Sadbhawna
I'm working as an Assistant Professor in the CSE Department at MNIT Jaipur since January 2024. Before that I was an Institute Post Doctoral Fellow at IIT Madras. I recieved my Ph.D. from the CSE Department at IIT Jammu advised by Dr. Vinit Jakhetiya in January 2023.
I primarily work in the area of Image Processing, Computer Vison and Machine Learning. My main focus includes analyzing and enhancing the perceptual quality of super-resolved images/videos, 3D synthesized views.
I completed my M.Tech. from SLIET Longowal in 2018 and B.Tech from HPTU in 2016.
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News
19/02/2024- One paper has been accepted in IEEE Transactions on Multimedia.
11/01/2024- I have joined MNIT Jaipur as an Assistant Professor.
16/04/2023- I have joined IIT Madras as an Institute Post Doctoral Fellow.
05/04/2023- One paper has been accepted in CVPR Workshops 2023 (NTIRE: New trends in image restoration and enhancement).
07/01/2023- I successfully defended my Ph.D. Thesis.
04/09/2022- One paper has been accepted in IEEE Transactions on Multimedia.
15/03/2022- Started working with Dr. Sunil Jaiswal, Head R&D, K|Lens GmbH
25/12/2021- Selected as PIEF candidate by IGSTC for a 6 months internship in Germany. (Results Link)
30/11/2021- One paper has been accepted in AAAI Student Abstract Program.
04/10/2021- Two papers has been accepted in IEEE Transactions on Image Processing.
15/06/2021- Our team has been awarded 600 USD sponsored by Google in ICASSP 2021 SPGC Grand Challenge on being Runner Ups.
Teaching
Spring 2024- CST310 Computer Graphics Course Website
Spring 2024- CS Computer Organization and Architecture Course Website
Publications
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Expanding Synthetic Real-World Degradations for Blind Video Super Resolution
Mehran Jeelani*,
Sadbhawna Thakur*,
Noshaba Cheema,
Klaus Illgner,
Philipp Slusallek,
Sunil Jaiswal,
IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2023
Paper
This work shows how varied random degradations can contribute to learning an effective VSR model, especially for
real-world video artifacts.
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Context Region Identification based Quality Assessment of 3D Synthesized Views
Sadbhawna Thakur,
Vinit jakhetiya,
Badri N. Subudhi,
Sunil Jaiswal,
Leida Li,
Weisi Lin,
IEEE Transactions on Multimedia, 2022
Paper
In this work, we propose a new and efficient quality assessment algorithm based upon the variation in the depth of 3D synthesized and reference views.
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Do we need a new large-scale quality assessment database for Generative Inpainting based 3D View Synthesis ? (Student Abstract)
Sadbhawna Thakur,
Vinit jakhetiya,
Badri N. Subudhi,
Harshit Shakya,
Deebha Mumtaz
AAAI 2022
Paper
We created a test dataset to analyze the need for a new perceptual metric for 3D synthesized views.
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Shift Compensation and Cosine Similarity based Quality Assessment of 3D-Synthesized Images
Sadbhawna Thakur,
Vinit jakhetiya,
Shubham Chaudhary,
Badri N. Subudhi,
Sharadh Chandra Guntuku,
Weisi Lin,
IEEE Transactions on Image Processing, 2021
project page
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Paper
In this work, we extract the perceptually important deep features from the pre-trained VGG-16 architectures on the Laplacian pyramid to predict the quality of 3D synthesized views.
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Stretching Artifacts Identification for Quality Assessment of 3D-Synthesized Views
Sadbhawna Thakur,
Vinit jakhetiya,
Sharadh Chandra Guntuku,
Deebha Mumtaz,
Badri N. Subudhi,
IEEE Transactions on Image Processing, 2021
project page
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Paper
We proposed a Convolutional Neural Network (CNN) based algorithm that identifies the blocks with stretching artifacts and further incorporates the number of blocks with the stretching artifacts to predict the quality of 3D-synthesized views.
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Detecting COVID-19 and Community Acquired Pneumonia using Chest CT Scan Images with Deep Learning
Shubham Chaudhary,
Sadbhawna Thakur,
Vinit jakhetiya,
Badri N. Subudhi,
Ujjwal Baid,
Sharadh Chandra Guntuku
ICASSP, 2021
project page
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Paper
We proposed a two-stage Convolutional Neural Network (CNN) based classification framework for detecting COVID-19 and Community Acquired Pneumonia (CAP) using the chest Computed Tomography (CT) scan images
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Distortion Specific Contrast Based No-Reference Quality Assessment of DIBR-Synthesized Views
Sadbhawna Thakur,
Vinit jakhetiya,
Deebha Mumtaz,
Sunil Jaiswal
MMSP 2020, 2021
project page
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Paper
We proposed a perceptual metric for 3D views based on the difference in propoerties of synthetic and natural images.
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Last updated on January 11, 2024 | Thanks Dr. Jonathan T. Barron for this awesome template.
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