OptiMD-3D DCNN: A Framework for Restoring the Haze-Free Images by Image Dehazing Techniques Using Heuristic Approach of Adaptive Lifting Wavelet Transform

Abstract

The recently implemented illustrative techniques are focused to restore haze-free images by using learning approaches and also physical models. Managing finer details of the images when completely removing the haze is a complicated task especially in performing the hazing on single images. Generally, hazy images comprise distorted colors, poor contrast, and lower visibility, which lead to lower efficiency in the recognition, detection of objects, and classification of images. The main aspect of the proposed work is to restore haze-free images without any degradation in image quality. Initially, the required raw hazy images are acquired from the benchmark data sources. Further, these images undergo the decomposing stage, which is accomplished by adopting the Adaptive Lifting Wavelet Transform (ALWT), where the wavelet levels are optimized by using the improved hybrid algorithm of the Modified Tunicate Galactic Swarm Optimization (MTGSO). Once the decomposed images are obtained, Optimal Multiscale Dilation assisted 3D Deep Convolutional Neural Network (OptiMD-3D DCNN) is developed for the image dehazing task, in which the hyperparameters are optimally tuned using a hybrid MTGSO algorithm. At last, the effectiveness is examined and guaranteed that the recommended scheme shows higher performance in estimating with the classical methods.