Fast Object Localization In Hazy Images Based On Superpixel Grid Regularization

Object localization is a key problem in computer vision. The existing algorithmsoften localize objects in pixel level. Due to the huge amount of pixels in an image, mostof them sufer bad performance. Besides, existing algorithms do not take noise intoconsideration which will highly decrease the performance of them in case of degradedimages. In this paper, we study how to quickly and accurately locate objects in thefoggy image. For foggy images, we first perform the dehazing algorithm on it. Got theclear image, we use the SLIC, EGS, MeanShift to get a collection of superpixels, thenthe superpixel gridization algorithm is performed on the superpixel set, finally, in thisstructure, we perform object localization precisely and quickly.The main contribution of this paper include:1) We propose a local-albedo-insensitivesingle image dehazing algorithm, which can generate more nature image. We also cal-culate the depth image using the transmission image, based on which we realize there-focus and re-lighting efect.2) We propose a single image dehazing algorithm basedon TV-L1minimization and it can apply to all kinds of haze well.3) A video dehazingalgorithm is proposed, which can generate clear video with spatial and temporal con-sistency.4) For a clear image, we propose Maximum Cohesive Grid (MCG) and Min-imum Topological Diference (MTD) to regularize the superpixel grid.5)We extendthe algorithm for pixel-level object localization to superpixel-level and the algorithmout-performs the state-of-the-art ones in both accuracy and efciency.In order to verify the efectiveness and robustness of the proposed method, weperform test on a large number of simulated and real data. From the experimental results,we found that compared to the comparative method, the proposed method gains moreaccuracy and efciency.