Haze removal is important for computational photography and computer vision applications. However, most of the existing methods for dehazing are designed for daytime images, and cannot always work well in the nighttime. Different from the imaging conditions in the daytime, images captured in nighttime haze condition may suffer from non-uniform illumination due to artificial light sources, which...

Abstract In recent years, image defogging has become a research hotspot in the field of digital processing. Through enhancement processing, visual quality foggy images can be significantly improved, and it is also an important part subsequent To overcome limitations traditional dehazing, algorithm for with sky based on segmentation proposed. Firstly, K-means clustering feature analysis, region ...

<p>Computer vision techniques are widely applied to the object detection, license plate recognition, remote sensing, and outdoor monitoring system. The performance of these applications mainly relies on high quality image. However, an image can be led contrast decrease, color distortion, unclear structure by poor weather conditions human factors such as haze, fog, air pollution. These iss...

Texture and color distortion are common in existing learning-based dehazing algorithms, it is argued that one of the major reasons shallow features fog images underutilized, deep insufficient for single image dehazing. In order to provide more texture information restoration, need be added process decoding. Therefore, a multi-scale feature fusion pyramid attention network (PAN) proposed. PAN, c...

Intra-domain and inter-domain gaps are widely presented in image processing tasks due to data distribution differences. In the field of dehazing, particular previous works have paid attention gap between synthetic domain real domain. However, those methods only establish connection from without considering significant shift within (intra-domain gap). this work, we propose a Two-Step Dehazing Ne...