Authors:
Angelos Kanlis
;
Vazgken Vanian
;
Sotiris Karvarsamis
;
Ioanna Gkika
;
Konstantinos Konstantoudakis
and
Dimitrios Zarpalas
Affiliation:
Visual Computing Lab (VCL), Information Technologies Institute (ITI), Centre for Research and Technology - Hellas (CERTH), Thessaloniki, Greece
Keyword(s):
Synthetic Dataset, Image Restoration, Adverse Weather Conditions, Semantic Segmentation, Depth Estimation, Benchmarking, Unreal Engine.
Abstract:
This paper presents the SynthRSF dataset for training and evaluating single-image rain, snow and haze denoising algorithms, as well as evaluating object detection, semantic segmentation, and depth estimation performance in noisy or denoised images. Our dataset features 26,893 noisy images, each accompanied by its corresponding ground truth image. It further includes 13,800 noisy images accompanied by ground truth, 16-bit depth maps and pixel-accurate annotations for various object instances in each frame. The utility of SynthRSF is assessed by training unified models for rain, snow, and haze removal, achieving good objective metrics and excellent subjective results compared to existing adverse weather condition datasets. Furthermore, we demonstrate its use as a benchmark for the performance of an object detection algorithm in weather-degraded image datasets.