Ghosting-free multi-exposure image fusion in gradient domain

Prabhakar, K. Ram; Babu, R. Venkatesh

doi:10.1109/icassp.2016.7471980

Cited by 32 publications

(14 citation statements)

References 8 publications

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“…However, we observed spurious colors in output which were not originally present in input. We follow the procedure used by Prabhakar et al [18] for chrominance channel fusion. If x 1 and x 2 denote the Cb (or Cr) channel value at any pixel location for image pairs, then the fused chrominance value x is obtained as follows,…”

Section: Testingmentioning

confidence: 99%

DeepFuse: A Deep Unsupervised Approach for Exposure Fusion with Extreme Exposure Image Pairs

Prabhakar

Srikar

Babu

2017

2017 IEEE International Conference on Computer Vision (ICCV)

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473

321

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We present a novel deep learning architecture for fusing static multi-exposure images. Current multi-exposure fusion (MEF) approaches use hand-crafted features to fuse input sequence. However, the weak hand-crafted representations are not robust to varying input conditions. Moreover, they perform poorly for extreme exposure image pairs. Thus, it is highly desirable to have a method that is robust to varying input conditions and capable of handling extreme exposure without artifacts. Deep representations have known to be robust to input conditions and have shown phenomenal performance in a supervised setting. However, the stumbling block in using deep learning for MEF was the lack of sufficient training data and an oracle to provide the ground-truth for supervision. To address the above issues, we have gathered a large dataset of multi-exposure image stacks for training and to circumvent the need for ground truth images, we propose an unsupervised deep learning framework for MEF utilizing a no-reference quality metric as loss function. The proposed approach uses a novel CNN architecture trained to learn the fusion operation without reference ground truth image. The model fuses a set of common low level features extracted from each image to generate artifact-free perceptually pleasing results. We perform extensive quantitative and qualitative evaluation and show that the proposed technique outperforms existing state-ofthe-art approaches for a variety of natural images.1 Exposure bias value indicates the amount of exposure offset from the auto exposure setting of an camera. For example, EV 1 is equal to doubling auto exposure time (EV 0).

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Section: Testingmentioning

confidence: 99%

DeepFuse: A Deep Unsupervised Approach for Exposure Fusion with Extreme Exposure Image Pairs

Prabhakar

Srikar

Babu

2017

2017 IEEE International Conference on Computer Vision (ICCV)

Self Cite

473

321

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“…Kinoshita proposed an MEF method based on the exposure compensation [26]. Prabhakar proposed an algorithm to generate a ghost-free HDR image by fusing a set of multi-exposure images in the gradient domain [27]. Liu introduced a quality measurement method to scale-invariant feature transform (SIFT) based MEF [28].…”

Section: Mef Algorithms In a Static Scenementioning

confidence: 99%

A Precise Multi-Exposure Image Fusion Method Based on Low-level Features

Chang

Luo

et al. 2020

Sensors

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Multi exposure image fusion (MEF) provides a concise way to generate high-dynamic-range (HDR) images. Although the precise fusion can be achieved by existing MEF methods in different static scenes, the corresponding performance of ghost removal varies in different dynamic scenes. This paper proposes a precise MEF method based on feature patches (FPM) to improve the robustness of ghost removal in a dynamic scene. A reference image is selected by a priori exposure quality first and then used in the structure consistency test to solve the image ghosting issues existing in the dynamic scene MEF. Source images are decomposed into spatial-domain structures by a guided filter. Both the base and detail layer of the decomposed images are fused to achieve the MEF. The structure decomposition of the image patch and the appropriate exposure evaluation are integrated into the proposed solution. Both global and local exposures are optimized to improve the fusion performance. Compared with six existing MEF methods, the proposed FPM not only improves the robustness of ghost removal in a dynamic scene, but also performs well in color saturation, image sharpness, and local detail processing.

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“…The main information is presented in the Y channel. Thus, different fusion strategies are applied in literature for Y, Cb and Cr fusion [13,37]. We can choose different methods to merge RGB channels.…”

Section: Testingmentioning

confidence: 99%

“…Therefore, fusing source image in RGB channels will ignore this correlation and cause obvious color difference. We merge the chromaticity channels of the source image by following the strategy of Prabhakar [37], which is shown as follows:…”

Section: Testingmentioning

confidence: 99%

GANFuse: a novel multi-exposure image fusion method based on generative adversarial networks

Yang

Chen

et al. 2020

Neural Comput & Applic

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In this paper, a novel multi-exposure image fusion method based on generative adversarial networks (termed as GANFuse) is presented. Conventional multi-exposure image fusion methods improve their fusion performance by designing sophisticated activity-level measurement and fusion rules. However, these methods have a limited success in complex fusion tasks. Inspired by the recent FusionGAN which firstly utilizes generative adversarial networks (GAN) to fuse infrared and visible images and achieves promising performance, we improve its architecture and customize it in the task of extreme exposure image fusion. To be specific, in order to keep content of extreme exposure image pairs in the fused image, we increase the number of discriminators differentiating between fused image and extreme exposure image pairs. While, a generator network is trained to generate fused images. Through the adversarial relationship between generator and discriminators, the fused image will contain more information from extreme exposure image pairs. Thus, this relationship can realize better performance of fusion. In addition, the method we proposed is an end-to-end and unsupervised learning model, which can avoid designing hand-crafted features and does not require a number of ground truth images for training. We conduct qualitative and quantitative experiments on a public dataset, and the experimental result shows that the proposed model demonstrates better fusion ability than existing multi-exposure image fusion methods in both visual effect and evaluation metrics.

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Ghosting-free multi-exposure image fusion in gradient domain

Cited by 32 publications

References 8 publications

DeepFuse: A Deep Unsupervised Approach for Exposure Fusion with Extreme Exposure Image Pairs

DeepFuse: A Deep Unsupervised Approach for Exposure Fusion with Extreme Exposure Image Pairs

A Precise Multi-Exposure Image Fusion Method Based on Low-level Features

GANFuse: a novel multi-exposure image fusion method based on generative adversarial networks

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