187 citations · 236 across the 12 of their papers we have counts for
13 papers
Low Light Image Enhancement Challenge at NTIRE 2026
George Ciubotariu, Sharif S M A, Abdur Rehman +90
This paper presents a comprehensive review of the NTIRE 2026 Low Light Image Enhancement Challenge, highlighting the proposed solutions and final results. The objective of this cha…
TriFusion-SR: Joint Tri-Modal Medical Image Fusion and SR
Fayaz Ali Dharejo, Sharif S. M. A., Aiman Khalil +3
Multimodal medical image fusion facilitates comprehensive diagnosis by aggregating complementary structural and functional information, but its effectiveness is limited by resoluti…
CLIP-Guided Multi-Task Regression for Multi-View Plant Phenotyping
Simon Warmers, Muhammad Zawish, Fayaz Ali Dharejo +2
Modeling plant growth dynamics plays a central role in modern agricultural research. However, learning robust predictors from multi-view plant imagery remains challenging due to st…
Degradation-Aware All-in-One Image Restoration via Latent Prior Encoding
S M A Sharif, Abdur Rehman, Fayaz Ali Dharejo +2
Real-world images often suffer from spatially diverse degradations such as haze, rain, snow, and low-light, significantly impacting visual quality and downstream vision tasks. Exis…
Illuminating Darkness: Learning to Enhance Low-light Images In-the-Wild
S M A Sharif, Abdur Rehman, Zain Ul Abidin +3
Single-shot low-light image enhancement (SLLIE) remains challenging due to the limited availability of diverse, real-world paired datasets. To bridge this gap, we introduce the Low…
ContextFormer: Redefining Efficiency in Semantic Segmentation
Mian Muhammad Naeem Abid, Nancy Mehta, Zongwei Wu +1
Semantic segmentation assigns labels to pixels in images, a critical yet challenging task in computer vision. Convolutional methods, although capturing local dependencies well, str…
CPLIP: Zero-Shot Learning for Histopathology with Comprehensive Vision-Language Alignment
Sajid Javed, Arif Mahmood, Iyyakutti Iyappan Ganapathi +3
This paper proposes Comprehensive Pathology Language Image Pre-training (CPLIP), a new unsupervised technique designed to enhance the alignment of images and text in histopathology…
3D-TexSeg: Unsupervised Segmentation of 3D Texture using Mutual Transformer Learning
Iyyakutti Iyappan Ganapathi, Fayaz Ali, Sajid Javed +2
Analysis of the 3D Texture is indispensable for various tasks, such as retrieval, segmentation, classification, and inspection of sculptures, knitted fabrics, and biological tissue…
Improving Underwater Visual Tracking With a Large Scale Dataset and Image Enhancement
Basit Alawode, Fayaz Ali Dharejo, Mehnaz Ummar +6
This paper presents a new dataset and general tracker enhancement method for Underwater Visual Object Tracking (UVOT). Despite its significance, underwater tracking has remained un…
AI and 6G into the Metaverse: Fundamentals, Challenges and Future Research Trends
Muhammad Zawish, Fayaz Ali Dharejo, Sunder Ali Khowaja +4
Since Facebook was renamed Meta, a lot of attention, debate, and exploration have intensified about what the Metaverse is, how it works, and the possible ways to exploit it. It is…
Multimodal-Boost: Multimodal Medical Image Super-Resolution using Multi-Attention Network with Wavelet Transform
Fayaz Ali Dharejo, Muhammad Zawish, Farah Deeba Yuanchun Zhou +3
Deep learning based single image super resolution (SISR) algorithms has revolutionized the overall diagnosis framework by continually improving the architectural components and tra…
Data Augmentation for Graph Convolutional Network on Semi-Supervised Classification
Zhengzheng Tang, Ziyue Qiao, Xuehai Hong +4
Data augmentation aims to generate new and synthetic features from the original data, which can identify a better representation of data and improve the performance and generalizab…
TWIST-GAN: Towards Wavelet Transform and Transferred GAN for Spatio-Temporal Single Image Super Resolution
Fayaz Ali Dharejo, Farah Deeba, Yuanchun Zhou +5
Single Image Super-resolution (SISR) produces high-resolution images with fine spatial resolutions from aremotely sensed image with low spatial resolution. Recently, deep learning…