Prothito Shovon Majumder

Prothito Shovon Majumder

Adjunct Lecturer, BRACU.
RA at CAIL, DU.
CS Graduate, University of Dhaka.

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Posts

Hands-On Deep Learning and Open-source Large Language Model Workshop

less than 1 minute read

Published:

The Cognitive Agents and Interaction Lab at University of Dhaka arranged a workshop on deep learning and open-source LLMs, where I was one of the presenters and trainers. The contents discussed in the workshop included multilayer perceptrons, neural networks and their implementations in PyTorch, transformer architectures, attention mechanisms, LLM fine-tuning and deployments.

portfolio

Apellai

A subsonic client, built using Kotlin, for storing, filtering, searching music libraries and podcasts in servers, with additional options for like/dislike, media controls and server switching.

Deversorium

Built with MERN stack, a web application for managing the residence and meal system for hostels, with separate interfaces for tenants and owners.

Habitrix

A Flutter application for tracking progress of forming new habits and visualising over defined periods of time, with additional features of priority-based task scheduling.

publications

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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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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talks

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

This is a description of a teaching experience. You can use markdown like any other post.

Teaching experience 2

Workshop, University 1, Department, 2015

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