187 citations · 236 across the 12 of their papers we have counts for
9 papers · 1 filter
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…