Single Image Deraining

Raindrop is a set of image pairs, where each pair contains exactly the same background scene, yet one is degraded by raindrops and the other one is free from raindrops. To obtain this, the images are captured through two pieces of exactly the same glass: one sprayed with water, and the other is left clean. The dataset consists of 1,119 pairs of images, with various background scenes and raindrops. They were captured with a Sony A6000 and a Canon EOS 60. Source: Attentive Generative Adversarial Network for Raindrop Removal from a Single Image

The Synthetic Rain Datasets consists of 13,712 clean-rain image pairs gathered from multiple datasets (Rain14000, Rain1800, Rain800, Rain12). With a single trained model, evaluation could be performed on various test sets, including Rain100H, Rain100L, Test100, Test2800, and Test1200. PSNR and SSIM are computed on Y-channel in YCbCr color space.

A dataset for rain removal with scene depth information. Compared with previous datasets, this dataset are all outdoor photos, each with a depth map, and the rain images exhibit different degrees of rain and fog.

A large-scale dataset of ~29.5K rain/rain-free image pairs that covers a wide range of natural rain scenes. Source: Spatial Attentive Single-Image Deraining with a High Quality Real Rain Dataset

The existing multi-modality image fusion dataset lacks comprehensive coverage of adverse weather scenarios. To address this, we introduce AWMM-100k, a benchmark dataset constructed by selecting samples from RoadScene, MSRS, M3FD, and LLVIP, followed by controlled degradation processing to simulate adverse weather conditions. Combined with real-world data captured using a DJI M30T drone equipped with high-resolution visible and thermal cameras, AWMM-100k comprises 187,699 images covering rain, haze, and snow, each categorized into heavy, medium, and light intensities. This dataset supports research on multi-modality image fusion under challenging weather conditions and is also applicable to image restoration tasks such as dehazing, deraining, and desnowing. We thank the original dataset for its contribution. In addition, we believe this dataset significantly expands the scope of multimodal image processing and computer vision research, facilitating advancements in both image fusion and restoration tasks under adverse conditions.

The HRI Dataset comprises a total of 3,200 image pairs. Each image pair comprises a clean background image, a depth image, a rain layer mask image, and a rainy image. It contains three scenes: lane, citystreet and japanesestreet, with image resolutions of $2048\times1024$. The lane scene contains 1,600 image pairs, consisting of images from 4 camera viewpoints, each viewpoint containing 100 images of different moments, and each moment containing 4 different intensities of rainy scenes. The citystreet scene contains 600 image pairs, consisting of images from 6 camera viewpoints, each viewpoint containing 25 images of different moments, and each moment containing 4 different intensities of rainy scenes. The japanesestreet scene contains 1,000 image pairs, consisting of images from 10 camera viewpoints, each viewpoint containing 25 images of different moments, and each moment containing 4 different intensities of rainy scenes. We split the HRI dataset into training set and test set according to camera viewpoints. For the lane scene, the training set contains images from 3 camera viewpoints, and the test set contains images from 1 camera viewpoint. For the citystreet scene, the training set contains images from 5 camera viewpoints, and the test set contains images from 1 camera viewpoint. For the japanesestreet scene, the training set contains images from 8 camera viewpoints, and the test set contains images from 2 camera viewpoints. Therefore, the training set comprises a total of 2,500 image pairs, and the test set comprises a total of 700 image pairs.