An Objective Deghosting Quality Metric for HDR Images

This paper proposes the first non-flow-based deep framework for high dynamic range (HDR) imaging of dynamic scenes with large-scale foreground motions. In state-of-the-art deep HDR imaging, input images are first aligned using optical flows before merging, which are still error-prone due to occlusion and large motions. In stark contrast to flow-based methods, we formulate HDR imaging as an image translation problem without optical flows. Moreover, our simple translation network can automatically hallucinate plausible HDR details in the presence of total occlusion, saturation and under-exposure, which are otherwise almost impossible to recover by conventional optimization approaches. Our framework can also be extended for different reference images. We performed extensive qualitative and quantitative comparisons to show that our approach produces excellent results where color artifacts and geometric distortions are significantly reduced compared to existing state-of-the-art methods, and is robust across various inputs, including images without radiometric calibration.

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“…Comparison against flow-based method [14]. Images are obtained from the Kalantari's dataset [14] and Tursun's dataset [27,28]. Fig.…”

Section: Implementation Detailsmentioning

confidence: 99%

Deep High Dynamic Range Imaging with Large Foreground Motions

Tai

et al. 2018

Computer Vision – ECCV 2018

191

243

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“…For objective quality assessment, we employed Tursun et al's [36] deghosting quality assessment measures. These indices were selected because they provide separate assessments for the dynamic range of HDR images and errors in magnitude and direction of gradients.…”

Section: Experimentation and Resultsmentioning

confidence: 99%

“…However, subjective assessment is user dependent and, therefore, it is better to assess the quality objectively. In this regard, we have compared the quality of the proposed method using the quantitative measures: dynamic range, difference in magnitude of gradient and difference in direction of gradient, as proposed by Tursun et al in [36].…”

Section: Subjective Assessmentmentioning

confidence: 99%

“…In [36], the authors proposed the calculation of dynamic range of non-static pixels. This was proposed to avoid the influence of static regions, as dynamic range from them could make the dynamic range contribution from non-static regions insignificant.…”

Section: Objective Assessmentmentioning

confidence: 99%

“…One possible reason for this could be that these test images are relatively bright as compared to other images in the dataset and Sens' method works better on brighter images as compared to the proposed method. In [36], the authors also hypothesize that neither should an HDR image have gradients that are not present in the LDR input images, nor should it be missing gradients that are present in the LDR images. To assess this, the authors propose to calculate the change in magnitude of gradients as: Comparing the dynamic range of HDR images obtained using the method proposed by Sen et al [23], it can be observed from Table 1 that the dynamic range of HDR image of the 'Flag' and 'Gallery1' data sets is slightly higher for Sen as compared to the proposed method.…”

Section: Objective Assessmentmentioning

confidence: 99%

See 2 more Smart Citations

High Dynamic Range Image Deghosting Using Spectral Angle Mapper

Khan

2019

Computers

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The generation of high dynamic range (HDR) images in the presence of moving objects results in the appearance of blurred objects. These blurred objects are called ghosts. Over the past decade, numerous deghosting techniques have been proposed for removing blurred objects from HDR images. These methods may try to identify moving objects and maximize dynamic range locally or may focus on removing moving objects and displaying static objects while enhancing the dynamic range. The resultant image may suffer from broken/incomplete objects or noise, depending upon the type of methodology selected. Generally, deghosting methods are computationally intensive; however, a simple deghosting method may provide sufficiently acceptable results while being computationally inexpensive. Inspired by this idea, a simple deghosting method based on the spectral angle mapper (SAM) measure is proposed. The advantage of using SAM is that it is intensity independent and focuses only on identifying the spectral—i.e., color—similarity between two images. The proposed method focuses on removing moving objects while enhancing the dynamic range of static objects. The subjective and objective results demonstrate the effectiveness of the proposed method.

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