Differentiating arterioles and venules in the fundus image is important for not only various eye diseases but also systemic diseases such as hypertension and ischemic stroke. In this study, we use dual-modal fundus images and develop a cascade refined U-net (CRU-net) to improve the arteriovenous segmentation. Dual-modal fundus images include not only a regular color fundus image (RGB image) but also another two monochromic images acquired using two different wavelengths, 570 nm and 610 nm in our study. The choice of these two wavelengths is based on the absorption spectra of hemoglobin. The two monochromic images provide much richer information on the arteriole and venule. Our proposed CRU-net can fully utilize the information and achieves the state-of-the-art performance on our dual-modal dataset (DualModal2019). The arteriovenous classification accuracy evaluated on the automatically detected vessels is 97.27%, significantly surpassed previous methods. The F1-scores are 77.69% and 79.53% for the arteriole and venule segmentation, respectively. We also test our CRU-net on the public DRIVE dataset with only the color fundus images. We achieve the accuracy of 93.97%, and F1-scores of 73.50% and 75.54% for the arteriole and venule, all of which significantly surpassed previously published methods. Our DualModal2019 dataset with manually annotated arterioles and venules is publicly available.

Blur detection is aimed at estimating the probability of each pixel being blurred or non-blurred in an image affected by motion or defocus blur. This task has gained considerable attention due to its promising application fields in computer vision. Accurate differentiations of anomalous regions (including the sharp but homogeneous regions and pseudo-sharp backgrounds) and motion-blurred regions are main challenges in blur detection, in which both conventional and recently developed blur detection methods have limited performance and low time efficiency. To address these issues, this paper develops an accurate and fast blur detection method for both motion and defocus blur using a new end-to-end deep neural network. First, a novel multi-input multi-loss encoder-decoder network (M-shaped) is proposed to learn rich hierarchical representations related to blur. Then, to resolve the problem that blur degree is susceptible to scales, we construct a pyramid ensemble model (PM-Net) consisting of different scales of M-shaped subnets and a unified fusion layer. Experiments demonstrate that the proposed PM-Net can accurately handle those challenging scenarios with anomalous regions for both defocus and motion blur. Our method performs better than previous state-of-the-art methods. It achieves the F1-score of 0.893 for only defocus blur and 0.884 for joint motion and defocus blur, both of which significantly surpass previous methods on the benchmark BDD dataset. We also test our PM-Net on another public CDD dataset composed of challenging defocused images. The proposed method also outperforms other published methods with an F1-score of 0.885. In addition, our proposed method is hundreds of times faster (millisecond) than other state-of-the-art methods (second). Moreover, our experiments also demonstrate that the PM-Net is robust to noise and has a good generalization property.

Blur detection is aimed to differentiate the blurry and sharp regions from a given image. This task has attracted much attention in recent years due to its importance in computer vision with the integration of image processing and artificial intelligence. However, blur detection still suffers from problems such as the oversensitivity to image noise and the difficulty in cost–benefit balance. To deal with these issues, we propose an accurate and efficient blur detection method, which is concise in architecture and robust against noise. First, we develop a sequency spectrum-based blur metric to estimate the blurriness of each pixel by integrating a re-blur scheme and the Walsh transform. Meanwhile, to eliminate the noise interference, we propose an adaptive sequency spectrum truncation strategy by which we can obtain an accurate blur map even in noise-polluted cases. Finally, a multi-scale fusion segmentation framework is designed to extract the blur region based on the clustering-guided region growth. Experimental results on benchmark datasets demonstrate that the proposed method achieves state-of-the-art performance and the best balance between cost and benefit. It offers an average F₁ score of 0.887, MAE of 0.101, detecting time of 0.7 s, and training time of 0.5 s. Especially for noise-polluted blurry images, the proposed method achieves the F₁ score of 0.887 and MAE of 0.101, which significantly surpasses other competitive approaches. Our method yields a cost–benefit advantage and noise immunity that has great application prospect in complex sensing environment.

Pedestrian detection is one of the most important applications of computer vision. In this paper, aiming at the problem of poor anti-interference ability of traditional codebook algorithm in the complex background and the cavity-prone problem of traditional frame difference method, a pedestrian detection algorithm integrating the optimized frame difference method and improved codebook is proposed. In this algorithm, a double code word structure is designed. An update mechanism is added to the code words in the codebook background modeling process and detection process, and improved double code word book algorithm based on YUV color space is designed to realize the real-time background update function. After that, bilateral filtering is added to the traditional three-frame difference method to further remove the noise. At the same time, the extracted rectangular region is used for filling operation and morphological processing to obtain the corresponding foreground image. Finally, the foreground image of codebook model and the foreground image of frame difference method are calculated with “and”, “different or” and “or” of pixels to obtain the precise region of moving target. The experimental results show that this algorithm combines the advantages of the two methods and abandons the disadvantages of each method in the detection prospect. It can accurately detect moving targets and has good robustness. The effectiveness of this algorithm is also verified by experiments.