GIFM: An Image Restoration Method With Generalized Image Formation Model for Poor Visible Conditions

Recently, image restoration has attracted considerable attention from researchers, and these methods generally restore degraded images based on the atmospheric scattering model (ATSM) and retinex model (RM). The two models only take into the single attenuation process during imaging, thereby introducing undesirable results. To deal with this issue, we propose an image restoration method based on a generalized image formation model (GIFM). First, unlike the existing image restoration methods, we rebuild a novel image formation model, which describes the light attenuation process that includes the light source-scene path and scene-sensor path. Second, we construct an objective optimization function to decompose a degraded image into a color distorted component and color corrected component, and an augmented Lagrange multiplier-based alternating direction minimization algorithm is provided to solve the optimization problem. Finally, we fully consider the advantages of the small-scale neighborhood and large-scale neighborhood in image restoration, and an image itself brightness-based weighted fusion strategy is proposed to balance brightness enhancement and contrast improvement. Extensive experiments on three image enhancement datasets show that our GIFM achieves better results than state-of-the-art methods. Experiments further suggest that our GIFM performs well for image restoration of extreme scenes, keypoint detection, object detection, and image segmentation.