A Deep Learning Approach in the DCT Domain to Detect the Source of HDR Images

Wang, Jiayu; Wang, Hongquan; Zhu, Xiaoyan; Zhou, Pengwei

doi:10.3390/electronics9122053

Cited by 7 publications

(4 citation statements)

References 29 publications

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“…Image SR using DL has successfully enhanced performance [104], [105]. Similar to HDR imaging, image SR is an ill-posed problem [85], [106] because multiple HR images exist for an LR image. As the degradation process of LR images is usually complex, the DNNbased methods mostly rely on manually designed degradation kernels [105], [107].…”

Section: Deep Hdr Imaging With Super-resolution (Sr)mentioning

confidence: 99%

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Deep Learning for HDR Imaging: State-of-the-Art and Future Trends

Wang

Yoon

2021

Preprint

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High dynamic range (HDR) imaging is a technique that allows an extensive dynamic range of exposures, which is important in image processing, computer graphics, and computer vision. In recent years, there has been a significant advancement in HDR imaging using deep learning (DL). This study conducts a comprehensive and insightful survey and analysis of recent developments in deep HDR imaging methodologies. We hierarchically and structurally group existing deep HDR imaging methods into five categories based on (1) number/domain of input exposures, (2) number of learning tasks, (3) novel sensor data, (4) novel learning strategies, and (5) applications. Importantly, we provide a constructive discussion on each category regarding its potential and challenges. Moreover, we review some crucial aspects of deep HDR imaging, such as datasets and evaluation metrics. Finally, we highlight some open problems and point out future research directions.

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Section: Deep Hdr Imaging With Super-resolution (Sr)mentioning

confidence: 99%

“…In their approach, traffic light candidates were detected from the low-exposure frames and accurately classified using a DNN in the high-exposure frames. [106] presented a DNN method for detecting the source of HDR images from LDR images.…”

Section: Applicationsmentioning

confidence: 99%

Deep Learning for HDR Imaging: State-of-the-Art and Future Trends

Wang

Yoon

2021

Preprint

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“…Combining features from various scales can enhance the performance and robustness of models. In a multiscale feature extraction block, channel-wise weights are applied to every channel of the multiscale feature; this process results in the emphasis on features that are beneficial for classification and the suppression of irrelevant features [49]. Therefore, Sun et al [50] proposed multiscale convolutional neural networks, which achieve accurate building extraction at various scales by leveraging multiscale deep features, employing an SVM-based decision fusion strategy, and optimizing results with superpixels, resulting in reduced noise and enhanced structural integrity in building extraction.…”

Section: Introductionmentioning

confidence: 99%

Multiscale Feature Extraction by Using Convolutional Neural Network: Extraction of Objects from Multiresolution Images of Urban Areas

Fan

2023

IJGI

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The emergence of deep learning-based classification methods has led to considerable advancements and remarkable performance in image recognition. This study introduces the Multiscale Feature Convolutional Neural Network (MSFCNN) for the extraction of complex urban land cover data, with a specific emphasis on buildings and roads. MSFCNN is employed to extract multiscale features from three distinct image types—Unmanned Aerial Vehicle (UAV) images, high-resolution satellite images (HR), and low-resolution satellite images (LR)—all collected within the Fengshan District of Kaohsiung, Taiwan. The model in this study demonstrated remarkable accuracy in classifying two key land cover categories. Its success in extracting multiscale features from different image resolutions. In the case of UAV images, MSFCNN achieved an accuracy rate of 91.67%, with a Producer’s Accuracy (PA) of 93.33% and a User’s Accuracy (UA) of 90.0%. Similarly, the model exhibited strong performance with HR images, yielding accuracy, PA, and UA values of 92.5%, 93.33%, and 91.67%, respectively. These results closely align with those obtained for LR imagery, which achieved respective accuracy rates of 93.33%, 95.0%, and 91.67%. Overall, the MSFCNN excels in the classification of both UAV and satellite images, showcasing its versatility and robustness across various data sources. The model is well suited for the task of updating cartographic data related to urban buildings and roads.

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“…The deep learning methodology is different compared to the traditional machine learning approach, whereby the features of interest are obtained through iterative optimal training such as through the convolution process [10], [13]. Usually, after the feature maps have passed through a convolution process, they will undergo a pooling process.…”

Section: Introductionmentioning

confidence: 99%

Micro Expression Recognition: Multi-scale Approach to Automatic Emotion Recognition by using Spatial Pyramid Pooling Module

Sian¹,

Stofa²,

Min³

et al. 2021

IJACSA

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Facial expression is one of the obvious cues that humans used to express their emotions. It is a necessary aspect of social communication between humans in their daily lives. However, humans do hide their real emotions in certain circumstances. Therefore, facial micro-expression has been observed and analyzed to reveal the true human emotions. However, micro-expression is a complicated type of signal that manifests only briefly. Hence, machine learning techniques have been used to perform micro-expression recognition. This paper introduces a compact deep learning architecture to classify and recognize human emotions of three categories, which are positive, negative, and surprise. This study utilizes the deep learning approach so that optimal features of interest can be extracted even with a limited number of training samples. To further improve the recognition performance, a multi-scale module through the spatial pyramid pooling network is embedded into the compact network to capture facial expressions of various sizes. The base model is derived from the VGG-M model, which is then validated by using combined datasets of CASMEII, SMIC, and SAMM. Moreover, various configurations of the spatial pyramid pooling layer were analyzed to find out the most optimal network setting for the micro-expression recognition task. The experimental results show that the addition of a multiscale module has managed to increase the recognition performance. The best network configuration from the experiment is composed of five parallel network branches that are placed after the second layer of the base model with pooling kernel sizes of two, three, four, five, and six.

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A Deep Learning Approach in the DCT Domain to Detect the Source of HDR Images

Cited by 7 publications

References 29 publications

Deep Learning for HDR Imaging: State-of-the-Art and Future Trends

Deep Learning for HDR Imaging: State-of-the-Art and Future Trends

Multiscale Feature Extraction by Using Convolutional Neural Network: Extraction of Objects from Multiresolution Images of Urban Areas

Micro Expression Recognition: Multi-scale Approach to Automatic Emotion Recognition by using Spatial Pyramid Pooling Module

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