Distortion-free image dehazing by superpixels and ensemble neural network

Abstract

Single image dehazing is a technique used to remove the effect of haze from an image captured in poor weather conditions. Due to the scattering of particles, a captured image suffers from low visibility and contrast. Besides, scattering also adds nonlinear noise to the captured image. Existing image dehazing methods improve the visibility of the hazy image. However, these methods significantly generate artifacts such as halo at the depth discontinuities, blocking, and color aliasing in the sky regions. Some methods addressed this problem, but these methods introduce other issues such as loss of details, blurring effects, and oversaturation in the dehazed image. This paper proposes a method using superpixels and ensemble nonlinear regression to estimate the transmission that improves the visibility of a hazy image without any artifact. Conventional machine learning methods require a vast amount of haze-free and hazy images of different haze concentrations to train the model. The use of superpixels offers less number of training examples and also helps in reducing halo artifacts. The ensemble nonlinear regression predicts the transmission for a superpixel in such a way that the recovered image looks more natural, especially in the sky regions. The proposed method is evaluated by the various distortion parameters on real-world challenging and synthetic hazy images. The qualitative and quantitative analysis in experimental results proves that the proposed method is superior to that of state-of-the-art dehazing methods.