Publications

You can also find my articles on my Google Scholar profile.

Journal Articles

Vision Transformer and FFT-ReLU Fusion for Advanced Image Deblurring

Preprint, 2024

In this paper, we utilise the FFT-ReLU prior to enhance relevant frequency components using the Fast Fourier Transform (FFT) while applying ReLU sparsity to suppress noise. Our approach utilizes a Vision Transformer as a pre-processing model to generate a less blurry intermediate image by capturing both local and global features, which is then refined through FFT-ReLU, resulting in a sharp, high-quality output. Our experimental results demonstrate that our method consistently outperforms state-of-the-art image deblurring models, providing sharper and more visually compelling images.

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Conference Papers

Deblurring in the Wild: A Real-World Dataset from Smartphone High-Speed Videos

Under review, 2025

We introduce the largest real-world image deblurring dataset constructed from smartphone slow-motion videos. Using 240 frames captured over one second, we simulate realistic long-exposure blur by averaging frames to produce blurry images, while using the temporally centered frame as the sharp reference. Our dataset contains over 42,000 high-resolution blur-sharp image pairs, making it approximately 10 times larger than widely used datasets, with 8 times the amount of different scenes, including indoor and outdoor environments, with varying object and camera motions. We benchmark multiple state-of-the-art (SOTA) deblurring models on our dataset and observe significant performance degradation, highlighting the complexity and diversity of our benchmark. Our dataset serves as a challenging new benchmark to facilitate robust and generalizable deblurring models.

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Blind Image Deblurring With FFT-ReLU Sparsity Prior

IEEE/CVF Winter Conference on Applications of Computer Vision, 2025

The paper introduces a method for blind image deblurring, which is the process of recovering a sharp image from a blurred one without prior knowledge about the blur kernel. The proposed method leverages a prior that targets the blur kernel to achieve effective deblurring across a wide range of image types. The authors' extensive empirical analysis shows that their algorithm achieves results that are competitive with the state-of-the-art blind image deblurring algorithms, and it offers up to two times faster inference, making it a highly efficient solution.

Recommended citation: Abdul Mohaimen Al Radi, Prothito Shovon Majumder, & Md. Mosaddek Khan. (2024). Blind Image Deblurring with FFT-ReLU Sparsity Prior.
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