In this paper, we propose an improved method for single image super-resolution on the basis of residual learning and convolutional sparse coding (CSC). The key idea of it is to first perform a CSC-based decomposition on the input so that it can be split into two predefined parts: the smooth and residual components. Then, the extracted components are individually mapped according to their own characteristics, rather than directly perform a mapping from the original input. Specifically, we place more emphases on the residual one as it is much important to our task, while the smooth one is just propagated to the final output for providing a quick reference. Accordingly, the final architecture of our method conceptually integrates all the above steps into a completely end-to-end trainable deep network. Extensive experimental results indicate that our proposed method outperforms many state-of-the-art methods in terms of both visual fidelity and objective evaluation.
Vehicle logo, as the key information of vehicle, combined with other vehicle characteristics will make vehicle management more effective in the intelligent transportation system. However, it is still a challenging task to extract effective features for vehicle logo recognition, largely due to its variations in illumination and low resolution. Aiming at improving the recognition rate of vehicle logo recognition, this paper proposes a new vehicle logo recognition method. First, in the aspect of vehicle logo feature extraction, a vehicle logo feature extraction algorithm based on the fusion of SIFT features and Dense-SIFT features was put forward to generate local feature descriptors. Then Bag-of-words model was used to describe vehicle logo features and form visual dictionary histogram. Considering that bag-of-words model ignores spatial structure information of objects, we introduced spatial pyramid model into bag-of-words model. In the aspect of vehicle logo recognition, vehicle logo was classified by using Support Vector Machine (SVM) based on one-against-the-rest multiclassification structure. Finally, our method was verified effectively through the experiment compared to other methods.
The vehicle with harmful black smoke pollutant emitted from vehicle exhaust pipe is usually called smoky vehicle. Existing smoky vehicle detection methods mainly lie on traditional manual monitoring. In this paper, we propose an intelligent smoky vehicle detection method based on Gray Level Co-occurrence Matrix (GLCM). This method can automatically detect smoky vehicles through analyzing the road surveillance videos. More specifically, we adopt Vibe background subtraction algorithm to detect vehicle objects. The gray-level integral projection technology and image local range technology are combined to detect the vehicle rear. We extract GLCM from the region at the back of the vehicle, and five different GLCM-based features, namely, angular second moment (ASM), entropy (ENT), contrast (CON), correlation (COR), and inverse difference moment (IDM), are selected to distinguish smoky images and nonsmoke images. The back propagation (BP) neural network is adopted to train the classifier and classify new samples. The experimental results show that the proposed method has a good performance.
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