Search Results for author: K. Ram Prabhakar

Found 4 papers, 1 papers with code

Segmentation Guided Deep HDR Deghosting

no code implementations4 Jul 2022 K. Ram Prabhakar, Susmit Agrawal, R. Venkatesh Babu

Our motion segmentation guided HDR fusion approach offers significant advantages over existing HDR deghosting methods.

Motion Segmentation

Self-Gated Memory Recurrent Network for Efficient Scalable HDR Deghosting

no code implementations24 Dec 2021 K. Ram Prabhakar, Susmit Agrawal, R. Venkatesh Babu

In the SGM cell, the information flow through a gate is controlled by multiplying the gate's output by a function of itself.

Labeled From Unlabeled: Exploiting Unlabeled Data for Few-Shot Deep HDR Deghosting

no code implementations CVPR 2021 K. Ram Prabhakar, Gowtham Senthil, Susmit Agrawal, R. Venkatesh Babu, Rama Krishna Sai S Gorthi

To derive data for the next stage of training, we propose a novel method for generating corresponding dynamic inputs from the predicted HDRs of unlabeled data.

Few-Shot Learning

DeepFuse: A Deep Unsupervised Approach for Exposure Fusion with Extreme Exposure Image Pairs

1 code implementation ICCV 2017 K. Ram Prabhakar, V. Sai Srikar, R. Venkatesh Babu

To address the above issues, we have gathered a large dataset of multi-exposure image stacks for training and to circumvent the need for ground truth images, we propose an unsupervised deep learning framework for MEF utilizing a no-reference quality metric as loss function.

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