5 papers
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…
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…
Deep Perceptual Enhancement for Medical Image Analysis
S M A Sharif, Rizwan Ali Naqvi, Mithun Biswas +1
Due to numerous hardware shortcomings, medical image acquisition devices are susceptible to producing low-quality (i.e., low contrast, inappropriate brightness, noisy, etc.) images…
Two-stage Deep Denoising with Self-guided Noise Attention for Multimodal Medical Images
S M A Sharif, Rizwan Ali Naqvi, Woong-Kee Loh
Medical image denoising is considered among the most challenging vision tasks. Despite the real-world implications, existing denoising methods have notable drawbacks as they often…
DarkDeblur: Learning single-shot image deblurring in low-light condition
S M A Sharif, Rizwan Ali Naqvi, Farman Alic +1
Single-shot image deblurring in a low-light condition is known to be a profoundly challenging image translation task. This study tackles the limitations of the low-light image debl…