Assessing The Impact of CNN Auto Encoder-Based Image Denoising on Image
  Classification Tasks
Mohsen Hami Mahdi JameBozorg
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 and defect status. The research
process begins with preprocessing images, followed by applying denoising
techniques tailored to specific noise categories. The goal is to enhance the
accuracy and robustness of defect detection by integrating noise detection and
denoising into the classification pipeline. The study achieved remarkable
results using VGG16 for noise type classification in the frequency domain,
achieving an accuracy of over 99%. Removal of salt and pepper noise resulted in
an average SSIM of 87.9, while Gaussian noise removal had an average SSIM of
64.0, and periodic noise removal yielded an average SSIM of 81.6. This
comprehensive approach showcases the effectiveness of the deep AutoEncoder
model and median filter, for denoising strategies in real-world industrial
applications. Finally, our study reports significant improvements in binary
classification accuracy for defect detection compared to previous methods. For
the VGG16 classifier, accuracy increased from 94.6% to 97.0%, demonstrating the
effectiveness of the proposed noise detection and denoising approach.
Similarly, for the InceptionV3 classifier, accuracy improved from 84.7% to
90.0%, further validating the benefits of integrating noise analysis into the
classification pipeline.