Removal of Salt and Pepper Noise Using Hybrid Adaptive Switching Median Filter with Ant Colony Optimization Technique in Nano Electronic Applications

Abstract

Background: Restoration of noisy images from the salt and pepper noise is an interesting area in the field of image processing. The restoration process can be done using various filtering algorithms. The restoration process should not affect the pixels of the original image. The problem of the existing work persists as the increase in the error rate while the dimensions as well as the image format changes. The proposed work consists of Hybrid Adaptive Switching median filtering (HASMF). The hybrid technique corrupted images’ high-density salt and pepper noise removal using Ant colony Optimization technique. This hybrid technique would remove the high-density salt and pepper noise from the corrupted images. The noisy pixel value from the corrupted images is identified and selected using the Ant Colony Optimization technique (ACO). The identified corrupted value can be replaced using the Adaptive Switching Median Filter. The switching process is carried out using the pixel by pixel with the normalized median values. The noisy pixels are identified and selected using Ant colony Optimization. The optimized values are subjected to the filtering process. The proposed method decreases the salt and pepper noise within the original image. The hybrid design approach was used in the proposed study, which used 45 nm technology combined with a Verilog-A model-based circuit that was implemented using Spintronic. It was discovered that the suggested changed task had less latency, used less space, and dissipated less power than the original. Furthermore, it was discovered that designed memory arrays were both energy and space-efficient. It does not affect the normal pixels within the original image. The comparison process has been made with the various existing algorithms such as Median Filter (MF), Modified Decision Based Unsymmetrical Trimmed Median Filter (MDBUTMF). The proposed method has overcome the various performance metrics such as Peak Signal Noise Ratio (PSNR), Mean Square Error (MSE), and Structural Similarity Index (SSIM). The results obtained have shown the significant results in terms of object measures as well as visual perception of the denoised image.