Single Image Deraining: A Comprehensive Benchmark Analysis
This work provides a comprehensive benchmark for researchers in computer vision to better assess and improve deraining algorithms, though it is incremental as it focuses on evaluation rather than new methods.
The authors tackled the problem of evaluating single image deraining algorithms by creating a new large-scale benchmark with synthetic and real-world rainy images, divided into three subsets for different purposes, and found that experiments on this dataset reveal comparisons and limitations of state-of-the-art methods.
We present a comprehensive study and evaluation of existing single image deraining algorithms, using a new large-scale benchmark consisting of both synthetic and real-world rainy images.This dataset highlights diverse data sources and image contents, and is divided into three subsets (rain streak, rain drop, rain and mist), 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 the dataset shed light on the comparisons and limitations of state-of-the-art deraining algorithms, and suggest promising future directions.