Sewer Image Super-Resolution with Depth Priors and Its Lightweight Network
Gang Pan, Chen Wang, Zhijie Sui, Shuai Guo, Yaozhi Lv, Honglie Li, Di, Sun, Zixia Xia

TL;DR
This paper introduces DSRNet, a depth-guided super-resolution framework for sewer images that leverages depth priors and knowledge distillation to enhance image quality and downstream task performance efficiently.
Contribution
The study proposes a novel depth-guided super-resolution network with a lightweight model using knowledge distillation, specifically designed for sewer image enhancement.
Findings
DSRNet significantly improves PSNR and SSIM over existing methods.
The approach enhances sewer defect detection, segmentation, and classification accuracy.
The lightweight model maintains high performance with reduced computational cost.
Abstract
The Quick-view (QV) technique serves as a primary method for detecting defects within sewerage systems. However, the effectiveness of QV is impeded by the limited visual range of its hardware, resulting in suboptimal image quality for distant portions of the sewer network. Image super-resolution is an effective way to improve image quality and has been applied in a variety of scenes. However, research on super-resolution for sewer images remains considerably unexplored. In response, this study leverages the inherent depth relationships present within QV images and introduces a novel Depth-guided, Reference-based Super-Resolution framework denoted as DSRNet. It comprises two core components: a depth extraction module and a depth information matching module (DMM). DSRNet utilizes the adjacent frames of the low-resolution image as reference images and helps them recover texture information…
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Taxonomy
TopicsAdvanced Image Processing Techniques · Image and Signal Denoising Methods · Image Enhancement Techniques
MethodsSoftmax · Attention Is All You Need · Knowledge Distillation
