A Novel Image Super-Resolution Approach for Industrial Product Visual Enhancement

This study proposes a novel approach for image super-resolution (SR) that combines deep learning algorithms, adaptive multi-path structures, and classification models. The goal is to enhance industrial product details, improve visual quality, and achieve better quantitative metrics such as PSNR and SSIM. The proposed multi-path super-resolution algorithm utilizes a big-size convolution kernel and residual network to extract features at different scales, enabling the capture and enhancement of fine details in low-resolution images. Two different loss functions are incorporated to improve the visual quality and fidelity of the SR images. Furthermore, the integration of a super-resolution model with a ResNet-18 classification model enhances image clarity, detail retention, and overall performance,. The experimental results demonstrate that our proposed super-resolution (SR) algorithm outperforms several typical methods. Additionally, incorporating the ResNet-18 classification model improves the performance of the model on the NEU-CLS dataset, achieving higher accuracy, recall, precision, and F1-score compared to the original dataset.