Benchmarking Single Image Dehazing and Beyond
arXiv:1712.04143
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.
arXiv commentIEEE Transactions on Image Processing(TIP 2019)
References in corpus (7)
- Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
- DehazeNet: An End-to-End System for Single Image Haze Removal
- Semantic Foggy Scene Understanding with Synthetic Data
- Learning Depth from Single Monocular Images Using Deep Convolutional Neural Fields
- Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single Image
- Using User Generated Online Photos to Estimate and Monitor Air Pollution in Major Cities
- Improved Techniques for Learning to Dehaze and Beyond: A Collective Study
Cited by in corpus (12)
- Unsupervised Single Image Dehazing Using Dark Channel Prior Loss
- Gated Fusion Network for Single Image Dehazing
- Improved Techniques for Learning to Dehaze and Beyond: A Collective Study
- Gated Context Aggregation Network for Image Dehazing and Deraining
- The Effectiveness of Instance Normalization: a Strong Baseline for Single Image Dehazing
- Semantic Single-Image Dehazing
- DALE : Dark Region-Aware Low-light Image Enhancement
- Pyramid Fusion Dark Channel Prior for Single Image Dehazing
- PAD-Net: A Perception-Aided Single Image Dehazing Network
- Multi-scale CNN stereo and pattern removal technique for underwater active stereo system
- Multiple Linear Regression Haze-removal Model Based on Dark Channel Prior
- CNN based dense underwater 3D scene reconstruction by transfer learning using bubble database