Learning a Non-Locally Regularized Convolutional Sparse Representation for Joint Chromatic and Polarimetric Demosaicking.

Division of focal plane color polarization camera becomes the mainstream in polarimetric imaging for it directly captures color polarization mosaic image by one snapshot, so image demosaicking is an essential task. Current color polarization demosaicking (CPDM) methods are prone to unsatisfied results since it's difficult to recover missed 15 or 14 pixels out of 16 pixels in color polarization mosaic images. To address this problem, a non-locally regularized convolutional sparse regularization model, which is advantaged in denoising and edge maintaining, is proposed to recall more information for CPDM task, and the CPDM task is transformed into an energy function to be solved by ADMM optimization.

A Novel Image Formation Model for Descattering.

In the field of image descattering, the image formation models employed for restoration approaches are often simplified. In these models, scattering distribution is uniform in homogeneous media when transmission is fixed. Through specifically designed experiments, we discover that scattering exhibits non-uniform characteristics even in homogeneous media.

A Shadow Imaging Bilinear Model and Three-Branch Residual Network for Shadow Removal.

The current shadow removal pipeline relies on the detected shadow masks, which have limitations for penumbras and tiny shadows, and results in an excessively long pipeline. To address these issues, we propose a shadow imaging bilinear model and design a novel three-branch residual (TBR) network for shadow removal. Our bilinear model reveals the single-image shadow removal process and can explain why simply increasing the brightness of shadow areas cannot remove shadows without artifacts.

Dual Aligned Siamese Dense Regression Tracker.

Anchor or anchor-free based Siamese trackers have achieved the astonishing advancement. However, their parallel regression and classification branches lack the tracked target information link and interaction, and the corresponding independent optimization maybe lead to task-misalignment, such as the reliable classification prediction with imprecisely localization and vice versa. To address this problem, we develop a general Siamese dense regression tracker (SDRT) with both task and feature alignments.

Optimizing the system-wide layout of sewage treatment plants based on enumeration and the orthogonal test.

Optimizing the locations of sewage treatment plants has enormous practical significance. In this study, a large-system mathematical model was developed for optimizing the locations of sewage treatment plants within a system and designing the associated pumping station pipe network. Head loss of pipe segments in the pipe network was the coupling constraint, the economic flow rate of pipe segments was determined by the feasible region constraints of decision variables, and the design variables were the sewage treatment plant locations, the design head of the pumping stations, the pipeline economic life, and the pipe diameter of divided pipe segments.

Tracking-by-Counting: Using Network Flows on Crowd Density Maps for Tracking Multiple Targets.

State-of-the-art multi-object tracking (MOT) methods follow the tracking-by-detection paradigm, where object trajectories are obtained by associating per-frame outputs of object detectors. In crowded scenes, however, detectors often fail to obtain accurate detections due to heavy occlusions and high crowd density. In this paper, we propose a new MOT paradigm, tracking-by-counting, tailored for crowded scenes.

Deep Retinex Network for Single Image Dehazing.

In this paper, we propose a retinex-based decomposition model for a hazy image and a novel end-to-end image dehazing network. In the model, the illumination of the hazy image is decomposed into natural illumination for the haze-free image and residual illumination caused by haze. Based on this model, we design a deep retinex dehazing network (RDN) to jointly estimate the residual illumination map and the haze-free image.

Brain metastases of lung cancer: comparison of survival outcomes among whole brain radiotherapy, whole brain radiotherapy with consecutive boost, and simultaneous integrated boost.

Purpose: Radiotherapy is the mainstay for treating brain metastasis (BM). The objective of this study is to evaluate the overall survival (OS) of patients with BM of lung cancer treated with different radiotherapy modalities.

Methods: Patients with BM of lung cancer who underwent radiotherapy between July 2007 and November 2017 were collected, and their baseline demographics, clinicopathological characteristics and treatments were recorded.

Reliable Multi-kernel Subtask Graph Correlation Tracker.

