A general framework based on histogram equalization for image contrast enhancement is presented. In this framework, contrast enhancement is posed as an optimization problem that minimizes a cost function. Histogram equalization is an effective technique for contrast enhancement. However, a conventional histogram equalization (HE) usually results in excessive contrast enhancement, which in turn gives the processed image an unnatural look and creates visual artifacts. By introducing specifically designed penalty terms, the level of contrast enhancement can be adjusted; noise robustness, white/black stretching and mean-brightness preservation may easily be incorporated into the optimization. Analytic solutions for some of the important criteria are presented. Finally, a low-complexity algorithm for contrast enhancement is presented, and its performance is demonstrated against a recently proposed method.

We present PINCO, an in-network compression scheme for energy constrained, distributed, wireless sensor networks. PINCO reduces redundancy in the data collected from sensors, thereby decreasing the wireless communication among the sensor nodes and saving energy. Sensor data is buffered in the network and combined through a pipelined compression scheme into groups of data, while satisfying a user-specified end to end latency bound. We introduce a PINCO scheme for single-valued sensor readings. In this scheme, each group of data is a highly flexible structure so that compressed data can be recompressed without decompressing, in order to reduce newly available redundancy at a different stage of the network. We discuss how PINCO paremeters affect its performance, and how to tweak them for different performance requirements. We also include a performance study demonstrating the advantages of our approach over other data collection schemes based on simulation and prototype deployment results. 539 0-7803-7945-4/03/$17.00 (C) 2003 IEEE

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