EFFECTIVE DEHAZING WITH CONCEPTUAL REGULARIZATION USING SURF FOR HAZE IMAGE

Image matching plays a crucial role in many remote sensing applications such as change detection, cartography using imagery with reduced overlapping, fusion of images taken with different sensors. In the early remote sensing systems, this task required substantial human involvement by manually selecting some feature points of significant landmarks. Nowadays, due to the significant progress of local feature point’s detectors and descriptors, the tasks of matching and registration can be done in most of the cases automatically. We Propose a new process of contrast enhancement aims to increase the perceptibility of the objects in the image such as auto-contrast, gamma correction, linear mapping, histogram stretching. We start from the global operator that enhances the contrast of the luminance ‘L’ based on the hue ‘H’ and saturation ‘S’. Our strategy is designed to preserve most of the local features and details in the enhanced version, the local contrast preservation is crucial in the process of matching by feature points. In its final stage, the well-known Scale Invariant Feature Transform (SIFT) operator filters out all the features with low local contrast. The enhanced hazy image optimizes the contrast by constraining the degradation of the finest details and gradients to minimum