several neurological diseases such as depression, Alzheimer’s disease and Parkinson’s disease. However, it is always challenging to accurately adjust the electric field on different specific brain regions due to the requirement of several stimulation parameters’ optimizations.

With an ever-growing amount of unexpected menaces in crowded places such as terrorist attacks, it is paramount to develop techniques to aid investigators reconstructing all details about an event of interest. To extract reliable information about the event, all kinds of available clues must be jointly exploited. As a matter of fact, today's sources of information are plenty and varied, as important events affecting many people are typically documented by different sources.

Over the years, several problems regarding the analysis of face images have been addressed, including face detection, recognition, identification, and verification. The advent of Convolutional Neural Networks (CNNs) gave rise to a drastic improvement on state-of-the-art performances for these problems.