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We develop a variational retinex algorithm for enhancing single underwater image with multiorder gradient priors of reflectance and illumination. First, a simple yet effective color correction approach is used to remove color casts and recover naturalness. Then, a variational retinex model for enhancing the color-corrected underwater image is established by imposing multiorder gradient priors of reflectance and illumination.

Visual impairment is one of the most serious social and public health problems in the world, therefore, it is of great theoretical and practical significance to study the image enhancement algorithms for the visually impaired, which is the basis for the development of assistive devices. In this paper, a general deep learning based image enhancement framework for the visually impaired is proposed, which can be used to enhance images to compensate for any visually impaired symptom that can be modeled.

We propose a ghost-free high dynamic range (HDR) image synthesis algorithm by unrolling low-rank matrix completion. By exploiting the low-rank structure of the irradiance maps from low dynamic range (LDR) images, we formulate ghost-free HDR imaging as a general low-rank matrix completion problem. Then, we solve the problem iteratively using the augmented Lagrange multiplier (ALM) method. At each iteration, the optimization variables are updated by closed-form solutions and the regularizers are updated by learned deep neural networks.

Binary shapes, or silhouettes, are essential in human communication. They include, for example, all fonts and many logos. They can be extracted from images in raster form but require a vectorization for resolution independent editing. In this paper, we propose a mathematically founded silhouette vectorization algorithm, which converts a raster 2D shape to a Scalable Vector Graphics (SVG) format whose control points are geometrically stable under affine transformations. The proposed method can also be used as a reliable feature point detector for silhouettes.

Depression is a common mental disorder that affects patients’ daily life. Most existing depression detection methods consume a lot of medical resources and exist at risk of subjective judgment. Therefore, we propose an objective and convenient experimental paradigm. Firstly, it selects emotional images as stimuli and records the subjects’ eye movement data. Secondly, we establish a connection between image processing and subjects’ psychological conditions analysis.

A few-shot personalized saliency prediction method using person similarity based on collaborative multi-output Gaussian process regression is presented in this paper. Contrary to prediction of general saliency maps, that of personalized saliency maps (PSMs), which is a focus of attention owing to its heterogeneity among individuals, is a challenging problem since the amount of training gaze data is limited due to the burden on new persons. Thus, the proposed method focuses on the similarity of gaze tendency between persons.

This paper presents a correlation-aware attention branch network (CorABN) using multi-modal data for deterioration level estimation of infrastructures. CorABN can collaboratively use visual features from distress images and text features from text data recorded at the inspection to improve the estimation accuracy of deterioration levels. Specifically, by maximizing correlation between the visual and text features that provide useful information for the deterioration level estimation, a correlation-aware attention map can be generated.

Fine-grained human action recognition is a core research topic in computer vision. Inspired by the recently proposed hierarchy representation of fine-grained actions in FineGym and SlowFast network for action recognition, we propose a novel multi-task network which exploits the FineGym hierarchy representation to achieve effective joint learning and prediction for fine-grained human action recognition.