A guided optimized recursive least square adaptive filtering based multi-variate dense fusion network model for image interpolation

Recently, learning-based image interpolation methods have gained significant popularity in the field of surveillance image processing due to their promising results. Notably, deep neural networks have shown considerable improvements in image super-resolution. To enhance the performance of image interpolation for surveillance images, researchers often employ deep convolutional neural Networks. However, merely increasing the network depth may not lead to substantial improvements and could even introduce new training-related challenges, necessitating novel training approaches. Therefore, in this proposed work, a new deep learning-based model is developed specifically tailored for effective surveillance image interpolation. The approach begins with the Optimized Recursive Least Square Adaptive Filter (ORLSAF) technique for image filtering. This step involves calculating the error signal and estimating weight factors to generate noise-free images. To reconstruct the interpolated surveillance images, the innovative Multi-Variate Dense Fusion Network (MVDFN) methodology is utilized, which incorporates feature fusion, augmentation, and loss regularization processes. Particularly, the loss factor is optimally calculated using the Hybrid Butterfly Optimization (HBO) algorithm. To evaluate the performance of the proposed technique, extensive experiments are conducted using a benchmarking dataset commonly employed in surveillance image processing. The evaluation metrics used include Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), loss factor, and normalized similarity index. Overall, this research aims to advance surveillance image interpolation using a novel deep learning-based approach, combining ORLSAF, MVDFN, and the HBO algorithm to achieve superior results compared to existing methods. The potential impact of this work includes enhancing the quality and clarity of surveillance images, contributing to improved surveillance systems and applications.