Image Denoising Using Hybrid Deep Learning Approach and Self-Improved Orca Predation Algorithm

• Published in: Technologies
• Main Authors: Rusul Sabah Jebur, Mohd Hazli Bin Mohamed Zabil, Dalal Abdulmohsin Hammood, Lim Kok Cheng, Ali Al-Naji
• Format: Text
• Language: English
• Published: Multidisciplinary Digital Publishing Institute 2023
• Subjects: image denoising; deep learning; Bi-LSTM; CNN; SI-OPA; Orca
• Online Access: https://doi.org/10.3390/technologies11040111

Image denoising is a critical task in computer vision aimed at removing unwanted noise from images, which can degrade image quality and affect visual details. This study proposes a novel approach that combines deep hybrid learning with the Self-Improved Orca Predation Algorithm (SI-OPA) for image denoising. Leveraging Bidirectional Long Short-Term Memory (Bi-LSTM) and optimized Convolutional Neural Networks (CNN), the hybrid model aims to enhance denoising performance. The CNN’s weights are optimized using SI-OPA, resulting in improved denoising accuracy. Extensive comparisons against state-of-the-art denoising methods, including traditional algorithms and deep learning-based techniques, are conducted, focusing on denoising effectiveness, computational efficiency, and preservation of image details. The proposed approach demonstrates superior performance in all aspects, highlighting its potential as a promising solution for image-denoising tasks. Implemented in Python, the hybrid model showcases the benefits of combining Bi-LSTM, optimized CNN, and SI-OPA for advanced image-denoising applications.