TL;DR
This work validates the design effectiveness of decoupling global and local representations within a frozen backbone, and establishes a new baseline for parameter-efficient enhancement.
Image enhancement is a widely researched area in the domain of computer vision, particularly image processing. Among the subdomains, low-light image enhancement (LLIE) receives considerable attention due to problems and challenges imposed by poor lighting conditions. As such, low-light images suffer from poor visibilit...
A Multimodal Intrinsics-Guided Framework that integrates RGB and thermal data to reconstruct well-lit images and demonstrates strong generalization to real-world benchmarks such as LLVIP and V-TIEE, outperforming state-of-the-art methods in most evaluation metrics.
Ablation, sensitivity, qualitative, and perceptual color-space analyses indicate that HLCNet is particularly effective for low-light images containing blur and weak structural details.
A CNN-based edge-aware artifact reduction framework (CNN-AR) is proposed that integrates an enhanced deep super-resolution (EDSR) backbone with a holistically nested edge detection (HED) guided loss, enabling superior artifact suppression while preserving fine structural details.
Images captured in hazy environment are often degraded, thereby impacting several applications of computer vision, like image segmentation, classification, object recognition, and many others. Accordingly, various techniques and algorithms for image dehazing were developed over the years - from old days traditional to...
Results validate the effectiveness and robustness of the proposed illumination-aware modeling strategy for low-light image enhancement, IA2former, which effectively captures long-range dependencies, improves detail restoration, and preserves spatial structures under challenging illumination conditions.
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