Saliency‐based dark channel prior model for single image haze removal

2021 IEEE International Conference on Image Processing (ICIP)

2021

Self Cite

The ambient light and transmission of light source regions and non-light source regions have different physical properties in nighttime scenes. However, existing nighttime dehazing algorithms ignored this phenomenon and got oversaturated results looked like in the daytime. In this paper, we propose a nighttime dehazing method using fused ambient light and transmission based on saliency detection. We first propose a multi-scale saliency detection algorithm to differentiate light source regions and non-light source regions. Then we estimate ambient light and transmission of the two regions based on different methods to acquire the fused ambient light and fused transmission. Finally, we get the haze-free results based on the nighttime imaging model. Experimental results show that our method outperforms other state-of-the-art methods in color recovery and our haze-free results still look like in the nighttime.

Section: Introductionmentioning

confidence: 99%

Nighttime Haze Removal Using Saliency-Oriented Ambient Light And Transmission Estimation

Lin

Wang

2021 IEEE International Conference on Image Processing (ICIP)

2021

Self Cite

“…(1), these parameters J , A and () tx are all unknown, which is a severely ill-posed problem for single image dehazing. Therefore, the prior-based methods [9][10][11][12], such as dark channel prior (DCP) [9], must leverage various priors or assumptions related to haze to estimate transmission maps and airlight from single hazy images. The prior-based methods strongly rely on their proposed priors or assumptions.…”

Section: Introductionmentioning

confidence: 99%

Afdn: Attention-Based Feedback Dehazing Network For Uav Remote Sensing Image Haze Removal

Wang¹,

Wu²,

2021 IEEE International Conference on Image Processing (ICIP)

2021

Self Cite

To efficiently remove haze in unmanned aerial vehicle (UAV) remote sensing images, a novel attention-based feedback dehazing network (AFDN) is proposed, which is constructed by feedback connections and attention-based feedback blocks (AFBs). It has three major advantages compared with other dehazing algorithms: 1) The feedback connections, which allow network to use previous state to improve current performance, can effectively help the proposed AFDN generate clear remote sensing scenes progressively. 2) The AFBs are specially designed to extract global residual features, in which the dual attention block can usefully reduce redundant information and improve the fitting ability of network. 3) To obtain abundant texture information from UAV remote sensing images and restore real ground surfaces, an energy loss is employed for texture features learning. Experiments on synthetic datasets and real UAV remote sensing images verify the superiority of AFDN over several state-of-the-art methods in terms of qualitative and quantitative analysis.

“…Saliency model is conducted to explain the visual attention mechanism [9]. Some saliency models are based on background [10][11] and compactness prior [12] [13]. Others introduce techniques such as low-rank recovery [14] and cooccurrence histogram [15] to detect salient regions.…”

Section: Introductionmentioning

confidence: 99%

Uav Remote Sensing Image Dehazing Based On Saliency Guided Two-Scaletransmission Correction

2021 IEEE International Conference on Image Processing (ICIP)

Zheng

et al. 2021

Self Cite

Current dehazing methods for unmanned aerial vehicle (UAV) remote sensing images often hold problems of texture detail loss in highlight regions and color distortions. This is mainly due to incorrect estimation of the transmission. In this paper, we propose a UAV dehazing method based on saliency guided two-scale transmission correction. Firstly, we propose a dehaze-driven frequency domain saliency model to detect highlight regions of hazy UAV images for better transmission correction. Secondly, we introduce a two-scale correction method to estimate the transmission map with more accurate texture details. We also introduce a suppression parameter to further suppress color distortions and energy over-reduction. Finally, the saliency map is taken as a weight of transmission correction to avoid texture detail loss and color distortions, especially in highlights. Compared with state-of-the-art methods, our method shows better visual effect and detail visibility, especially for UAV images with highlight regions.