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Water level measure is the key task in hydrological monitoring, wherein water level ruler (WLR) is the necessary enabled measure instrument. Due to its low cost and easy deployment, WLR has been widely used for flood monitoring in many hydrological stations. For saving labor costs, it is very desirable to achieve automatic reading of WLR, which has recently become a focus of the research in hydrological field. However, in automatic reading of WLR, there exist several issues or challenges, which mainly include low recognition rate of characters on WLR and low precision in calculating water level. To address these issues, in this paper we propose a practical and efficient method which is based on image processing. The proposed method consists of three important components, i.e., (1) a multi-template matching algorithm to recognize the characters on WLR, (2) a sequence verification algorithm to check and refine the recognized characters, and (3) a projection height comparison method to achieve accurate reading even under the circumstance of incomplete characters. We conduct experiments on real-world data to verify the efficiency of our proposed method. The experiment results show that the proposed method achieves $$63\%$$ recognition rate of characters on WLR, as well as average measure error of $$\pm {0.90\hbox { cm}}$$ which is much smaller than the national error standards on water-level monitoring in China ( $$\pm {1.0\hbox { cm}}$$ ). Therefore, we believe our proposed method could be useful for facilitating effective water level measure in practice.

Sparse coding-based single image super-resolution has attracted much interest. In this paper, a super-resolution reconstruction algorithm based on sparse coding with multi-class dictionaries is put forward. We propose a novel method for image patch classification, using the phase congruency information. A sub-dictionary is learned from patches in each category. For a given image patch, the sub-dictionary that belongs to the same category is selected adaptively. Since the given patch has similar pattern with the selected sub-dictionary, it can be better represented. Finally, iterative back-projection is used to enforce global reconstruction constraint. Experiments demonstrate that our approach can produce comparable or even better super-resolution reconstruction results with some existing algorithms, in both subjective visual quality and numerical measures.

Glaucoma is a chronic and widespread eye disease threatening humans' irreversible vision loss. The cup-to-disc ratio (CDR), one of the most important measurements used for glaucoma screening and diagnosis, requires accurate segmentation of optic disc and cup from fundus images. However, most existing techniques fail to obtain satisfactory segmentation performance because a significant number of pixel-level annotated data are often unavailable during training. To cope with this limitation, in this paper, we propose an effective joint optic disc and cup segmentation method based on semi-supervised conditional Generative Adversarial Nets (GANs). Our architecture consists of a segmentation net, a generator and a discriminator, to learn a mapping between the fundus images and the corresponding segmentation maps. Additionally, we employ both labeled and unlabeled data to improve the segmentation performance. The extensive experiments show that our method achieves state-of-the-art optic disc and cup segmentation results on both ORIGA and REFUGE datasets.