Benchmarking Single Image Dehazing and Beyond
This work provides a comprehensive benchmark for researchers in computer vision to better assess and improve dehazing algorithms, though it is incremental as it focuses on evaluation rather than new methods.
The authors tackled the problem of evaluating single image dehazing algorithms by creating a new large-scale benchmark called RESIDE, which includes synthetic and real-world images across five subsets, and they found that it highlights the limitations of state-of-the-art methods and suggests future directions.
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.