Accurate Transmission Estimation for Removing Haze and Noise from a Single Image

Image noise usually causes depth-dependent visual artifacts in single image dehazing. Most existing dehazing methods exploit a two-step strategy in the restoration, which inevitably leads to inaccurate transmission maps and low-quality scene radiance for noisy and hazy inputs. To address these problems, we present a novel variational model for joint recovery of the transmission map and the scene radiance from a single image. In the model, we propose a transmission-aware non-local regularization to avoid noise amplification by adaptively suppressing noise and preserving fine details in the recovered image. Meanwhile, to improve the accuracy of transmission estimation, we introduce a semantic-guided regularization to smooth out the transmission map while keeping depth inconsistency at the boundaries of different objects. Furthermore, we design an alternating scheme to jointly optimize the transmission map and the scene radiance as well as the segmentation map. Extensive experiments on synthetic and real-world data demonstrate that the proposed algorithm performs favorably against state-of-the-art dehazing methods on noisy and hazy images.