Effect of Image Noise Removal Using Bottleneck Attention Mechanism

In computer vision applications, image enhancement is important for improving image quality and extracting meaningful information. Noise removal is a commonly used technique in image enhancement. In this study, the Batch Renormalization Denoising Network (BRDNet), which performs well in noise removal, is used as the base model with the use of the Bottleneck Attention Module (BAM) to achieve performance improvement. The proposed method is tested on different datasets with different noise levels and their results are compared. In quantitative experiments, an increase in the PSNR metric value was observed and the visual results were found to be closer to the target images.