Many astonishing correlation filter trackers pay limited concentration on the tracking reliability and locating accuracy. To solve the issues, we propose a reliable and accurate cross correlation particle filter tracker via graph regularized multi-kernel multi-subtask learning. Specifically, multiple non-linear kernels are assigned to multi-channel features with reliable feature selection.

Optimization design method for urban sewage collection pipe networks.

In this study, a secondary subsystem mathematical model is established under the condition that the layout of the sewage collection branch, trunk, and main pipe network projects is fixed. The sewage collection branch and trunk pipe network projects are treated as the research objective by taking the minimum annual cost of the sewage collection pipe network projects as the objective function, the longitudinal slope of the pipe section and the economic flow rate of the pipe section as constraints, and the diameter of the pipe section as the decision variable. A first-level subsystem mathematical model is established by taking the sewage collection branch, trunk, and main pipe network project as the research object.

RGBD Salient Object Detection via Deep Fusion.

Numerous efforts have been made to design various low-level saliency cues for RGBD saliency detection, such as color and depth contrast features as well as background and color compactness priors. However, how these low-level saliency cues interact with each other and how they can be effectively incorporated to generate a master saliency map remain challenging problems. In this paper, we design a new convolutional neural network (CNN) to automatically learn the interaction mechanism for RGBD salient object detection.

Specular Reflection Separation With Color-Lines Constraint.

According to dichromatic reflection model, the previous methods of specular reflection separation in image processing often separate specular reflection from a single image using patch-based priors. Due to lack of global information, these methods often cannot completely separate the specular component of an image and are incline to degrade image textures. In this paper, we derive a global color-lines constraint from dichromatic reflection model to effectively recover specular and diffuse reflection.

A New Intrinsic-Lighting Color Space for Daytime Outdoor Images.

Extracting or separating intrinsic information and illumination from natural images is crucial for better solving computer vision tasks. In this paper, we present a new illumination-based color space, the IL (intrinsic information and lighting level) space. Its first two channels represent 2D intrinsic information, and the third channel is for lighting levels.

Simple and effective calculations about spectral power distributions of outdoor light sources for computer vision.

The spectral power distributions (SPD) of outdoor light sources are not constant over time and atmospheric conditions, which causes the appearance variation of a scene and common natural illumination phenomena, such as twilight, shadow, and haze/fog. Calculating the SPD of outdoor light sources at different time (or zenith angles) and under different atmospheric conditions is of interest to physically-based vision. In this paper, for computer vision and its applications, we propose a feasible, simple, and effective SPD calculating method based on analyzing the transmittance functions of absorption and scattering along the path of solar radiation through the atmosphere in the visible spectrum.

Pixel-wise orthogonal decomposition for color illumination invariant and shadow-free image.

In this paper, we propose a novel, effective and fast method to obtain a color illumination invariant and shadow-free image from a single outdoor image. Different from state-of-the-art methods for shadow-free image that either need shadow detection or statistical learning, we set up a linear equation set for each pixel value vector based on physically-based shadow invariants, deduce a pixel-wise orthogonal decomposition for its solutions, and then get an illumination invariant vector for each pixel value vector on an image. The illumination invariant vector is the unique particular solution of the linear equation set, which is orthogonal to its free solutions.

Wavelength-sensitive-function controlled reflectance reconstruction.

Spectral reflectance is defined as the "fingerprint" of an object and is illumination invariant. It has many applications in color reproduction, imaging, computer vision, and computer graphics. In previous reflectance reconstruction methods, spectral reflectance has been treated equally over the whole wavelength.

Tricolor attenuation model for shadow detection.

Shadows, the common phenomena in most outdoor scenes, bring many problems in image processing and computer vision. In this paper, we present a novel method focusing on extracting shadows from a single outdoor image. The proposed tricolor attenuation model (TAM) that describe the attenuation relationship between shadow and its nonshadow background is derived based on image formation theory.