Assessing The Impact of CNN Auto Encoder-Based Image Denoising on Image Classification Tasks

arXiv:2404.10664v1 Announce Type: cross
Abstract: Images captured from the real world are often affected by different types of noise, which can significantly impact the performance of Computer Vision systems and the quality of visual data. This study presents a novel approach for defect detection in casting product noisy images, specifically focusing on submersible pump impellers. The methodology involves utilizing deep learning models such as VGG16, InceptionV3, and other models in both the spatial and frequency domains to identify noise types …

abstract arxiv auto classification cnn computer computer vision cs.cv cs.lg data defect detection denoising detection eess.iv encoder image images impact noise novel performance product quality study systems tasks type types vision visual visual data world