4 accepted papers
Image dehazing, an important image restoration problem, aims to recover clear scene content from images degraded by atmospheric haze. Existing dehazing methods rely on observing the distribution of hazy images during training: supervised approaches typically depend on synthetic datasets, leading to …
Real-time image dehazmg is crucial for applications such as autonomous driving, surveillance, and remote sensing, where haze can significantly reduce visibility. However, many deep learning algorithms are hindered by large model sizes, making real-time processing difficult to achieve. Several fast a…
Restoring images under different adverse weather conditions with a single model is practical in many applications. Most existing weather restoration approaches are only able to handle a specific type of degradation, which is often insufficient in real-world scenarios where the weather type is unknow…
Tone mapping techniques aiming to convert high dynamic range (HDR) images to high-quality low dynamic range (LDR) images for display play a more crucial role in real-world vision systems with the increasing application of HDR images. However obtaining paired HDR and high-quality LDR images is diffic…