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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

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A PSO-optimized self-supervised convolutional network for robust image watermark removal.

Taghi Javdani Gandomani¹, Maryam Karimi², Mahdi Mosleh²

¹Depatment of Computer Science, Faculty of Mathematical Sciences, Shahrekord University, Shahrekord, Iran. javdani@sku.ac.ir.

Scientific Reports

|April 19, 2026

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This study introduces an enhanced self-supervised convolutional neural network (CNN) for effective image watermark removal. Particle Swarm Optimization (PSO) improves restoration quality and convergence speed for research and archival needs.

Area of Science:

Computer Vision
Digital Image Processing
Machine Learning

Background:

Images are increasingly vital for professional and recreational use.
Watermarks protect copyright but are difficult to remove, even for restoration.
Existing watermark removal methods require manual tuning and complex optimization.

Purpose of the Study:

To present an improved self-supervised convolutional neural network (CNN) for image watermark removal.
To enhance the CNN with Particle Swarm Optimization (PSO) for hyperparameter tuning.
To achieve faster convergence and higher restoration quality without clean reference images.

Main Methods:

Developed a self-supervised convolutional neural network (CNN) for watermark removal.
Integrated Particle Swarm Optimization (PSO) for automated hyperparameter tuning.

Keywords:

Convolutional Neural Network (CNN)Deep Learning Image Restoration Image Watermark Removal Particle Swarm Optimization (PSO)Self-Supervised Learning

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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Utilized self-supervised learning to generate training data, eliminating the need for clean images.

Main Results:

The proposed PSO-SWCNN achieved superior performance on the PASCAL VOC 2012 dataset.
Demonstrated the highest Structural Similarity Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR) compared to state-of-the-art methods.
Validated the effectiveness of self-supervised learning and PSO for watermark removal.

Conclusions:

The integrated PSO-SWCNN offers an efficient and effective solution for image watermark removal.
This method facilitates image restoration for research and archival purposes.
The approach overcomes limitations of traditional manual optimization techniques.