Zobrazeno 1 - 10
of 224
pro vyhledávání: '"Jiang, Zhiying"'
Image stitching seamlessly integrates images captured from varying perspectives into a single wide field-of-view image. Such integration not only broadens the captured scene but also augments holistic perception in computer vision applications. Given
Externí odkaz:
http://arxiv.org/abs/2402.15959
From Text to Pixels: A Context-Aware Semantic Synergy Solution for Infrared and Visible Image Fusion
With the rapid progression of deep learning technologies, multi-modality image fusion has become increasingly prevalent in object detection tasks. Despite its popularity, the inherent disparities in how different sources depict scene content make fus
Externí odkaz:
http://arxiv.org/abs/2401.00421
The correction of exposure-related issues is a pivotal component in enhancing the quality of images, offering substantial implications for various computer vision tasks. Historically, most methodologies have predominantly utilized spatial domain reco
Externí odkaz:
http://arxiv.org/abs/2309.01183
Due to the uneven scattering and absorption of different light wavelengths in aquatic environments, underwater images suffer from low visibility and clear color deviations. With the advancement of autonomous underwater vehicles, extensive research ha
Externí odkaz:
http://arxiv.org/abs/2309.01102
Photographs taken with less-than-ideal exposure settings often display poor visual quality. Since the correction procedures vary significantly, it is difficult for a single neural network to handle all exposure problems. Moreover, the inherent limita
Externí odkaz:
http://arxiv.org/abs/2309.00872
In this work, we conceptualize the learning process as information compression. We seek to equip generative pre-trained models with human-like learning capabilities that enable data compression during inference. We present a novel approach that utili
Externí odkaz:
http://arxiv.org/abs/2308.06942
Underwater images suffer from light refraction and absorption, which impairs visibility and interferes the subsequent applications. Existing underwater image enhancement methods mainly focus on image quality improvement, ignoring the effect on practi
Externí odkaz:
http://arxiv.org/abs/2308.00931
Multi-spectral image stitching leverages the complementarity between infrared and visible images to generate a robust and reliable wide field-of-view (FOV) scene. The primary challenge of this task is to explore the relations between multi-spectral i
Externí odkaz:
http://arxiv.org/abs/2307.16741
Rain streaks significantly decrease the visibility of captured images and are also a stumbling block that restricts the performance of subsequent computer vision applications. The existing deep learning-based image deraining methods employ manually c
Externí odkaz:
http://arxiv.org/abs/2305.18092
Since the differences in viewing range, resolution and relative position, the multi-modality sensing module composed of infrared and visible cameras needs to be registered so as to have more accurate scene perception. In practice, manual calibration-
Externí odkaz:
http://arxiv.org/abs/2304.05646