Deep learning-based blind image deblurring plays an essential role in solving image blur since all existing kernels are limited in modeling the real world blur. Thus far, researchers focus on powerful models to handle the deblurring problem and achieve decent results. For this work, in a new aspect, we discover the great opportunity for image enhancement (e.g., deblurring) directly from RAW images and investigate novel neural network structures benefiting RAW-based learning.

The pedestrian attribute recognition aims at generating the structured description of pedestrian, which plays an important role in surveillance. However, it is difficult to achieve accurate recognition results due to diverse illumination, partial body occlusion and limited resolutions. Therefore, this paper proposes a comprehensive relationship framework for comprehensively describing and utilizing relations among attributes, describing different type of relations in the same dimension, and implementing complex transfers of relations in a GCN manner. 

In this paper, we present LensCast, a novel cross-layer video transmission framework for wireless networks, which seamlessly integrates millimeter wave (mmWave) lens multiple-input multiple-output (MIMO) with robust video transmission. LensCast is designed to exploit the video content diversity at the application layer, together with the spatial path diversity of lens antenna array at the physical layer, to achieve graceful video transmission performance under varying channel conditions.

Low light images suffer from a low dynamic range and severe noise due to low signal-to-noise ratio (SNR). In this paper, we propose joint contrast enhancement and noise reduction of low light images via just-noticeable-difference (JND) transform. We adopt the JND transform to achieve both contrast enhancement and noise reduction based on human visual perception.

Omnidirectional video, also known as 360-degree video, has become increasingly popular nowadays due to its ability to provide immersive and interactive visual experiences. However, the ultra high resolution and the spherical observation space brought by the large spherical viewing range make omnidirectional video distinctly different from traditional 2D video. To date, the video quality assessment (VQA) for omnidirectional video is still an open issue

Superpixel provides local pixel coherence and respects object boundary, which is beneficial to stereo matching. Recently, superpixel cues are introduced into deep stereo networks. These methods develop a superpixel-based sampling scheme to downsample input color images and upsample output disparity maps. However, in this way, the image details are inevitably lost in the downsampling and the upsampling process introduces errors in the final disparity as well. Besides, this mechanism further limits the possibility of utilizing larger and multi-scale superpixels, which are important to alleviate the matching ambiguity.

We introduce an efficient synthetic electrode selection strategy for use in Adaptive Electrical Capacitance Volume Tomography (AECVT). The proposed strategy is based on the Adaptive Relevance Vector Machine (ARVM) method and allows to successively obtain synthetic electrode configurations that yield the most decrease in the image reconstruction uncertainty for the spatial distribution of the permittivity in the region of interest. 

In this paper, we explore the spatiospectral image super-resolution (SSSR) task, i.e., joint spatial and spectral super-resolution, which aims to generate a high spatial resolution hyperspectral image (HR-HSI) from a low spatial resolution multispectral image (LR-MSI). To tackle such a severely ill-posed problem, one straightforward but inefficient way is to sequentially perform a single image super-resolution (SISR) network followed by a spectral super-resolution (SSR) network in a two-stage manner or reverse order.

Conventional digital cameras typically accumulate all the photons within an exposure period to form a snapshot image. It requires the scene to be quite still during the imaging time, otherwise it would result in blurry image for the moving objects. Recently, a retina-inspired spike camera has been proposed and shown great potential for recording high-speed motion scenes. Instead of capturing the visual scene by a single snapshot, the spike camera records the dynamic light intensity variation continuously.

In this work, a new nonlinear framework is presented for superior reconstructions in ultrasound-modulated optical tomography. The framework is based on minimizing a functional comprising of least squares data fitting term along with additional sparsity priors that promote high contrast, subject to the photon-propagation diffusion equation. The resulting optimization problem is solved using a sequential quadratic Hamiltonian scheme, based on the Pontryagin’s maximum principle, that does not involve semi-smooth calculus and is easy to implement.