4 papers
Decoder-Free Distillation for Quantized Image Restoration
S. M. A. Sharif, Abdur Rehman, Seongwan Kim +1
Quantization-Aware Training (QAT), combined with Knowledge Distillation (KD), holds immense promise for compressing models for edge deployment. However, joint optimization for prec…
Punching Above Precision: Small Quantized Model Distillation with Learnable Regularizer
Abdur Rehman, S M A Sharif, Md Abdur Rahaman +3
Quantization-aware training (QAT) combined with knowledge distillation (KD) is a promising strategy for compressing Artificial Intelligence (AI) models for deployment on resource-c…
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…