Single Image Defogging Method Based on Image Patch Decomposition and Multi-Exposure Image Fusion

Abstract

Bad weather conditions (such as fog, haze) seriously affect the visual quality of images. According to the scene depth information, physical model-based methods are used to improve image visibility for further image restoration. However, the unstable acquisition of the scene depth information seriously affects the defogging performance of physical model-based methods. Additionally, most of image enhancement-based methods focus on the global adjustment of image contrast and saturation, and lack the local details for image restoration. So, this paper proposes a single image defogging method based on image patch decomposition and multi-exposure fusion. First, a single foggy image is processed by gamma correction to obtain a set of underexposed images. Then the saturation of the obtained underexposed and original images is enhanced. Next, each image in the multi-exposure image set (including the set of underexposed images and the original image) is decomposed into the base and detail layers by a guided filter. The base layers are first decomposed into image patches, and then the fusion weight maps of the image patches are constructed. For detail layers, the exposure features are first extracted from the luminance components of images, and then the extracted exposure features are evaluated by constructing gaussian functions. Finally, both base and detail layers are combined to obtain the defogged image. The proposed method is compared with the state-of-the-art methods. The comparative experimental results confirm the effectiveness of the proposed method and its superiority over the state-of-the-art methods.

A Review of Traditional Dehazing Methods for Foggy Images

“…Liu et al [9] proposed a joint contrast enhancement and exposure fusion framework that treats image dehazing as a local visibility and global contrast enhancement problem and then processed separately on three independent components. Liu et al [10] used the gamma transform and saturation adjustment to produce a series of exposure images, image layer decomposition by bootstrap filtering, and then the construction of fusion weights and extraction of exposure features, respectively, and finally, the two layers of information are fused to obtain a dehazed image.…”

Brightness and Contrast Enhancement Method for Color Images Via Pairing Adaptive Gamma Correction and Histogram Equalization

“…In general, the visual quality of images is enhanced by improving their illumination and contrast. The main enhancement techniques are histogram equalization (HE) [3], grey transformation (GT) [13], defogging model [14], image fusion [8], Retinex theory [15], frequency-domain [11] and machine learning [16].…”