Blind Image Quality Evaluation Method Based on Cyclic Generative Adversarial Network

Li, Xiaoying; He, Shouwu

doi:10.1109/access.2024.3375940

Cited by 3 publications

(1 citation statement)

References 34 publications

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“…Moreover, the concern that standard reference images for multimodal fusion images are not available in the clinical setting will be mitigated. A series of GAN-based work has also been carried out by researchers related to image quality evaluation ( Ma et al, 2019 ; Guo et al, 2023 ; Kelkar et al, 2023 ; Li and He, 2024 ). In 2019, Ma et al (2019) proposed an end-to-end GAN model for quality assessment of images based on multitasking.…”

Section: Related Workmentioning

confidence: 99%

G2NPAN: GAN-guided nuance perceptual attention network for multimodal medical fusion image quality assessment

Tian,

Zhang

2024

Front. Neurosci.

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Multimodal medical fusion images (MMFI) are formed by fusing medical images of two or more modalities with the aim of displaying as much valuable information as possible in a single image. However, due to the different strategies of various fusion algorithms, the quality of the generated fused images is uneven. Thus, an effective blind image quality assessment (BIQA) method is urgently required. The challenge of MMFI quality assessment is to enable the network to perceive the nuances between fused images of different qualities, and the key point for the success of BIQA is the availability of valid reference information. To this end, this work proposes a generative adversarial network (GAN) -guided nuance perceptual attention network (G2NPAN) to implement BIQA for MMFI. Specifically, we achieve the blind evaluation style via the design of a GAN and develop a Unique Feature Warehouse module to learn the effective features of fused images from the pixel level. The redesigned loss function guides the network to perceive the image quality. In the end, the class activation mapping supervised quality assessment network is employed to obtain the MMFI quality score. Extensive experiments and validation have been conducted in a database of medical fusion images, and the proposed method is superior to the state-of-the-art BIQA method.

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Section: Related Workmentioning

confidence: 99%

G2NPAN: GAN-guided nuance perceptual attention network for multimodal medical fusion image quality assessment

Tian,

Zhang

2024

Front. Neurosci.

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QualityNet: A multi-stream fusion framework with spatial and channel attention for blind image quality assessment

Aslam,

Wei,

Khalid

et al. 2024

Sci Rep

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This study introduces a novel Blind Image Quality Assessment (BIQA) approach leveraging a multi-stream spatial and channel attention model. Our method addresses challenges posed by diverse image content and distortions by integrating feature maps from two distinct backbones. Through spatial and channel attention mechanisms, our algorithm prioritizes regions of interest, enhancing its ability to capture crucial image details. Extensive evaluations on four benchmark datasets demonstrate superior performance compared to existing methods, closely aligning with human perceptual assessment. Our approach exhibits exceptional generalization capabilities on both authentic and synthetic distortion databases. Moreover, it demonstrates a distinctive focus on perceptual foreground information, enhancing its practical applicability. Thorough quantitative analyses underscore the algorithm’s superior performance, establishing its dominance over existing methods.

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Sharpness-Based Distance Detection

Jin,

Zhou,

Dai

2024

Applied Sciences

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With the advancement of artificial intelligence, visual ranging has become a widely researched field. This paper introduces a novel method for distance measurement by evaluating the sharpness of objects in the current frame. It is well known that the image is sharpest at the camera’s focal point and becomes blurry when moving away from it. Leveraging this characteristic, this study utilizes sharpness to achieve distance measurement. Initially, the specific orientation of the target object is identified and located. The image area of the target object is cropped in a certain direction, and its image quality is assessed through sharpness calculation. Subsequently, the relationship between sharpness and distance values is analysed statistically, and a function curve depicting their correlation is plotted. Consequently, the distance between the target object and the detector can be determined based on sharpness with an extremely small margin of error using this method.

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Blind Image Quality Evaluation Method Based on Cyclic Generative Adversarial Network

Cited by 3 publications

References 34 publications

G2NPAN: GAN-guided nuance perceptual attention network for multimodal medical fusion image quality assessment

G2NPAN: GAN-guided nuance perceptual attention network for multimodal medical fusion image quality assessment

QualityNet: A multi-stream fusion framework with spatial and channel attention for blind image quality assessment

Sharpness-Based Distance Detection

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