Revisiting Image Deblurring with an Efficient ConvNet (2302.02234v1)

Abstract: Image deblurring aims to recover the latent sharp image from its blurry counterpart and has a wide range of applications in computer vision. The Convolution Neural Networks (CNNs) have performed well in this domain for many years, and until recently an alternative network architecture, namely Transformer, has demonstrated even stronger performance. One can attribute its superiority to the multi-head self-attention (MHSA) mechanism, which offers a larger receptive field and better input content adaptability than CNNs. However, as MHSA demands high computational costs that grow quadratically with respect to the input resolution, it becomes impractical for high-resolution image deblurring tasks. In this work, we propose a unified lightweight CNN network that features a large effective receptive field (ERF) and demonstrates comparable or even better performance than Transformers while bearing less computational costs. Our key design is an efficient CNN block dubbed LaKD, equipped with a large kernel depth-wise convolution and spatial-channel mixing structure, attaining comparable or larger ERF than Transformers but with a smaller parameter scale. Specifically, we achieve +0.17dB / +0.43dB PSNR over the state-of-the-art Restormer on defocus / motion deblurring benchmark datasets with 32% fewer parameters and 39% fewer MACs. Extensive experiments demonstrate the superior performance of our network and the effectiveness of each module. Furthermore, we propose a compact and intuitive ERFMeter metric that quantitatively characterizes ERF, and shows a high correlation to the network performance. We hope this work can inspire the research community to further explore the pros and cons of CNN and Transformer architectures beyond image deblurring tasks.

Analysis of the LaTeX Structure in Academic Paper Drafts

This document represents a LaTeX manuscript, outlining a foundational structure commonly utilized in academic papers. The LaTeX template includes predefined commands and settings aimed at facilitating the production of well-formatted scientific documents. Although no actual content or experimental data is provided within this specific document draft, the configuration components outlined here reflect a preparation stage for complex academic authorship in fields reliant on technical rigor, such as computer science or mathematics.

Key Elements of the LaTeX Template

1. Document Class and Formatting:
   • The template employs the article class, specified with options like 10pt, twocolumn, and letterpaper. This selection supports readability and layout preferences typical in conference proceedings and journal articles.
2. Mathematical Operators:
   • It defines mathematical operators such as \argmax and \argmin, which are critical in optimization problems prevalent in machine learning and operations research.
3. Commands for Norm and Absolute Value:
   • The template includes declarations for norm (\norm) and absolute value (\abs) indicators, leveraging paired delimiters. This function is essential for expressions involving vector magnitudes and deviations that appear frequently in quantitative research.
4. Table Formatting Settings:
   • Custom column types (P and M) and enhancements to header and cell alignment improve the presentation of tabular data, ensuring clarity in data-intensive sections.
5. Cross-Referencing and Section Formatting:
   • Employing the cleveref package, it facilitates precise referencing, enhancing navigation within complex documents. This functionality is significant for readers seeking rapid access to specific sections or data tables.
6. Structured Document Organization:
   • \subfile commands indicate the integration of separate source files for content, promoting modular writing and collaboration among authors. This is particularly advantageous in multi-author papers, where sections may be concurrently developed.

Implications and Future Considerations

Academic documentation requirements continue to evolve with the needs for clarity and precision in presenting scientific results. This LaTeX template represents a scaffold adaptable to a range of academic contexts and is designed to support structured output characteristic of high-impact scientific communication.

For the artificial intelligence community, such templates remain critical as researchers increasingly rely on complex mathematical expressions and data-driven insights. The integration of such templates with tools like Overleaf further empowers researchers by providing a collaborative environment facilitated by version control and instant rendering capabilities.

In future developments, there may be an increased emphasis on enhancing LaTeX templates to support interactive elements or automated validation of content, assisting researchers in maintaining accuracy and reproducibility of their scientific discourse. Additionally, given the rise of interdisciplinary research, future templates might incorporate a broader array of packages supporting diverse data and graph visualizations, thereby bridging the gap between textual narratives and data interpretation.