Underwater images suffer from color cast and low visibility caused by the medium scattering and absorption, which will reduce the use of valuable information from the image. In this paper, we propose a novel method which includes four stages of pixel intensity center regionalization, global equalization of histogram, local equalization of histogram and multi-scale fusion. Additionally, this method uses a pixel intensity center regionalization strategy to perform centralization of the image histogram on the overall image. Global equalization of histogram is employed to correct color of the image according to the characteristics of each channel. Local equalization of dual-interval histogram based on average of peak and mean values is used to improve contrast of the image according to the characteristics of each channel. Dual-image multiscale fusion to integrate the contrast, saliency and exposure weight maps of the color corrected and contrast enhanced images. Experiments on variety types of degraded underwater images show that the proposed method produces better output results in both qualitative and quantitative analysis, thus, the proposed method outperforms other state-of-the-art methods. INDEX TERMS Underwater image enhancement, Pixel intensity center regionalization, Histogram equalization, Multi-scale fusion.

Based on analyzing the microwave image reconstruction characteristic, this paper expands the existing microwave image reconstruction model, and a mixed integer linear program model for microwave image reconstruction is created. The existing particle swarm algorithm is improved and used for solving the model. In the improved PSO, the efficiency of this algorithm in that problem is raised through improvement

Vehicle license plate detection is an important step in automatic license plate recognition, which is prone to be influenced by the background interference and complex environment conditions. It is known that cartoon-texture decomposition split an image into geometric cartoon and texture component, which can remove background interference away from the vehicle image. In this paper, we introduce a fast cartoon-texture decomposition filter into the detection process. Combining the edge detection, morphological filtering and Radon transform based tilt correction method, we formulate a new license plate detection algorithm. Experiment results confirm that the proposed algorithm can remove background interference away, inhibit the emergence of fake license plates, and improve the detection accuracy. Moreover, there is no inner loop iteration in the new algorithm, so it is fast and high-efficiency.

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The importance of case-indexing method selection in the CBR system is analyzed, and the defaults of traditional case indexing methods are pointed out. A model of caseretrieving based on AHP is presented and the basic principle and process of CBR and AHP are introduced. With the development environment of CGI, the prototype system is developed, and the core source code and running interface are shown to confirm the effectivity and feasibility of this model.