Benchmarking Single Image Dehazing and Beyond

2017/12/12 by Boyi Li, Li, Boyi, Wenqi Ren +11 · 62 citations
Computer Science · #Artificial Intelligence (cs.AI) #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Image Enhancement Techniques #Machine Learning (cs.LG) #Video Surveillance and Tracking Methods #Visual Attention and Saliency Detection

paper · pdf · doi:10.48550/arxiv.1712.04143

openalex publication_date 2017/12/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We present a comprehensive study and evaluation of existing single image dehazing algorithms, using a new large-scale benchmark consisting of both synthetic and real-world hazy images, called REalistic Single Image DEhazing (RESIDE). RESIDE highlights diverse data sources and image contents, and is divided into five subsets, each serving different training or evaluation purposes. We further provide a rich variety of criteria for dehazing algorithm evaluation, ranging from full-reference metrics, to no-reference metrics, to subjective evaluation and the novel task-driven evaluation. Experiments on RESIDE shed light on the comparisons and limitations of state-of-the-art dehazing algorithms, and suggest promising future directions.

Citations

• No-Reference Image Quality Assessment in the Spatial Domain
• ImageNet Large Scale Visual Recognition Challenge
• A Statistical Evaluation of Recent Full Reference Image Quality Assessment Algorithms
• Blind Image Quality Assessment: A Natural Scene Statistics Approach in the DCT Domain
• Faster R-CNN: Towards Real-Time Object Detection with Region Proposal\n Networks
• Semantic Foggy Scene Understanding with Synthetic Data
• Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single Image
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Related

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