Kavinder Singh

dblp:302/8306 · DBLP profile ↗
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9ranked-venue papers
5as first author
9since 2021 · last 2026
0000-0002-2278-5270ORCID · verified

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 8 · 5 first-author · 8 since 2021Artificial intelligence and machine learning · 1 · 1 since 2021
YearPublicationVenuePosition
2026 Frame-wise Learned Offset Network with localized loss for video summarization
Md Hasnat Hosen Arafat, Ranu Singh, Kavinder Singh, Anil Singh Parihar, Payal Dabas
Eng. Appl. Artif. Intell.3
2025 QLight-Net: Quaternion based low light image enhancement network
Sudeep Kumar Acharjee, Kavinder Singh, Anil Singh Parihar
J. Vis. Commun. Image Represent.2
2025 Capturing spatiotemporal dependencies with competitive set attention for video summarization
Md Hasnat Hosen Arafat, Kavinder Singh
Vis. Comput.2
2024 MRN-LOD: Multi-exposure Refinement Network for Low-light Object Detection
Kavinder Singh, Anil Singh Parihar
J. Vis. Commun. Image Represent.1
2024 FRN: Fusion and recalibration network for low-light image enhancement
Kavinder Singh, Akshat Agarwal, Mohit Kumar Agarwal, Aditya Shankar, Anil Singh Parihar
Multim. Tools Appl.1
2024 Illumination estimation for nature preserving low-light image enhancement
Kavinder Singh, Anil Singh Parihar
Vis. Comput.1
2023 DSE-Net: Deep simultaneous estimation network for low-light image enhancement
Kavinder Singh, Anil Singh Parihar
J. Vis. Commun. Image Represent.1
2021 Fusion-based simultaneous estimation of reflectance and illumination for low-light image enhancement
abstract
Abstract Low‐light image enhancement is a challenging field in image processing. Retinex‐based methods perform well for low‐light images. However, reflectance and illumination estimation is an ill‐posed problem. This paper presents a new framework for the simultaneous estimation of reflectance and illumination for low‐light image enhancement. The algorithm estimates multiple instances of illumination and reflectance and blends them to estimate the final components. The proposed approach uses multi‐scale fusion for illumination estimation and naive fusion for reflectance estimation. Extensive experimentation and analysis with a large set of low‐light images validates the performance of the proposed approach. The comparison shows the superiority of the proposed approach over most of the existing low‐light image enhancement methods. The proposed method provides colour constancy in low‐light image enhancement and preserves the naturalness of the image.
Anil Singh Parihar, Kavinder Singh, Hrithik Rohilla, Gul Asnani
IET Image Process.2
2021 Variational optimization based single image dehazing
Kavinder Singh, Anil Singh Parihar
J. Vis. Commun. Image Represent.1