CascNet: No-reference saliency quality assessment with cascaded applicability sorting and comparing network

Li, Kunqian; Shi, Duo; Zhang, Yongchang; Wu, Q. M. Jonathan; Luan, Xin; Song, Dalei

doi:10.1016/j.neucom.2020.04.090

Cited by 6 publications

(3 citation statements)

References 45 publications

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“…In addition to the above-mentioned datasets, we also establish a new dataset of marine ranching, that is, URPC, which includes 100 underwater images used for underwater robot picking contest 2 . In our experiments, UIEB is used for model training and test, while SQUID, RUIE and URPC are only used for test.…”

Section: Evaluation Metrics and Datasetsmentioning

confidence: 99%

“…And the complicated and dynamic conditions of underwater imaging make the quality of underwater images even worse. In fact, a very large number of common vision tasks, not limited to underwater scenes, require high image quality, such as image matching [1], visual detection [2,3], depth estimation [4], etc. Therefore, underwater image enhancement (UIE) is an essential technique in the underwater vision community [6].…”

Section: Introductionmentioning

confidence: 99%

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Underwater image enhancement with latent consistency learning‐based color transfer

Yang

Tian

et al. 2022

IET Image Processing

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Due to the inevitable wavelength-dependent light absorption and forward/backward scattering, underwater images usually suffer severe color distortion and are hazy. It has become quite necessary to improve the visual quality of underwater images for both underwater observation and operation. Traditional enhancement methods and existing deep learningbased approaches to underwater image enhancement usually produce unsatisfactory results for photographs taken in complicated, wild underwater scenes. In such scenes, complex and diverse degradation-enhancement mappings are often difficult to model, especially since there are very limited samples available for learning. Inspired by the success of colortransfer techniques, it is found that clear template image-assisted color transfer is a promising strategy for underwater image enhancement, including not only color correction but also contrast and visibility improvement. Therefore, instead of directly learning the complex deep enhancement models, it is proposed to select proper color-transfer templates by learning the latent consistency between the templates and the raw underwater images. The proposed new enhancement strategy alleviates the problem caused by incomplete colorcorrection models and provides more stable enhancements by utilizing color transfer with consideration of global color distribution consistency and local visual contrast. Comprehensive experiments conducted on UIEB, RUIE, URPC and SQUID datasets demonstrate the good performance and great potential of the proposed new underwater image enhancement strategy.

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Section: Evaluation Metrics and Datasetsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Underwater image enhancement with latent consistency learning‐based color transfer

Yang

Tian

et al. 2022

IET Image Processing

Self Cite

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“…FIQA is a branch of image quality assessment (IQA) but is also an extension of image quality. IQA can be subdivided into (i) full-reference (FR) [16,17], (ii) reduced-reference (RR) [18,19], and (iii) no-reference (NR) [20][21][22][23] categories according to the amount of information provided by the reference image. FIQAs also include FR-based approaches; for example, there is relevant literature [24][25][26] that reports the use of computing luminance distortion, structural similarity (SSIM), and probabilistic similarity to reference face images.…”

Section: Related Workmentioning

confidence: 99%

Sharpness and Brightness Quality Assessment of Face Images for Recognition

Chen

Huang

et al. 2021

Scientific Programming

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Face image quality has an important effect on recognition performance. Recognition-oriented face image quality assessment is particularly necessary for the screening or application of face images with various qualities. In this work, sharpness and brightness were mainly assessed by a classification model. We selected very high-quality images of each subject and established nine kinds of quality labels that are related to recognition performance by utilizing a combination of face recognition algorithms, the human vision system, and a traditional brightness calculation method. Experiments were conducted on a custom dataset and the CMU multi-PIE face database for training and testing and on Labeled Faces in the Wild for cross-validation. The experimental results show that the proposed method can effectively reduce the false nonmatch rate by removing the low-quality face images identified by the classification model and vice versa. This method is even effective for face recognition algorithms that are not involved in label creation and whose training data are nonhomologous to the training set of our quality assessment model. The results show that the proposed method can distinguish images of different qualities with reasonable accuracy and is consistent with subjective human evaluation. The quality labels established in this paper are closely related to the recognition performance and exhibit good generalization to other recognition algorithms. Our method can be used to reject low-quality images to improve the recognition rate and screen high-quality images for subsequent processing.

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No-reference shadow detection quality assessment via reference learning and multi-mode exploring

Wei

Xing

et al. 2023

Computers & Graphics

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CascNet: No-reference saliency quality assessment with cascaded applicability sorting and comparing network

Cited by 6 publications

References 45 publications

Underwater image enhancement with latent consistency learning‐based color transfer

Underwater image enhancement with latent consistency learning‐based color transfer

Sharpness and Brightness Quality Assessment of Face Images for Recognition

No-reference shadow detection quality assessment via reference learning and multi-mode exploring

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