2 research outputs found
Recent Advances in Image Restoration with Applications to Real World Problems
In the past few decades, imaging hardware has improved tremendously in terms of resolution, making widespread usage of images in many diverse applications on Earth and planetary missions. However, practical issues associated with image acquisition are still affecting image quality. Some of these issues such as blurring, measurement noise, mosaicing artifacts, low spatial or spectral resolution, etc. can seriously affect the accuracy of the aforementioned applications. This book intends to provide the reader with a glimpse of the latest developments and recent advances in image restoration, which includes image super-resolution, image fusion to enhance spatial, spectral resolution, and temporal resolutions, and the generation of synthetic images using deep learning techniques. Some practical applications are also included
SMars: Semi-Supervised Learning for Mars Semantic Segmentation
Deep learning has become a powerful tool for Mars exploration. Mars terrain
semantic segmentation is an important Martian vision task, which is the base of
rover autonomous planning and safe driving. However, there is a lack of
sufficient detailed and high-confidence data annotations, which are exactly
required by most deep learning methods to obtain a good model. To address this
problem, we propose our solution from the perspective of joint data and method
design. We first present a newdataset S5Mars for Semi-SuperviSed learning on
Mars Semantic Segmentation, which contains 6K high-resolution images and is
sparsely annotated based on confidence, ensuring the high quality of labels.
Then to learn from this sparse data, we propose a semi-supervised learning
(SSL) framework for Mars image semantic segmentation, to learn representations
from limited labeled data. Different from the existing SSL methods which are
mostly targeted at the Earth image data, our method takes into account Mars
data characteristics. Specifically, we first investigate the impact of current
widely used natural image augmentations on Mars images. Based on the analysis,
we then proposed two novel and effective augmentations for SSL of Mars
segmentation, AugIN and SAM-Mix, which serve as strong augmentations to boost
the model performance. Meanwhile, to fully leverage the unlabeled data, we
introduce a soft-to-hard consistency learning strategy, learning from different
targets based on prediction confidence. Experimental results show that our
method can outperform state-of-the-art SSL approaches remarkably. Our proposed
dataset is available at https://jhang2020.github.io/S5Mars.github.io/