Linear Fusion of Multi-Scale Transmissions for Image Dehazing
Subhash Chand Agrawal, Anand Singh Jalal · 2021
Images acquired in inclement weather conditions (haze, mist, fog, rain, etc.) suffer from various degradation problems such as low contrast, diminished visibility and color distortions. These low-quality images do not meet the demand for computer vision applications like object recognition, smart transportation, remote sensing, weather forecasting, etc. To restore the haze-free image, we require two parameters to estimate. The first parameter is an estimation of the transmission and second is the atmospheric light. Existing work focused on estimation of the transmission. However, two issues halo artifacts at the sudden change of the depth and over enhancement are unresolved in the dehazed image due to inaccurate estimation of the transmission. The traditional methods utilized two methods: pixel-wise and patch-wise while estimating the transmission. However, these two methods pixel-wise and patch-wise suffer from the problem of over-saturation or loss of details and halo artifacts in the dehazed images respectively. This paper suggests a linear fusion of multi-scale transmissions to overcome these problems. Experiments are performed on various hazy images and qualitative and quantitative results are presented. These qualitative and quantitative results reveal that the proposed method has successfully overcome these problems.