1 code implementation • CVPR 2023 • Nancy Mehta, Akshay Dudhane, Subrahmanyam Murala, Syed Waqas Zamir, Salman Khan, Fahad Shahbaz Khan
Burst image processing is becoming increasingly popular in recent years.
1 code implementation • CVPR 2023 • Jasdeep Singh, Subrahmanyam Murala, G. Sankara Raju Kosuru
But small motions are prone to noise, illumination changes, large motions, etc.
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.
1 code implementation • 5 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.
no code implementations • 20 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.
no code implementations • 7 May 2018 • Sonakshi Mathur, Mallika Chaudhary, Hemant Verma, Murari Mandal, S. K. Vipparthi, Subrahmanyam Murala
A novel color feature descriptor, Multichannel Distributed Local Pattern (MDLP) is proposed in this manuscript.
no code implementations • 19 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.
no code implementations • 25 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.
no code implementations • 3 Jan 2018 • Ayan Kumar Bhunia, Avirup Bhattacharyya, Prithaj Banerjee, Partha Pratim Roy, Subrahmanyam Murala
In this paper, we have proposed a novel feature descriptors combining color and texture information collectively.
no code implementations • 7 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.