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Found 9 papers, 3 papers with code
Multi-Modal Prompt Learning on Blind Image Quality Assessment
Image Quality Assessment (IQA) models benefit significantly from semantic information, which allows them to treat different types of objects distinctly.
Feature Denoising Diffusion Model for Blind Image Quality Assessment
Blind Image Quality Assessment (BIQA) aims to evaluate image quality in line with human perception, without reference benchmarks.
Concealed Object Segmentation with Hierarchical Coherence Modeling
Concealed object segmentation (COS) is a challenging task that involves localizing and segmenting those concealed objects that are visually blended with their surrounding environments.
Adaptive Feature Selection for No-Reference Image Quality Assessment using Contrastive Mitigating Semantic Noise Sensitivity
The current state-of-the-art No-Reference Image Quality Assessment (NR-IQA) methods typically use feature extraction in upstream backbone networks, which assumes that all extracted features are relevant.
Less is More: Learning Reference Knowledge Using No-Reference Image Quality Assessment
Concretely, by innovatively introducing a novel feature distillation method in IQA, we propose a new framework to learn comparative knowledge from non-aligned reference images.
RAUNE-Net: A Residual and Attention-Driven Underwater Image Enhancement Method
Underwater image enhancement (UIE) poses challenges due to distinctive properties of the underwater environment, including low contrast, high turbidity, visual blurriness, and color distortion.
AquaSAM: Underwater Image Foreground Segmentation
The Segment Anything Model (SAM) has revolutionized natural image segmentation, nevertheless, its performance on underwater images is still restricted.
Data-Efficient Image Quality Assessment with Attention-Panel Decoder
Blind Image Quality Assessment (BIQA) is a fundamental task in computer vision, which however remains unresolved due to the complex distortion conditions and diversified image contents.
Depth image denoising using nuclear norm and learning graph model
The depth images denoising are increasingly becoming the hot research topic nowadays because they reflect the three-dimensional (3D) scene and can be applied in various fields of computer vision.
