DEHAZING USING LIFTING WAVELET AND LAPLACIAN MATTE

Abstract

This work presents single image dehazing based on dark channel prior. Firstly, lifting haar wavelet has been used to decompose the hazy image into approximation and details. Then only the approximation component which is just one-fourth of the actual image dimension is further processed for dehazing. The dark channel prior used in the proposed work for dehazing is based on statistics of haze free images. The property that the intensity of dark channel gives approximate thickness of the haze is used to estimate the transmission and atmospheric light. Instead of constant airlight, proposed method employs scene depth to estimate spatially varying atmospheric light as it truly occurs in nature. Haze imaging model together with soft matting method has been used in this work to obtain a high quality haze free image. Experimental results demonstrate that the proposed approach produces better results as color fidelity and contrast of haze free image are improved and no over saturation in the sky region is observed. Further, with the use of lifting wavelet transform, reduction in computational time by a factor of two to three as compared to the conventional approach has been observed.