Abstract:
The light is scattered by air particles, which reduces the clarity of an image
taken in bad lighting conditions (such as haze, fog, mist, or smog). When a
single hazy image needs to be made more visible, single image dehazing (SID)
techniques are applied. Single image dehazing is a challenging problem due
to the ill-posed nature of the problem. Existing methods for single image
dehazing typically rely on atmospheric scattering models (ATSMs). How ever, ATSMs are often inaccurate and can lead to artifacts in the dehazed
images. The proposed method for single image dehazing uses a non-linear
bounding function (BF) to estimate the lower bound on the transmission of
a hazy image. The BF is a non-linear function that is estimated using a
training dataset of hazy and haze-free images. This function is used to com pute the lower bound on the transmission of a hazy image. The lower bound
on the transmission is then used to minimize the reconstruction error in the
dehazing process. The proposed method is implemented as an optimiza tion problem that is solved using a gradient descent algorithm.The proposed
method was evaluated on a number of benchmark datasets and showed that it
outperformed state-of-the-art methods. The results shows that the proposed
method outperformed state-of-the-art methods in terms of both accuracy and
robustness. This method also produced dehazed images that were visually
more appealing. The proposed method is a introducing a non linear bound ing function for single image dehazing. The experimental results shows that
the proposed method is a promising new approach for single image dehazing.
It is more accurate, more robust, and more visually appealing than existing
methods. The proposed method has the potential to be used in a variety of
applications where it can improve the visibility and quality of images.
