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Road safety mapping using satellite images is a cost-effective but a challenging problem for smart city planning. The scarcity of labeled data, misalignment and ambiguity makes it hard to learn efficient embeddings in order to classify between safe and dangerous road segments. In this paper, we address the challenges using a region guided attention network. In our model, we extract global features from a base network and augment it with local features obtained using the region guided attention network. In addition, we perform domain adaptation for unlabeled target data.

Noise level is required as an input parameter in various image processing applications. In this work, we use the dark channel prior (DCP) to estimate the noise level of an image degraded by additive white Gaussian noise. We develop an approximate model of the probability density function of the dark channel of the noisy image. Using this model, the noise level is determined with the maximum likelihood estimation method from the dark channel intensity values of the noisy image.

Evaluating medical imaging segmentation is a very complex problem. Several papers proposed methodologies and differ-

High-frequency noise is present in several modalities of medical images. It originates from the acquisition process, scanner configurations, the scanned body, or to other external factors. This way, prospective filters are an important tool to improve image quality. In this paper, we propose a non-local weighted operational anisotropic diffusion filter and evaluate its effect on magnetic resonance images and on kV/CBCT radiotherapy images. We also provide a detailed analysis of non-local parameter settings.

High quality algorithms are demanded to reconstruct distorted color images in a variety of applications. For example, distortions can result during transmission over lossy channels in image coding or in multi-view imaging scenarios. In general, not all color channels are equally affected and the losses distribute differently in-between channels. However, state-of-the-art methods process color channels independently and do not take the cross color information into account.

Few-shot learning (FSL) addresses learning tasks in which only few samples are available for selected object categories. In this paper, we propose a deep learning framework for data hallucination, which overcomes the above limitation and alleviates possible overfitting problems. In particular, our method exploits semantic information into the hallucination process, and thus the augmented data would be able to exhibit semantics-oriented modes of variation for improved FSL performances.

Photovoltaic is one of the most important renewable energy sources for dealing with world-wide steadily increasing energy consumption. This raises the demand for fast and scalable automatic quality management during production and operation. However, the detection and segmentation of cracks on electroluminescence (EL) images of mono- or polycrystalline solar modules is a challenging task. In this work, we propose a weakly supervised learning strategy that only uses image-level annotations to obtain a method that is capable of segmenting cracks on EL images of solar cells.

Accurate hand segmentation is important for human segmentation. However, in videos, hand regions usually have serious motion blur, which reduces segmentation performance obviously. To solve this problem, we propose an automatic matting network to deal with motion-blurred hands. Then we combine the hand alpha mattes provided by matting network and the human segmentation results provided by segmentation network to generate our final human soft segmentation results.