Enhanced dragonfly optimization for stationary wavelet transform in image dehazing

Abstract

newline The rapid development of wireless network technology has changed how individuals now acquire and share multimedia information via mobile devices with cameras. However, based on the weather, atmospheric pollutants like dust, fog, smoke, mist, snow, and haze can negatively impact outdoor images taken with high-end cameras and reduce image quality. The scattered air particles have a direct impact on the quality of the images and videos shot outside. They often cause low contrast, colour fading, and limited vision. This makes it difficult to discern between the objects in the gathered fuzzy images or frames. Image dehazing has been studied in great detail to recover images impacted by fog or haze since it became significant in a surveillance system. Nevertheless, the colour, detail, and depth of image field cannot be smoothly integrated into the study that is now underway. To this goal, the proposed research project offers a dual contribution. In order to effectively retrieve the fog-free image, the first module suggests a Gamma correction optimization model with wavelet decomposition approach. Initially, the hazy images are subjected to Stationary Wavelet Transform (SWT) to preserve the temporal attributes of images and to mitigate loss of details.