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Image denoising via a non-local patch graph total variation.

Yan Zhang1,2, Jiasong Wu1,2,3, Youyong Kong1,2,3

  • 1LIST, the Key Laboratory of Computer Network and Information Integration, Southeast University, Ministry of Education, Nanjing, China.

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Summary
This summary is machine-generated.

This study introduces a new image denoising method, non-local patch graph total variation (NPGTV), which effectively preserves image details. NPGTV overcomes limitations of traditional methods by combining graph signal processing with total variation for superior denoising results.

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Area of Science:

  • Image Processing
  • Computer Vision
  • Signal Processing

Background:

  • Total variation (TV) models are popular for image denoising but can cause over-smoothing, losing image details.
  • Local TV methods struggle with edge and texture preservation, while non-local TV methods are computationally expensive.

Purpose of the Study:

  • To develop an effective image denoising method that preserves image details and overcomes the limitations of existing TV-based approaches.
  • Introduce the non-local patch graph total variation (NPGTV) method for improved image denoising.

Main Methods:

  • Constructing a K-nearest graph from the original image using non-local patch-based methods.
  • Combining total variation with graph signal processing to create the NPGTV model.
  • Solving the NPGTV model using the Douglas-Rachford Splitting algorithm.

Main Results:

  • The NPGTV method effectively preserves image details during the denoising process.
  • Experimental results demonstrate the superiority of NPGTV compared to classical TV, NLM, NLGBT, and AGTV methods.
  • NPGTV achieves effective denoising while maintaining image fidelity.

Conclusions:

  • The proposed NPGTV method offers an effective solution for image denoising, balancing detail preservation and noise reduction.
  • NPGTV represents a significant advancement by integrating graph signal processing with total variation for enhanced image restoration.