EBSD-Net: Enhancing Brightness and Suppressing Degradation for Low-light Color Image using Deep Networks

Duong, Minh-Thien; Hong, Min-Cheol

doi:10.1109/icce-asia57006.2022.9954784

Cited by 10 publications

(7 citation statements)

References 14 publications

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“…Inspired by the remarkable success of transformer architectures in natural language processing (NLP), researchers have recently extended their applications to computer-vision (CV) tasks. Although convolutional neural networks (CNNs) have long been regarded as foundational components in vision applications [35], [36], transformers are now emerging as a promising alternative. Image-generative pre-trained transformer (Image GPT) [37] pioneered the introduction of transformers to 2-D image-classification tasks through unsupervised learning.…”

Section: Transformer-based Object Detectionmentioning

confidence: 99%

TSSTDet: Transformation-Based 3-D Object Detection via a Spatial Shape Transformer

Hoang,

Bui,

Yoo

2024

IEEE Sensors J.

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Accurately detecting and understanding the shapes of objects in three-dimensional (3-D) scenes are essential for autonomous driving. In a 3-D scene, objects are distributed with various incomplete shapes and rotations. Determining the shape allows for a comprehensive understanding of an object's dimensions, rotations, and spatial relationships with its surroundings. Traditional detection methods do not explicitly consider the rotations and complete shapes that objects can assume. Consequently, these methods require large networks and extensive data augmentation to detect accurately. Taking advantage of the Vision-Transformer (ViT), we introduce an efficient transformer-based 3-D detector called Transformation-based 3-D Object Detection via a Spatial Shape Transformer (TSSTDet) to address these challenges. We constructed TSSTDet as a multistage detector based on a LiDAR point cloud. Specifically, TSSTDet utilizes a sparse convolution backbone to extract multi-channel and transformationequivariant voxel features. Furthermore, we designed an efficient module that employs the transformer approach to estimate the completed shape of an object. These features are then aligned and aggregated to create lightweight and compact representations that enable high-performance 3-D object detection. We assessed the effectiveness of the proposed framework by evaluating its performance on both the KITTI and Waymo Open datasets. These evaluations demonstrated that our framework achieves top-tier performance in 3-D object detection.

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Section: Transformer-based Object Detectionmentioning

confidence: 99%

TSSTDet: Transformation-Based 3-D Object Detection via a Spatial Shape Transformer

Hoang,

Bui,

Yoo

2024

IEEE Sensors J.

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“…Recently, deep learning (DL) and machine learning (ML) have been applied in various fields, such as object detection [26,27], image processing [28,29], and speech processing [30,31]. In the field of data storage systems, multilayer perceptrons (MLPs) are used to predict noise and decode modulation codes in BPMR systems [32].…”

Section: Reconstructedmentioning

confidence: 99%

A Nonlinear Convolutional Neural Network-Based Equalizer for Holographic Data Storage Systems

Nguyen,

Lee

2023

Applied Sciences

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Central data systems require mass storage systems for big data from many fields and devices. Several technologies have been proposed to meet this demand. Holographic data storage (HDS) is at the forefront of data storage innovation and exploits the extraordinary characteristics of light to encode and retrieve two-dimensional (2D) data from holographic volume media. Nevertheless, a formidable challenge exists in the form of 2D interference that is a by-product of hologram dispersion during data retrieval and is a substantial barrier to the reliability and efficiency of HDS systems. To solve these problems, an equalizer and target are applied to HDS systems. However, in previous studies, the equalizer acted only as a linear convolution filter for the received signal. In this study, we propose a nonlinear equalizer using a convolutional neural network (CNN) for HDS systems. Using a CNN-based equalizer, the received signal can be nonlinearly converted into the desired signal with higher accuracy. In the experiments, our proposed model achieved a gain of approximately 2.5 dB in contrast to conventional models.

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“…Therefore, it results in the enhanced image containing additive noise, which can come from a low-light environment. Several studies have recently been proposed to enhance low-light images in the YCbCr color space [44], [45], but outstanding visual enhancement results…”

Section: Related Workmentioning

confidence: 99%

DMT-Net: Deep Multiple Networks for Low-Light Image Enhancement Based on Retinex Model

Duong,

Lee,

Hong

2023

IEEE Access

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Images captured under low-light conditions are typically prone to undesirable visual phenomena, particularly color distortion and additive noise, that impede the aesthetics and performance of high-level vision tasks. Therefore, developing an enhancement method is necessary to obtain visually pleasing images. This paper proposes deep multiple networks for low-light image enhancement based on the Retinex model (defined as DMT-Net), wherein each network is configured to perform its own role in the YCbCr color space. More specifically, Decoupled-Net is presented to accurately decouple the luminance channel into reflectance and illumination components using the Retinex model. Denoising-Net is connected to Decoupled-Net to eradicate additive noise in the reflectance component. Boosting-Net enhances the illumination component and reduces halo artifacts. In addition, we propose Chrominance-Net to mitigate the distortion of the Cb and Cr channels. Our study also focuses on having a suitable norm order for the loss function based on kurtosis characteristics, such that each network is properly trained to minimize the residual in the training process. Finally, the experimental results prove that our proposed method outperforms numerous other cutting-edge methods in both qualitative and quantitative terms. INDEX TERMSDeep multiple networks, low-light image enhancement, Retinex model, undesirable degradation, YCbCr color space.

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EBSD-Net: Enhancing Brightness and Suppressing Degradation for Low-light Color Image using Deep Networks

Cited by 10 publications

References 14 publications

TSSTDet: Transformation-Based 3-D Object Detection via a Spatial Shape Transformer

TSSTDet: Transformation-Based 3-D Object Detection via a Spatial Shape Transformer

A Nonlinear Convolutional Neural Network-Based Equalizer for Holographic Data Storage Systems

DMT-Net: Deep Multiple Networks for Low-Light Image Enhancement Based on Retinex Model

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