In this paper, a probabilistic model is proposed for high dynamic image's tone reproduction. This novel method learns a distribution for local pixel energy of the tone. With the constraint of the gradient variation on the HDR image, an energy distribution is set up based on the similarity between the gradient variation on the HDR and the LDR image. The probabilistic framework for the tone mapping operation is formulated into an energy minimization process by a Maximum A posteriori (MAP) deduction. It turns out that, the proposed method generates LDR image with more visual information than the previous ones. Experimental results show that this approach is convincible and competitive, which can be applied in areas like advanced image editing, displayer development, etc.

Technological advances in sensor manufacture, communication, and computing are stimulating the development of new applications that are transforming traditional vision systems into pervasive intelligent camera networks. The analysis of visual cues in multi-camera networks enables a wide range of applications, from smart home and office automation to large area surveillance and traffic surveillance.While dense camera networks -in which most cameras have large overlapping fields of view -are well studied, we are mainly concerned with sparse camera networks. A sparse camera network undertakes large area surveillance using as few cameras as possible, and most cameras have non-overlapping fields of view with one another. The task is challenging due to the lack of knowledge about the topological structure of the network, variations in the appearance and motion of specific tracking targets in different views, and the difficulties of understanding composite events in the network. In this review paper, we present a comprehensive survey of recent research results to address the problems of intra-camera tracking, topological structure learning, target appearance modeling, and global activity understanding in sparse camera networks. A number of current open research issues are discussed.

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