No-Reference Hyperspectral Image Quality Assessment via Ranking Feature Learning

Li, Yuyan; Dong, Yubo; Li, Haoyong; Liu, Danhua; Xue, Fang; Gao, Dahua

doi:10.3390/rs16101657

Cited by 2 publications

(1 citation statement)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, in the hyperspectral image (HSI) reconstruction task, for the phenomenon that the best model validated on the simulated dataset has a failure in real measurements, Li et al [ 52 ] proposed a novel reference-free HSI quality assessment metric through ranked feature learning (R-NHSIQA). The metric realizes the acquisition of the best model in real-world tasks by calculating the Wasserstein distance between the depth feature distribution of the reconstructed HSI and the reference distribution.…”

Section: Related Workmentioning

confidence: 99%

Semantic-Aware Fusion Network Based on Super-Resolution

Xu,

Zou

2024

Sensors

View full text Add to dashboard Cite

The aim of infrared and visible image fusion is to generate a fused image that not only contains salient targets and rich texture details, but also facilitates high-level vision tasks. However, due to the hardware limitations of digital cameras and other devices, there are more low-resolution images in the existing datasets, and low-resolution images are often accompanied by the problem of losing details and structural information. At the same time, existing fusion algorithms focus too much on the visual quality of the fused images, while ignoring the requirements of high-level vision tasks. To address the above challenges, in this paper, we skillfully unite the super-resolution network, fusion network and segmentation network, and propose a super-resolution-based semantic-aware fusion network. First, we design a super-resolution network based on a multi-branch hybrid attention module (MHAM), which aims to enhance the quality and details of the source image, enabling the fusion network to integrate the features of the source image more accurately. Then, a comprehensive information extraction module (STDC) is designed in the fusion network to enhance the network’s ability to extract finer-grained complementary information from the source image. Finally, the fusion network and segmentation network are jointly trained to utilize semantic loss to guide the semantic information back to the fusion network, which effectively improves the performance of the fused images on high-level vision tasks. Extensive experiments show that our method is more effective than other state-of-the-art image fusion methods. In particular, our fused images not only have excellent visual perception effects, but also help to improve the performance of high-level vision tasks.

show abstract

Section: Related Workmentioning

confidence: 99%

Semantic-Aware Fusion Network Based on Super-Resolution

Xu,

Zou

2024

Sensors

View full text Add to dashboard Cite

show abstract

Joint Luminance-Saliency Prior and Attention for Underwater Image Quality Assessment

Lin,

He,

Jin

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

Underwater images, as a crucial medium for storing ocean information in underwater sensors, play a vital role in various underwater tasks. However, they are prone to distortion due to the imaging environment, which leads to a decline in visual quality, which is an urgent issue for various marine vision systems to address. Therefore, it is necessary to develop underwater image enhancement (UIE) and corresponding quality assessment methods. At present, most underwater image quality assessment (UIQA) methods primarily rely on extracting handcrafted features that characterize degradation attributes, which struggle to measure complex mixed distortions and often exhibit discrepancies with human visual perception in practical applications. Furthermore, current UIQA methods lack the consideration of the perception perspective of enhanced effects. To this end, this paper employs luminance and saliency priors as critical visual information for the first time to measure the enhancement effect of global and local quality achieved by the UIE algorithms, named JLSAU. The proposed JLSAU is built upon an overall pyramid-structured backbone, supplemented by the Luminance Feature Extraction Module (LFEM) and Saliency Weight Learning Module (SWLM), which aim at obtaining perception features with luminance and saliency priors at multiple scales. The supplement of luminance priors aims to perceive visually sensitive global distortion of luminance, including histogram statistical features and grayscale features with positional information. The supplement of saliency priors aims to perceive visual information that reflects local quality variation both in spatial and channel domains. Finally, to effectively model the relationship among different levels of visual information contained in the multi-scale features, the Attention Feature Fusion Module (AFFM) is proposed. Experimental results on the public UIQE and UWIQA datasets demonstrate that the proposed JLSAU outperforms existing state-of-the-art UIQA methods.

show abstract

No-Reference Hyperspectral Image Quality Assessment via Ranking Feature Learning

Cited by 2 publications

References 50 publications

Semantic-Aware Fusion Network Based on Super-Resolution

Semantic-Aware Fusion Network Based on Super-Resolution

Joint Luminance-Saliency Prior and Attention for Underwater Image Quality Assessment

Contact Info

Product

Resources

About