Detail-Enhanced Multi-Scale Exposure Fusion in YUV Color Space

Wang, Qiantong; Chen, Weihai; Wu, Xiaofeng; Li, Zhengguo

doi:10.1109/tcsvt.2019.2919310

Cited by 78 publications

(31 citation statements)

References 34 publications

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“…It integrated the input images under different exposure conditions into a fused image by using the linear fusion framework based on the Laplace pyramid. Wang [48] presented a multi-scale MEF algorithm based on YUV color space instead of RGB color space and designed a new weight smoothing pyramid used in YUV color space. A vector field construction algorithm was introduced to maintain the details of the brightest and the darkest areas in HDR scenes and avoid color distortion in the fused image.…”

Section: Multi-scale Decomposition-based Methodsmentioning

confidence: 99%

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: Multi-scale Decomposition-based Methodsmentioning

confidence: 99%

Multi-Exposure Image Fusion Techniques: A Comprehensive Review

Liu

Song

et al. 2022

Remote Sensing

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“…The problem we address is that of generating one representative image by fusing crack details present in several images acquired with different illuminations. Multi-Exposure Fusion (MEF) is a similar problem where the objective is to fuse a sequence of images acquired with different exposure times to get a single well exposed image [10,11,12,13,14,15]. Nevertheless, cracks can have better visibility in a multi-illumination sequence than in a multi-exposure sequence.…”

Section: Related Workmentioning

confidence: 99%

Multi-Illumination Fusion With Crack Enhancement Using Cycle-Consistent Losses

Padalkar

Beltrán-González

Bue

2021

2021 IEEE International Conference on Image Processing (ICIP)

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This paper addresses for the first time, the problem of multiillumination fusion with crack enhancement. Our models are trained using cycle-consistent losses to combine crack details from several mutually registered multi-illumination images of ceramic tiles, into a single representative image. Using realworld industrial data, we show that the crack locations are enhanced in the fused images, making them easily noticeable for remote inspection, and demonstrate the effectiveness of our method compared to a multi-exposure fusion technique.

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“…If the window exceeds the boundary, the area with the size l * w within the image boundary is selected as the filtering area. In order to increase the efficiency of filtering and reduce the number of filtering layers, it is necessary to format the color pictures [20]. Images are stored in a variety of formats, such as RGB format, YUV format, YCBCR format, and so on.…”

Section: B Ambient Light Estimationmentioning

confidence: 99%

Multilayer Fusion and Chunk-Based Transmittance Estimation for Natural Hazy Image Enhancement

Mei

Yuan

2019

IEEE Access

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Aiming at the uneven distribution of haze concentration and color imbalance in haze weather images, a natural hazy image enhancement method which combines with multilayer fusion and chunk-based is proposed. Based on the atmospheric physical model, the detail and base layers of scene images can be extracted using multilayer decomposition and nonlinear mapping function. Iterative Box Filter can improve the accuracy of ambient light selection and avoid the imbalance of ambient light estimation. The image is segmented into blocks, and the block images are processed by the same operations which are multilayer decomposition and nonlinear mapping function to obtain the detail maps of block images. By splicing these detail maps into the whole detail maps and combining with the dark channel priori, a good transmittance estimation map can be obtained. Throughout the experiment, the guide image filter is utilized to preserve the edges of the image and color correction is added to the model. The experiment results demonstrate that our proposed method can outperform state-of-the-art methods in both qualitative and quantitative comparisons. INDEX TERMS Image enhancement, multilayer decomposition, nonlinear mapping function, box filter, chunk-based.

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Detail-Enhanced Multi-Scale Exposure Fusion in YUV Color Space

Cited by 78 publications

References 34 publications

Multi-Exposure Image Fusion Techniques: A Comprehensive Review

Multi-Exposure Image Fusion Techniques: A Comprehensive Review

Multi-Illumination Fusion With Crack Enhancement Using Cycle-Consistent Losses

Multilayer Fusion and Chunk-Based Transmittance Estimation for Natural Hazy Image Enhancement

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