Fast Single Image Dehazing via Multilevel Wavelet Transform based Optimization
This addresses image quality degradation for outdoor computer vision tasks like segmentation and detection, but it is incremental as it builds on existing optical models with optimization and wavelet techniques.
The paper tackles the problem of image dehazing in poor weather conditions by proposing a method that converts a non-convex bilinear problem into a convex linear optimization using atmospheric light estimation and accelerates it with multilevel Haar wavelet transform, resulting in improved image quality and computational efficiency compared to state-of-the-art algorithms.
The quality of images captured in outdoor environments can be affected by poor weather conditions such as fog, dust, and atmospheric scattering of other particles. This problem can bring extra challenges to high-level computer vision tasks like image segmentation and object detection. However, previous studies on image dehazing suffer from a huge computational workload and corruption of the original image, such as over-saturation and halos. In this paper, we present a novel image dehazing approach based on the optical model for haze images and regularized optimization. Specifically, we convert the non-convex, bilinear problem concerning the unknown haze-free image and light transmission distribution to a convex, linear optimization problem by estimating the atmosphere light constant. Our method is further accelerated by introducing a multilevel Haar wavelet transform. The optimization, instead, is applied to the low frequency sub-band decomposition of the original image. This dimension reduction significantly improves the processing speed of our method and exhibits the potential for real-time applications. Experimental results show that our approach outperforms state-of-the-art dehazing algorithms in terms of both image reconstruction quality and computational efficiency. For implementation details, source code can be publicly accessed via http://github.com/JiaxiHe/Image-and-Video-Dehazing.