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Light field (LF) cameras often have significant limitations in spatial and angular resolutions due to their design. Many techniques that attempt to reconstruct LF images at a higher resolution only consider either spatial or angular resolution, but not both. We propose a generative network using high-dimensional convolution to improve both aspects. Our experimental results on both synthetic and real-world data demonstrate that the proposed model outperforms existing state-of-the-art methods in terms of both peak signal-to-noise ratio (PSNR) and visual quality.

We present an approach for ranking a collection of videos with overlapping fields of view. The ranking depends on how they allow to visualize as best as possible, i.e. with significant details, a trajectory query drawn in one of the videos. The proposed approach decomposes each video into cells and aims at estimating a correspondence map between cells from different videos using the linear correlation between their functions of activity.

Deep learning computation is often used in single-image dehazing techniques for outdoor vision systems. Its development is restricted by the difficulties in providing a training set of degraded and ground-truth image pairs. In this paper, we develop a novel model that utilizes cycle generative adversarial network through unsupervised learning to effectively remove the requirement of a haze/depth data set. Qualitative and quantitative experiments demonstrated that the proposed model outperforms existing state-of-the-art dehazing models when tested on both synthetic and real haze images.

Image smoothing is a very important topic in image processing. Among these image smoothing methods, the $L_0$ gradient minimization method is one of the most popular ones. However, the $L_0$ gradient minimization method suffers from the staircasing effect and over-sharpening issue, which highly degrade the quality of the smoothed image. To overcome these issues, we use not only the $L_0$ gradient term for finding edges, but also a surface area based term for the purpose of smoothing the inside of each region.

For images with congested scenes, the task of crowd analysis,
including crowd counting and crowd distribution prediction,
becomes very difficult. To address these issues, various
CNN-based approaches have been proposed. However, those
methods usually have a large number of parameters and require
huge computing resources. In this paper, we focus on
low-complexity approaches and propose a lightweight endto-
end network for crowd analysis. Our method utilizes an
effective scale-aware module to extract multi-scale features

Multiple atmospheric pollutants, such as PM2.5, PM10, and NO2, degrades air quality in many parts of the world. Fine-grained air pollution data can help combat the problem, but conventional monitoring stations are too expensive to support high spatial resolution; image-based estimates have the potential to improve spatial coverage. We estimate pollutant concentrations from images using the position-and color-dependent properties of scattering and absorption. We are the first to use images to estimate pollutant concentrations in systems with multiple pollutants.