EDBT 2026 Demo / reviewers in the wild / expert
Kavinder Singh
dblp:302/8306
· DBLP profile ↗
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
| Year | Publication | Venue | Position |
|---|---|---|---|
| 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 enhancementabstractAbstract 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 |