High Edge-Quality Light-Field Salient Object Detection Using Convolutional Neural Network

Abstract

The detection result of current light-field salient object detection methods suffers from loss of edge details, which significantly limits the performance of subsequent computer vision tasks. To solve this problem, we propose a novel convolutional neural network to accurately detect salient objects, by digging effective edge information from light-field data. In particular, our method is divided into four steps. Firstly, the network extracts multi-level saliency features from light-field data. Secondly, edge features are extracted from low-level saliency features and optimized by ground-truth guidance. Then, to sufficiently leverage high-level saliency features and edge features, the network hierarchically fuses them in a complementary manner. Finally, spatial correlations between different levels of fused features are considered to detect salient objects. Our method can accurately locate salient objects with exquisite edge details, by extracting clear edge information and accurate saliency information and fully fusing them. We conduct extensive evaluations on three widely used benchmark datasets. The experimental results demonstrate the effectiveness of our method, and it is superior to eight state-of-the-art methods.

A Hierarchical Resource Scheduling Method for Satellite Control System Based on Deep Reinforcement Learning

“…Artificial intelligence [34] and machine learning [35] have been widely implemented in recent years across different fields. They have been used in various fields, including computer vision [36], personalized recommendation systems [37], and intelligent machine control [38]. Reinforcement learning, in particular, has gained popularity due to its ability to achieve large-scale coverage and search of solution space, continuously learn and evolve from data, and display strong algorithm universality without requiring precise modeling, making it excellent for decision-making problems.…”

Reivew of Light Field Image Super-Resolution

“…With the boom in artificial intelligence in recent decades, deep learning has proven its effectiveness in many fields, including image super-resolution [15], image depth estimation [16], object detection [17], face recognition [18][19][20] and biometrics [21]. At the same time, deep learning is also used in the task of light field super-resolution.…”