S
Soumik Sarkar
Researcher at Iowa State University
Publications - 288
Citations - 7113
Soumik Sarkar is an academic researcher from Iowa State University. The author has contributed to research in topics: Deep learning & Computer science. The author has an hindex of 31, co-authored 258 publications receiving 4542 citations. Previous affiliations of Soumik Sarkar include Indian Institute of Science & Raytheon.
Papers
More filters
Journal ArticleDOI
LLNet: A deep autoencoder approach to natural low-light image enhancement
TL;DR: In this paper, a deep autoencoder-based approach is proposed to identify signal features from low-light images and adaptively brighten images without over-amplifying/saturating the lighter parts in images with high dynamic range.
Posted Content
LLNet: A Deep Autoencoder Approach to Natural Low-light Image Enhancement
TL;DR: It is shown that a variant of the stacked-sparse denoising autoencoder can learn from synthetically darkened and noise-added training examples to adaptively enhance images taken from natural low-light environment and/or are hardware-degraded.
Journal ArticleDOI
Machine Learning for High-Throughput Stress Phenotyping in Plants
TL;DR: This work provides a comprehensive overview and user-friendly taxonomy of ML tools to enable the plant community to correctly and easily apply the appropriate ML tools and best-practice guidelines for various biotic and abiotic stress traits.
Journal ArticleDOI
Deep Learning for Plant Stress Phenotyping: Trends and Future Perspectives.
TL;DR: A comparative assessment of DL tools against other existing techniques, with respect to decision accuracy, data size requirement, and applicability in various scenarios is provided.
Journal ArticleDOI
An explainable deep machine vision framework for plant stress phenotyping.
Sambuddha Ghosal,David Blystone,Asheesh K. Singh,Baskar Ganapathysubramanian,Arti Singh,Soumik Sarkar +5 more
TL;DR: A machine learning framework’s ability to identify and classify a diverse set of foliar stresses in soybean with remarkable accuracy is demonstrated, and the learned model appears to be agnostic to species, seemingly demonstrating an ability of transfer learning.