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Atmospheric moisture, dust, smoke and vapor result in haze which tends to produce a distinctive gray or bluish hue and diminishes visibility. Acquired images can be used in applications such as surveillance, object identification, classification etc. only if the effect of weather is removed from them.One of the popular existing haze removal algorithms uses a dark channel prior based approach. Though this approach gives very good results, it is computationally complex. Retinex theory, which is widely used in image enhancement, can be applied effectively for haze removal also. Retinex theory is based on illumination -reflectance model.It also makes use of the theory of homomorphic filtering [1] which simultaneously normalizes the brightness across an image and increases contrast.We combine the ideas of dark channel prior and Retinex methods to obtain a haze removal technique that gives good results, and is computationally simple compared to the existing methods. Image quality assessment methods help us compare the quality of the dehazed images.

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Big data testing services are to deliver end to end testing methodologies which address our big data challenges. The testing module includes two types of functionalities. One is functional testing and second is non- functional testing. The functional testing should be accomplished at every stage of big data processing. Functional testing is nothing but the big data sources extraction testing, data migration testing and big data ecosystem. Testing which completes ETL test strategy, Map job reduce validation, multicore Data integration validation and data duplication check. On the other side the non-functional testing is to ensure that there are no quality defeat in data and no performance related issues. It covers the area for security testing, performance testing which solve the problem of monitoring and identify bottlenecks.

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