Exposedness-Based Noise-Suppressing Low-Light Image Enhancement

Dhara, Sobhan Kanti; Sen, Debashis

doi:10.1109/tcsvt.2021.3113559

Cited by 17 publications

(4 citation statements)

References 63 publications

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“…Xu et al [31] presented a structure-texture aware network to fully consider the global structure and local detailed texture. Dhara et al [32] proposed a structure-aware exposedness estimation procedure for noisesuppressing enhancement. For real-time photo enhancement, Gharbi et al [33] applied a bilateral grid [34], Lv et al [35] built light-weight CNNs, Zeng et al [36], Wang et al [37], and Yang et al [7] used learnable 3D lookup tables, and Zhang et al [38] used Transformer [39].…”

Section: B Learning Methodsmentioning

confidence: 99%

Crowd-Powered Photo Enhancement Featuring an Active Learning Based Local Filter

Kosugi

Yamasaki

2023

IEEE Trans. Circuits Syst. Video Technol.

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In this study, we address local photo enhancement to improve the aesthetic quality of an input image by applying different effects to different regions. Existing photo enhancement methods are either not content-aware or not local; therefore, we propose a crowd-powered local enhancement method for content-aware local enhancement, which is achieved by asking crowd workers to locally optimize parameters for image editing functions. To make it easier to locally optimize the parameters, we propose an active learning based local filter. The parameters need to be determined at only a few key pixels selected by an active learning method, and the parameters at the other pixels are automatically predicted using a regression model. The parameters at the selected key pixels are independently optimized, breaking down the optimization problem into a sequence of single-slider adjustments. Our experiments show that the proposed filter outperforms existing filters, and our enhanced results are more visually pleasing than the results by the existing enhancement methods. Our source code and results are available at https://github.com/satoshi-kosugi/crowd-powered.

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Section: B Learning Methodsmentioning

confidence: 99%

Crowd-Powered Photo Enhancement Featuring an Active Learning Based Local Filter

Kosugi

Yamasaki

2023

IEEE Trans. Circuits Syst. Video Technol.

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“…Compared with traditional methods, learning methods are robust to the noise due to the strong learning ability of deep neural networks, which could also improve the computational efficiency due to the acceleration of GPU. However, in transportation scenes, the edge feature is rarely considered in previous low-light image enhancement methods [17], which is especially important for higher-level image analysis like vehicle detection [13], pedestrian detection [18], and scene segmentation [19]. In practical applications, the frame rate of most transportation surveillance cameras is less than 30 FPS [20], which is thus the basic efficiency requirement of real-time image processing methods.…”

Section: A Motivationmentioning

confidence: 99%

Double Domain Guided Real-Time Low-Light Image Enhancement for Ultra-High-Definition Transportation Surveillance

Qu,

Liu,

Gao

et al. 2024

IEEE Trans. Intell. Transport. Syst.

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Real-time transportation surveillance is an essential part of the intelligent transportation system (ITS). However, images captured under low-light conditions often suffer poor visibility with types of degradation, such as noise interference and vague edge features, etc. With the development of imaging devices, the quality of the visual surveillance data is continually increasing, like 2K and 4K, which have more strict requirements on the efficiency of image processing. To satisfy the requirements on both enhancement quality and computational speed, this paper proposes a double domain guided real-time low-light image enhancement network (DDNet) for ultra-high-definition (UHD) transportation surveillance. Specifically, we design an encoderdecoder structure as the main architecture of the learning network. In particular, the enhancement processing is divided into two subtasks (i.e., color enhancement and gradient enhancement) via the proposed coarse enhancement module (CEM) and LoG-based gradient enhancement module (GEM), which are embedded in the encoder-decoder structure. It enables the network to enhance the color and edge features simultaneously. Through the decomposition and reconstruction on both color and gradient domains, our DDNet can restore the detailed feature information concealed by the darkness with better visual quality and efficiency. The evaluation experiments on standard and transportation-related datasets demonstrate that our DDNet provides superior enhancement quality and efficiency compared with state-of-the-art methods. Besides, the object detection and scene segmentation experiments indicate the practical benefits for higher-level image analysis under low-light environments in ITS.

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“…Following this, a series of LDR images with extreme exposure is generated through the brightness transform [59] of the original HDR images. To simulate the presence of noise in under-exposed images in real-world scenarios, we further process the under-exposed images by introducing Gaussian noise following the previous works [60][61][62]. The noise level dynamically increases as the lighting conditions decrease and varies between 0 and 50 in response to changes in light conditions.…”

Section: Synthetic Datasetmentioning

confidence: 99%

ERS-HDRI: Event-Based Remote Sensing HDR Imaging

Li,

Cheng,

Zeng

et al. 2024

Remote Sensing

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High dynamic range imaging (HDRI) is an essential task in remote sensing, enhancing low dynamic range (LDR) remote sensing images and benefiting downstream tasks, such as object detection and image segmentation. However, conventional frame-based HDRI methods may encounter challenges in real-world scenarios due to the limited information inherent in a single image captured by conventional cameras. In this paper, an event-based remote sensing HDR imaging framework is proposed to address this problem, denoted as ERS-HDRI, which reconstructs the remote sensing HDR image from a single-exposure LDR image and its concurrent event streams. The proposed ERS-HDRI leverages a coarse-to-fine framework, incorporating the event-based dynamic range enhancement (E-DRE) network and the gradient-enhanced HDR reconstruction (G-HDRR) network. Specifically, to efficiently achieve dynamic range fusion from different domains, the E-DRE network is designed to extract the dynamic range features from LDR frames and events and perform intra- and cross-attention operations to adaptively fuse multi-modal data. A denoise network and a dense feature fusion network are then employed for the generation of the coarse, clean HDR image. Then, the G-HDRR network, with its gradient enhancement module and multiscale fusion module, performs structure enforcement on the coarse HDR image and generates a fine informative HDR image. In addition, this work introduces a specialized hybrid imaging system and a novel, real-world event-based remote sensing HDRI dataset that contains aligned remote sensing LDR images, remote sensing HDR images, and concurrent event streams for evaluation. Comprehensive experiments have demonstrated the effectiveness of the proposed method. Specifically, it improves state-of-the-art PSNR by about 30% and the SSIM score by about 9% on the real-world dataset.

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Exposedness-Based Noise-Suppressing Low-Light Image Enhancement

Cited by 17 publications

References 63 publications

Crowd-Powered Photo Enhancement Featuring an Active Learning Based Local Filter

Crowd-Powered Photo Enhancement Featuring an Active Learning Based Local Filter

Double Domain Guided Real-Time Low-Light Image Enhancement for Ultra-High-Definition Transportation Surveillance

ERS-HDRI: Event-Based Remote Sensing HDR Imaging

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