S
Srimanta Mandal
Researcher at Dhirubhai Ambani Institute of Information and Communication Technology
Publications - 29
Citations - 199
Srimanta Mandal is an academic researcher from Dhirubhai Ambani Institute of Information and Communication Technology. The author has contributed to research in topics: Sparse approximation & Computer science. The author has an hindex of 7, co-authored 23 publications receiving 135 citations. Previous affiliations of Srimanta Mandal include Indian Institute of Chemical Technology & Indian Institute of Technology Madras.
Papers
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Journal ArticleDOI
Noise adaptive super-resolution from single image via non-local mean and sparse representation
TL;DR: A robust super-resolution algorithm which adapts itself based on the noise-level in the image, which demonstrates better efficacy for optical and range images under different types and strengths of noise.
Journal ArticleDOI
Depth Map Restoration From Undersampled Data
TL;DR: This paper proposes a new approach to address issues in a unified framework of depth map restoration, based on sparse representation, and suggests an alternative method of reconstructing dense depth map from very sparse non- uniformly sampled depth data by sequential cascading of uniform and non-uniform upsampling techniques.
Proceedings ArticleDOI
Edge preserving single image super resolution in sparse environment
Srimanta Mandal,Anil Kumar Sao +1 more
TL;DR: An edge preserving constraint is proposed, which preserve the edge information of image by minimizing the differences between edges of LR image and the edges of the reconstructed image (down-sampled version), in sparse coding based SR problem.
Journal ArticleDOI
Mixed-dense connection networks for image and video super-resolution
TL;DR: A deep architecture for single image super-resolution (SISR) is proposed, built using efficient convolutional units the authors refer to as mixed-dense connection blocks (MDCB), which combines the strengths of both residual and dense connection strategies, while overcoming their limitations.
Journal ArticleDOI
Local Proximity for Enhanced Visibility in Haze
TL;DR: The approach is developed based on the assumption that for outdoor scenes, the depth of patches changes gradually in a local neighborhood surrounding the patch, and this change in depth can be approximated by patch similarity in that neighborhood.