End-to-end single image enhancement based on a dual network cascade model

Chen, Yeyao; Yu, Mei; Jiang, Gangyi; Peng, Zongju; Chen, Fen

doi:10.1016/j.jvcir.2019.04.008

Cited by 14 publications

(10 citation statements)

References 33 publications

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“…The fused results of Kou [49], Lee [21], Liu [114], LST [42], LXN [123], LZG [46], MEFNet [86], and Wang [48] contain dark regions and the intensity of the fused images distributes non uniformly. IFCNN [81], LH20 [32], Ma [31], Paul [57], Mertens [41], and Nejati [45] perform relatively better on this sequence. [114], (c) Ma [31], (d) Lee [21], (e) Hayat [115], (f) Qi [40], (g) LH20 [32], (h) LH21 [33], (i) Mertens [41], (j) LST [42], (k) Paul [57], (l) Nejati [45], (m) LZG [46], (n) Kou [49], (o) Yang [50], (p) LXN [123], (q) Wang [48], (r) IFCNN [82], (s) MEFNet [86].…”

Section: Testing For Static Scenementioning

confidence: 90%

“…It worked on a source image sequence consisting of three exposure levels and each exposure level can be viewed as a signal channel. In [81], a dual-network cascade model was constructed consisting of an exposure prediction network and an exposure fusion network. The former was used to recover the lost details in underexposed or overexposed regions, and the latter could perform fusion enhancement.…”

Section: Supervised Methodsmentioning

confidence: 99%

“…Regardless, it can be used as an evaluation reference. Huang [1]; Yang [13]; MEF-GAN [17]; Liu [58]; Yang [62]; Martorell [64]; Li [77]; Liu [80]; Chen [81]; Deepfuse [84]; MEFNet [86]; U2fusion [88]; Gao [89]; LXN [123]; Shao [134]; Wu [135]; Merianos [136] The larger, the better 2 Q AB/F Nie [6]; Liu [38]; LST [42]; Hayat [115]; Shao [134] The larger, the better 3 MEF-SSIMc Martorell [64]; UMEF [87]; Shao [134] The larger, the better 4 Mutual information (MI) Nie [6]; Wang [34]; Gao [89]; Choi [137] The larger, the better 5 Peak signal-to-noise ratio (PSNR) Kim [7]; MEF-GAN [17]; Chen [81]; U2fusion [88]; Gao [89]; Shao [134] The larger, the better 6 Natural image quality evaluator (NIQE) Huang [1]; Hayat [115]; Wu [135]; Xu [138] The smaller, the better 7 Standard deviation (SD) MEF-GAN [17]; Gao [89]; Wu [135] The larger, the better 8 Entropy (EN) Gao [89]; Wu…”

Section: Objective Quantitative Comparisonmentioning

confidence: 99%

See 2 more Smart Citations

Multi-Exposure Image Fusion Techniques: A Comprehensive Review

Liu

Song

et al. 2022

Remote Sensing

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Multi-exposure image fusion (MEF) is emerging as a research hotspot in the fields of image processing and computer vision, which can integrate images with multiple exposure levels into a full exposure image of high quality. It is an economical and effective way to improve the dynamic range of the imaging system and has broad application prospects. In recent years, with the further development of image representation theories such as multi-scale analysis and deep learning, significant progress has been achieved in this field. This paper comprehensively investigates the current research status of MEF methods. The relevant theories and key technologies for constructing MEF models are analyzed and categorized. The representative MEF methods in each category are introduced and summarized. Then, based on the multi-exposure image sequences in static and dynamic scenes, we present a comparative study for 18 representative MEF approaches using nine commonly used objective fusion metrics. Finally, the key issues of current MEF research are discussed, and a development trend for future research is put forward.

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Section: Testing For Static Scenementioning

confidence: 90%

Section: Supervised Methodsmentioning

confidence: 99%

Section: Objective Quantitative Comparisonmentioning

confidence: 99%

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Multi-Exposure Image Fusion Techniques: A Comprehensive Review

Liu

Song

et al. 2022

Remote Sensing

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show abstract

“…Therefore, they proposed a network that learns to recover image objects in the low-frequency layer and then enhances high-frequency details based on the recovered image objects. Chen et al [ 25 ] used an exposure prediction network to generate under-/overexposure images and then fused them with the input image to obtain the enhanced image. Lv et al [ 26 ] proposed a multi-branch network to extract rich features of different levels and then fused the multi-branch outputs to produce the output image.…”

Section: Introductionmentioning

confidence: 99%

Low-Light Image Enhancement Based on Multi-Path Interaction

Zhao

Gong

Wang

et al. 2021

Sensors

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Due to the non-uniform illumination conditions, images captured by sensors often suffer from uneven brightness, low contrast and noise. In order to improve the quality of the image, in this paper, a multi-path interaction network is proposed to enhance the R, G, B channels, and then the three channels are combined into the color image and further adjusted in detail. In the multi-path interaction network, the feature maps in several encoding–decoding subnetworks are used to exchange information across paths, while a high-resolution path is retained to enrich the feature representation. Meanwhile, in order to avoid the possible unnatural results caused by the separation of the R, G, B channels, the output of the multi-path interaction network is corrected in detail to obtain the final enhancement results. Experimental results show that the proposed method can effectively improve the visual quality of low-light images, and the performance is better than the state-of-the-art methods.

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“…The differences of among aerial photography instruments, environments and target states, which lead to high information content, multiple heterogeneity and high dimensionality of aerial photography images or videos. Available image processing algorithms such as image denoising [ 4 ], image enhancement [ 5 ] and image mosaicking [ 6 ] can satisfy the real-time processing requirements of aerial image target recognition, but difficult problems and challenges remain in the realization of target tracking, including the following.…”

Section: Introductionmentioning

confidence: 99%

Aerial Video Trackers Review

Jia

Lai

Qian

et al. 2020

Entropy

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Target tracking technology that is based on aerial videos is widely used in many fields; however, this technology has challenges, such as image jitter, target blur, high data dimensionality, and large changes in the target scale. In this paper, the research status of aerial video tracking and the characteristics, background complexity and tracking diversity of aerial video targets are summarized. Based on the findings, the key technologies that are related to tracking are elaborated according to the target type, number of targets and applicable scene system. The tracking algorithms are classified according to the type of target, and the target tracking algorithms that are based on deep learning are classified according to the network structure. Commonly used aerial photography datasets are described, and the accuracies of commonly used target tracking methods are evaluated in an aerial photography dataset, namely, UAV123, and a long-video dataset, namely, UAV20L. Potential problems are discussed, and possible future research directions and corresponding development trends in this field are analyzed and summarized.

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End-to-end single image enhancement based on a dual network cascade model

Cited by 14 publications

References 33 publications

Multi-Exposure Image Fusion Techniques: A Comprehensive Review

Multi-Exposure Image Fusion Techniques: A Comprehensive Review

Low-Light Image Enhancement Based on Multi-Path Interaction

Aerial Video Trackers Review

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