Himanshu Kumar

Himanshu Kumar
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Himanshu verified their affiliation via an institutional email.
Verified
Himanshu verified their affiliation via an institutional email.
  • Visvesvaraya Fellow
  • PhD Student at Central Electronics Engineering Research Institute

About

10
Publications
561
Reads
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25
Citations
Introduction
My research primarily focuses on Image Synthesis, Generation, and addressing inverse problems. I am a graduate with an M.Tech. in Advanced Electronics from the Academy of Scientific and Innovative Research, India, ranked 12th in NIRF.
Current institution
Central Electronics Engineering Research Institute
Current position
  • PhD Student
Additional affiliations
December 2022 - present
Central Electronics Engineering Research Institute
Position
  • Project Associate

Publications

Publications (10)
Preprint
Full-text available
Detecting and interpreting operator actions, engagement, and object interactions in dynamic industrial workflows remains a significant challenge in human-robot collaboration research, especially within complex, real-world environments. Traditional unimodal methods often fall short of capturing the intricacies of these unstructured industrial settin...
Article
Full-text available
Image colorization is a fascinating application of AI for information restoration. The inherently ill-posed nature of the problem increases the challenge since the outputs could be multimodal. Existing learning-based methods produce acceptable results for straightforward cases but usually fail to restore the contextual information without clear fig...
Chapter
Lensless image reconstruction is an ill-posed inverse problem in computational imaging, having several applications in machine vision. Existing approaches rely on large datasets for learning to perform deconvolution and are often specific to the point spread function of a particular lensless imager. Generating pairs of lensless images and their cor...
Preprint
Full-text available
Grayscale image colorization is a fascinating application of AI for information restoration. The inherently ill-posed nature of the problem makes it even more challenging since the outputs could be multi-modal. The learning-based methods currently in use produce acceptable results for straightforward cases but usually fail to restore the contextual...

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