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20162025
most citedTowards an Automated Image De-fencing Algorithm Using Sparsity

2 citations · 3 across the 5 of their papers we have counts for

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7 papers · 1 filter

cs.CV2025

Dark Channel-Assisted Depth-from-Defocus from a Single Image

Moushumi Medhi, Rajiv Ranjan Sahay

We estimate scene depth from a single defocus-blurred image using the dark channel as a complementary cue, leveraging its ability to capture local statistics and scene structure. T…

cs.CV2024

Deep Generative Adversarial Network for Occlusion Removal from a Single Image

Sankaraganesh Jonna, Moushumi Medhi, Rajiv Ranjan Sahay

Nowadays, the enhanced capabilities of in-expensive imaging devices have led to a tremendous increase in the acquisition and sharing of multimedia content over the Internet. Despit…

cs.CV2019★ 1 cited

PAG-Net: Progressive Attention Guided Depth Super-resolution Network

Arpit Bansal, Sankaraganesh Jonna, Rajiv R. Sahay

In this paper, we propose a novel method for the challenging problem of guided depth map super-resolution, called PAGNet. It is based on residual dense networks and involves the at…

cs.CV2018

My camera can see through fences: A deep learning approach for image de-fencing

Sankaraganesh Jonna, Krishna Kanth Nakka, Rajiv R. Sahay

In recent times, the availability of inexpensive image capturing devices such as smartphones/tablets has led to an exponential increase in the number of images/videos captured. How…

cs.CV2016★ 2 cited

Towards an Automated Image De-fencing Algorithm Using Sparsity

Sankaraganesh Jonna, Krishna K. Nakka, Rajiv R. Sahay

Conventional approaches to image de-fencing suffer from non-robust fence detection and are limited to processing images of static scenes. In this position paper, we propose an auto…

cs.CV2016

Stereo image de-fencing using smartphones

Sankaraganesh Jonna, Sukla Satapathy, Rajiv R. Sahay

Conventional approaches to image de-fencing have limited themselves to using only image data in adjacent frames of the captured video of an approximately static scene. In this work…