Related papers: Removing cloud shadows from ground-based solar ima…

In recent years, various shadow detection methods from a single image have been proposed and used in vision systems; however, most of them are not appropriate for the robotic applications due to the expensive time complexity. This paper…

Severe weather conditions such as rain and snow adversely affect the visual quality of images captured under such conditions thus rendering them useless for further usage and sharing. In addition, such degraded images drastically affect…

Rain removal is an important but challenging computer vision task as rain streaks can severely degrade the visibility of images that may make other visions or multimedia tasks fail to work. Previous works mainly focused on feature…

Existing unsupervised methods have addressed the challenges of inconsistent paired data and tedious acquisition of ground-truth labels in shadow removal tasks. However, GAN-based training often faces issues such as mode collapse and…

Exploring the significance of geo-tagged information of a satellite image and associated shadow information in the image to enable man-made infrastructure dimensional characterization, in this paper a novel approach has been developed to…

Automatic detection of shadow regions in an image is a difficult task due to the lack of prior information about the illumination source and the dynamic of the scene objects. To address this problem, in this paper, a deep-learning based…

Rain removal plays an important role in the restoration of degraded images. Recently, data-driven methods have achieved remarkable success. However, these approaches neglect that the appearance of rain is often accompanied by low light…

Removing rain effects from an image is of importance for various applications such as autonomous driving, drone piloting, and photo editing. Conventional methods rely on some heuristics to handcraft various priors to remove or separate the…

Cloud removal plays a crucial role in enhancing remote sensing image analysis, yet accurately reconstructing cloud-obscured regions remains a significant challenge. Recent advancements in generative models have made the generation of…

We developed an automated method for sunspot detection using digital white-light solar images to achieve a performance similar to that of visual drawing observations in sunspot counting. To identify down to small, isolated spots correctly,…

Context. Clouds have already been detected in exoplanetary atmospheres. They play crucial roles in a planet's atmosphere and climate and can also create ambiguities in the determination of atmospheric parameters such as trace gas mixing…

Ground-based solar image restoration is a computationally expensive procedure that involves nonlinear optimization techniques. The presence of atmospheric turbulence produces perturbations in individual images that make it necessary to…

Outdoor scene relighting is a challenging problem that requires good understanding of the scene geometry, illumination and albedo. Current techniques are completely supervised, requiring high quality synthetic renderings to train a…

This paper presents the development and implementation of a cloud detection algorithm for Proba-V. Accurate and automatic detection of clouds in satellite scenes is a key issue for a wide range of remote sensing applications. With no…

Meteorologists use shapes and movements of clouds in satellite images as indicators of several major types of severe storms. Satellite imaginary data are in increasingly higher resolution, both spatially and temporally, making it impossible…

Sky-image-based solar forecasting using deep learning has been recognized as a promising approach in reducing the uncertainty in solar power generation. However, one of the biggest challenges is the lack of massive and diversified sky image…

Using only lidar or radar an accurate cloud boundary height estimate is often not possible. The combination of lidar and radar can give a reliable cloud boundary estimate in a much broader range of cases. However, also this combination with…

Semantic segmentation by convolutional neural networks (CNN) has advanced the state of the art in pixel-level classification of remote sensing images. However, processing large images typically requires analyzing the image in small patches,…

Attitude is one of the crucial parameters for space objects and plays a vital role in collision prediction and debris removal. Analyzing light curves to determine attitude is the most commonly used method. In photometric observations,…

Acquisition of data with adverse conditions in robotics is a cumbersome task due to the difficulty in guaranteeing proper ground truth and synchronising with desired weather conditions. In this paper, we present a simple method - recording…