# Research on Image Super-Resolution Reconstruction Mechanism based on Convolutional Neural Network

arXiv: https://arxiv.org/abs/2407.13211 · HTML: https://mlsanity.com/paper/2407.13211/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 11 references that are in the map.
- First posted on arXiv 18 Jul 2024.

## Built on by (most cited first, 8 of 8)

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- ["Humor, Art, or Misinformation?": A Multimodal Dataset for Intent-Aware Synthetic Image Detection](https://mlsanity.com/paper/2508.20670/) (2025)
- [Latent Reconstruction from Generated Data for Multimodal Misinformation Detection](https://mlsanity.com/paper/2504.06010/) (2025)
- [A Multiscale Gradient Fusion Method for Edge Detection in Color Images Utilizing the CBM3D Filter](https://mlsanity.com/paper/2408.14013/) (2024)

## Abstract (arXiv)

Super-resolution reconstruction techniques entail the utilization of software algorithms to transform one or more sets of low-resolution images captured from the same scene into high-resolution images. In recent years, considerable advancement has been observed in the domain of single-image super-resolution algorithms, particularly those based on deep learning techniques. Nevertheless, the extraction of image features and nonlinear mapping methods in the reconstruction process remain challenging for existing algorithms. These issues result in the network architecture being unable to effectively utilize the diverse range of information at different levels. The loss of high-frequency details is significant, and the final reconstructed image features are overly smooth, with a lack of fine texture details. This negatively impacts the subjective visual quality of the image. The objective is to recover high-quality, high-resolution images from low-resolution images. In this work, an enhanced deep convolutional neural network model is employed, comprising multiple convolutional layers, each of which is configured with specific filters and activation functions to effectively capture the diverse features of the image. Furthermore, a residual learning strategy is employed to accelerate training and enhance the convergence of the network, while sub-pixel convolutional layers are utilized to refine the high-frequency details and textures of the image. The experimental analysis demonstrates the superior performance of the proposed model on multiple public datasets when compared with the traditional bicubic interpolation method and several other learning-based super-resolution methods. Furthermore, it proves the model's efficacy in maintaining image edges and textures.