Fast visual saliency based on multi‐scale difference of Gaussians fusion in frequency domain

Li, Weipeng; Yang, Xiaogang; Li, Chuanxiang; Lü, Ruitao; Xie, Xueli

doi:10.1049/iet-ipr.2020.0773

Cited by 18 publications

(10 citation statements)

References 34 publications

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“…As the visualization results show in Figure 5 , four SOTA saliency detection models, a Deep Hierarchical Saliency Network (DHSNet) [ 36 ], a U 2 -Net [ 14 ], a Multi-Scale Difference of Gaussians Fusion in Frequency (MDF) [ 37 ], and Wavelet Integrated Deep Networks for Image Segmentation (WaveSNet) [ 38 ] were used in the experiment to compare with our FESNet. We completed a verification of the datasets (GrainPest and SOC).…”

Section: Experiments and Resultsmentioning

confidence: 99%

FESNet: Frequency-Enhanced Saliency Detection Network for Grain Pest Segmentation

Zhai

Liu

et al. 2023

Insects

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As insect infestation is the leading factor accounting for nutritive and economic losses in stored grains, it is important to detect the presence and number of insects for the sake of taking proper control measures. Inspired by the human visual attention mechanism, we propose a U-net-like frequency-enhanced saliency (FESNet) detection model, resulting in the pixelwise segmentation of grain pests. The frequency clues, as well as the spatial information, are leveraged to enhance the detection performance of small insects from the cluttered grain background. Firstly, we collect a dedicated dataset, GrainPest, with pixel-level annotation after analyzing the image attributes of the existing salient object detection datasets. Secondly, we design a FESNet with the discrete wavelet transformation (DWT) and the discrete cosine transformation (DCT), both involved in the traditional convolution layers. As current salient object detection models will reduce the spatial information with pooling operations in the sequence of encoding stages, a special branch of the discrete wavelet transformation (DWT) is connected to the higher stages to capture accurate spatial information for saliency detection. Then, we introduce the discrete cosine transform (DCT) into the backbone bottlenecks to enhance the channel attention with low-frequency information. Moreover, we also propose a new receptive field block (NRFB) to enlarge the receptive fields by aggregating three atrous convolution features. Finally, in the phase of decoding, we use the high-frequency information and aggregated features together to restore the saliency map. Extensive experiments and ablation studies on our dataset, GrainPest, and open dataset, Salient Objects in Clutter (SOC), demonstrate that the proposed model performs favorably against the state-of-the-art model.

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Section: Experiments and Resultsmentioning

confidence: 99%

FESNet: Frequency-Enhanced Saliency Detection Network for Grain Pest Segmentation

Zhai

Liu

et al. 2023

Insects

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“…The coordinates of the feature maps and the resized saliency map are denoted by i and j . In this study, the method of Li et al [ 79 ] was applied to determine the saliency map of a video frame due to its low computational costs. Figure 3 depicts several video frames and their saliency maps.…”

Section: Proposed Methodsmentioning

confidence: 99%

“… Illustration of saliency map extraction: ( a , c , e , g ) input video frames and ( b , d , f , h ) saliency maps of the input video frames obtained by the method of Li et al [ 79 ]. …”

Section: Figurementioning

confidence: 99%

No-Reference Video Quality Assessment Using Multi-Pooled, Saliency Weighted Deep Features and Decision Fusion

Varga

2022

Sensors

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With the constantly growing popularity of video-based services and applications, no-reference video quality assessment (NR-VQA) has become a very hot research topic. Over the years, many different approaches have been introduced in the literature to evaluate the perceptual quality of digital videos. Due to the advent of large benchmark video quality assessment databases, deep learning has attracted a significant amount of attention in this field in recent years. This paper presents a novel, innovative deep learning-based approach for NR-VQA that relies on a set of in parallel pre-trained convolutional neural networks (CNN) to characterize versatitely the potential image and video distortions. Specifically, temporally pooled and saliency weighted video-level deep features are extracted with the help of a set of pre-trained CNNs and mapped onto perceptual quality scores independently from each other. Finally, the quality scores coming from the different regressors are fused together to obtain the perceptual quality of a given video sequence. Extensive experiments demonstrate that the proposed method sets a new state-of-the-art on two large benchmark video quality assessment databases with authentic distortions. Moreover, the presented results underline that the decision fusion of multiple deep architectures can significantly benefit NR-VQA.

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“…Compared with RGB space, the color distribution of LAB space is more abundant and uniform in visual sense, which is more suitable for saliency detection. Since saliency models [13][17] [18] based on frequency domain could segment regions with better edge effects than spatial domain models, we introduce the multiscale Difference of Gaussians fusion [19] to extract the feature spectral M of LAB space in frequency domain:…”

Section: Dehaze-driven Saliency Detectionmentioning

confidence: 99%

Uav Remote Sensing Image Dehazing Based On Saliency Guided Two-Scaletransmission Correction

Zhang

Zheng

et al. 2021

2021 IEEE International Conference on Image Processing (ICIP)

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Current dehazing methods for unmanned aerial vehicle (UAV) remote sensing images often hold problems of texture detail loss in highlight regions and color distortions. This is mainly due to incorrect estimation of the transmission. In this paper, we propose a UAV dehazing method based on saliency guided two-scale transmission correction. Firstly, we propose a dehaze-driven frequency domain saliency model to detect highlight regions of hazy UAV images for better transmission correction. Secondly, we introduce a two-scale correction method to estimate the transmission map with more accurate texture details. We also introduce a suppression parameter to further suppress color distortions and energy over-reduction. Finally, the saliency map is taken as a weight of transmission correction to avoid texture detail loss and color distortions, especially in highlights. Compared with state-of-the-art methods, our method shows better visual effect and detail visibility, especially for UAV images with highlight regions.

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Fast visual saliency based on multi‐scale difference of Gaussians fusion in frequency domain

Cited by 18 publications

References 34 publications

FESNet: Frequency-Enhanced Saliency Detection Network for Grain Pest Segmentation

FESNet: Frequency-Enhanced Saliency Detection Network for Grain Pest Segmentation

No-Reference Video Quality Assessment Using Multi-Pooled, Saliency Weighted Deep Features and Decision Fusion

Uav Remote Sensing Image Dehazing Based On Saliency Guided Two-Scaletransmission Correction

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