Application of convolutional neural networks in image super-resolution

2025-06-03

AI Summary

Existing CNN-based upsampling methods for image super-resolution lack systematic comparative analysis and hardware-aware evaluation. Method: This paper establishes a unified experimental framework to conduct the first horizontal benchmarking of mainstream upsampling modules—including bicubic interpolation, transposed convolution, sub-pixel convolution, and meta-upsampling—across reconstruction quality (PSNR/SSIM), computational efficiency, and hardware resource utilization. Empirical evaluations are performed across diverse hardware platforms (edge and cloud). Contribution/Results: The study quantitatively characterizes performance ceilings and deployment constraints of each method, yielding hardware-aware model selection guidelines for edge and cloud scenarios. It clarifies theoretical mechanisms and practical trade-offs among upsampling paradigms and fills a critical gap in systematic, cross-platform adaptability analysis for deep super-resolution. The findings provide an empirical foundation and design direction for future lightweight and adaptive upsampling architectures.

Technology Category

• Machine Learning: Hardware-aware ML
• Computer Vision: Multi-modal Vision
• Search and Optimization: Metareasoning and Metaheuristics

Application Category

• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Systems and Infrastructure for Web, Mobile and WoT: Cloud, edge and content delivery systems for the Web
• Economics, Online Markets and Human Computation: Economics and fairness of platforms and recommendation systems

Abstract

Due to strong learning abilities of convolutional neural networks (CNNs), they have become mainstream methods for image super-resolution. However, there are big differences of different deep learning methods with different types. There is little literature to summarize relations and differences of different methods in image super-resolution. Thus, summarizing these literatures are important, according to loading capacity and execution speed of devices. This paper first introduces principles of CNNs in image super-resolution, then introduces CNNs based bicubic interpolation, nearest neighbor interpolation, bilinear interpolation, transposed convolution, sub-pixel layer, meta up-sampling for image super-resolution to analyze differences and relations of different CNNs based interpolations and modules, and compare performance of these methods by experiments. Finally, this paper gives potential research points and drawbacks and summarizes the whole paper, which can facilitate developments of CNNs in image super-resolution.

Problem

• Summarize differences among CNN-based image super-resolution methods
• Compare performance of various interpolation techniques in super-resolution
• Identify research gaps and challenges in CNN super-resolution approaches

Innovation

• Uses CNNs for image super-resolution
• Compares various interpolation methods
• Analyzes performance through experiments