Search Results for author: Subrahmanyam Murala

Found 11 papers, 3 papers with code

Multi-weather Image Restoration via Domain Translation

no code implementations ICCV 2023 Prashant W. Patil, Sunil Gupta, Santu Rana, Svetha Venkatesh, Subrahmanyam Murala

Therefore, effective restoration of multi-weather degraded images is an essential prerequisite for successful functioning of such systems.

Image Restoration Translation

Hyperrealistic Image Inpainting with Hypergraphs

1 code implementation5 Nov 2020 Gourav Wadhwa, Abhinav Dhall, Subrahmanyam Murala, Usman Tariq

In this paper, we introduce hypergraph convolution on spatial features to learn the complex relationship among the data.

Image Inpainting

LEARNet Dynamic Imaging Network for Micro Expression Recognition

no code implementations20 Apr 2019 Monu Verma, Santosh Kumar Vipparthi, Girdhari Singh, Subrahmanyam Murala

We also propose a Lateral Accretive Hybrid Network (LEARNet) to capture micro-level features of an expression in the facial region.

Micro Expression Recognition Micro-Expression Recognition

CANDID: Robust Change Dynamics and Deterministic Update Policy for Dynamic Background Subtraction

no code implementations19 Apr 2018 Murari Mandal, Prafulla Saxena, Santosh Kumar Vipparthi, Subrahmanyam Murala

Background subtraction in video provides the preliminary information which is essential for many computer vision applications.

Change Detection

C2MSNet: A Novel approach for single image haze removal

no code implementations25 Jan 2018 Akshay Dudhane, Subrahmanyam Murala

In this paper, a cardinal (red, green and blue) color fusion network for single image haze removal is proposed.

Image Dehazing Single Image Dehazing +2

Local Neighborhood Intensity Pattern: A new texture feature descriptor for image retrieval

no code implementations7 Sep 2017 Prithaj Banerjee, Ayan Kumar Bhunia, Avirup Bhattacharyya, Partha Pratim Roy, Subrahmanyam Murala

The proposed method is based on the concept that neighbors of a particular pixel hold a significant amount of texture information that can be considered for efficient texture representation for CBIR.

Content-Based Image Retrieval Retrieval

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