Abstract
In this paper, we present a novel defogging technique, named CurL-Defog, with the aim of minimizing the insertion of artifacts while maintaining good contrast restoration and visibility enhancement. Many learning-based defogging approaches rely on paired data, where fog is artificially added to clear images; this usually provides good results on mildly fogged images but is not effective for difficult cases. On the other hand, the models trained with real data can produce visually impressive results, but unwanted artifacts are often present. We propose a curriculum learning strategy and an enhanced CycleGAN model to reduce the number of produced artifacts, where both synthetic and real data are used in the training procedure. We also introduce a new metric, called HArD (Hazy Artifact Detector), to numerically quantify the number of artifacts in the defogged images, thus avoiding the tedious and subjective manual inspection of the results. HArD is then combined with other defogging indicators to produce a solid metric that is not deceived by the presence of artifacts. The proposed approach compares favorably with state-of-the-art techniques on both real and synthetic datasets.
Highlights
Atmosphere 2021, 12, 577. https://Fog, mist, or haze may negatively affect the acquisition of digital photographs
In order to train a model with real images and at the same time reduce the artifacts inserted during the defogging, we propose CurL-Defog, an approach inspired by curriculum learning [13], where the model is first guided towards a desirable parameter-space region via pix2pix-like [34] training using a synthetic paired image
This last pre-processing step served as a sort of data augmentation procedure since the datasets were not composed of a high number of images
Summary
Atmosphere 2021, 12, 577. https://Fog, mist, or haze may negatively affect the acquisition of digital photographs. Images captured under bad weather conditions may suffer from limited visibility, poor contrast, faded colors, and a loss of sharpness. This makes the photographs esthetically less pleasant but can greatly decrease the accuracy of computer vision tasks such as object detection, tracking, classification, and segmentation [1], which constitute the basic blocks of challenging applications such as self-driving cars. Defogging (or dehazing) is the task of removing the fog from a given image, with the aim of reconstructing the same scene as if it were taken in good (or at least better) weather conditions. Defogging can be used as an independent operation in image enhancement tasks, with the aim of improving the visual quality of images; e.g., in photographs taken with smartphones or digital cameras
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