We present a comprehensive study and evaluation of existing single-image dehazing algorithms, using a new largescale 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 and to subjective evaluation, and the novel task-driven evaluation. Experiments on RESIDE shed light on the comparisons and limitations of the state-of-the-art dehazing algorithms, and suggest promising future directions.
Li et al. REF gave a new approach called realistic single-image dehazing (RESIDE) based on a training set, with two different quality evaluations objective and subjective.
Benchmarking Single-Image Dehazing and Beyond
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Mathematics Medicine Computer Science