Haze Removal and Sky Detection Algorithms for Photo Images

Sky is one of the most significant subject matters commonly seen in outdoor photos. We propose a highly efficient sky detection algorithm. First we detect a rough sky-ground boundary. Second we calculate parameters related to the appearance of sky. Finally, we use these parameters on a probability model that indicates how possible a pixel is belong to sky. And an image processing library with parallel processing techniques is used to implement proposed algorithm. In a common desktop computer, a VGA size image only requires less than 35ms, which can be considered a threshold for real-time processing, e.g. proposed algorithm may process 480p video in real time. Another characteristic commonly presented on photo image is haze. There are a number of researches on images’ dehazing. Dehazing technique is especially useful on applications such as object recognition. However there is a tradeoff between strength of haze-removing and tones of color. If we want to remove haze as much as possible, we may sacrifice tones of color. This tradeoff usually occurs in intensity and saturation, caused by dehazing algorithms which may do some guesses about information hidden by haze. A framework has been proposed to handle the tradeoff between tones of color and strength of removing haze. We aim to make human feel the same color tones after processing. Experiment results show that our framework is efficient to remove blocky and over-saturation effects on dehazed images. Furthermore, some features are explored for a haze-degree classification system which employs SVM as the learning model. This system is suitable for image content recognition or helping adjust parameters needed by haze-removal algorithms.