5 research outputs found
Physical-based optimization for non-physical image dehazing methods
Images captured under hazy conditions (e.g. fog, air pollution) usually present faded colors and loss of contrast. To improve their visibility, a process called image dehazing can be applied. Some of the most successful image dehazing algorithms are based on image processing methods but do not follow any physical image formation model, which limits their performance. In this paper, we propose a post-processing technique to alleviate this handicap by enforcing the original method to be consistent with a popular physical model for image formation under haze. Our results improve upon those of the original methods qualitatively and according to several metrics, and they have also been validated via psychophysical experiments. These results are particularly striking in terms of avoiding over-saturation and reducing color artifacts, which are the most common shortcomings faced by image dehazing methods
Color Image Enhancement Method Based on Weighted Image Guided Filtering
A novel color image enhancement method is proposed based on Retinex to
enhance color images under non-uniform illumination or poor visibility
conditions. Different from the conventional Retinex algorithms, the Weighted
Guided Image Filter is used as a surround function instead of the Gaussian
filter to estimate the background illumination, which can overcome the
drawbacks of local blur and halo artifact that may appear by Gaussian filter.
To avoid color distortion, the image is converted to the HSI color model, and
only the intensity channel is enhanced. Then a linear color restoration
algorithm is adopted to convert the enhanced intensity image back to the RGB
color model, which ensures the hue is constant and undistorted. Experimental
results show that the proposed method is effective to enhance both color and
gray images with low exposure and non-uniform illumination, resulting in better
visual quality than traditional method. At the same time, the objective
evaluation indicators are also superior to the conventional methods. In
addition, the efficiency of the proposed method is also improved thanks to the
linear color restoration algorithm.Comment: 15 page