Adaptive discrete wavelet transform and optimized residual-based deep CNN for image dehazing with a new meta-heuristic algorithm

• Published in: Multimedia tools and applications 2024-02, Vol.83 (28), p.71335-71358
• Main Authors: Kumar, R. Prakash, Naik N, Manaja
• Format: Article
• Language: English
• Subjects: Adaptive algorithms; Artificial neural networks; Computer Communication Networks; Computer Science; Computer vision; Data Structures and Information Theory; Discrete Wavelet Transform; Haze; Heuristic methods; Image processing; Image restoration; Information retrieval; Internet resources; Multimedia Information Systems; Special Purpose and Application-Based Systems; Wavelet transforms
• ISSN: 1573-7721; 1380-7501; 1573-7721
• DOI: 10.1007/s11042-023-18098-4

Image dehazing is said to be an emerging research area in the platform of computer vision and image processing. Due to the cruel fog, air dispersion, and haze around the environment, the hazes images are resulted in different challenges in retrieving the actual information of the original image. On the other hand, the conventional approaches are ensured with the huge computational complexity and also with the distortion of actual images like over-saturation and halos. The recent methods are used for restoring the haze-free images however they are worked with the physical models and along with the learning methods. It is a very challenging task to maintain the detailed details of the image at the time of reducing the fog in the single-image dehazing. With an advanced development deep structured strategy, mostly Convolutional Neural Network (CNN)-aided dehazing approaches are developed for processing the single image dehazing. However, haze residual and slow training of the convergence rate are considered as the two main drawbacks in these conventional dehazing networks. To deal with these problems, the latest approach is proposed for the restoration of haze-free images. The hazy images are gathered from the standard datasets. At first, Adaptive Discrete Wavelet Transform (ADWT) is utilized for decomposing the images, where the ADWT is implemented by Hybrid African Vultures Fire Fly Optimization (HAVFFO). Further, image dehazing is designed by Optimized Residual-Based Deep CNN (OR-Deep CNN), where the hyperparameters of the Residual-Based Deep CNN are optimized by the same HAVFFO. Finally, the restoration of haze-free images is carried out through adaptive inverse DWT. Through the performance analysis, our recommended model is better in quantitative visual and performances on online resources.