Reconstruction of high contrast images for dynamic scenes

Raman, Shanmuganathan; Chaudhuri, Subhasis

doi:10.1007/s00371-011-0653-0

Cited by 58 publications

(32 citation statements)

References 40 publications

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“…When a pixel in the ghost map is 1, the corresponding pixel in the input frame will be included in the fusion process and vice versa. The proposed method begins by finding the reference frame that has the largest well-contrasted region (i.e., smallest saturated region) as in conventional methods [16,[24][25][26]29,30]. It needs to be noted that our method identifies the inconsistent pixels in high-contrast regions and low-contrast regions separately.…”

Section: Proposed Algorithmmentioning

confidence: 99%

“…In this paper, unlike the existing IMF estimation methods in [24,25] which use all the pixels without considering the pixel quality, we use the pixels only in the areas of 'highcontrast region without moving objects, ' which correspond to the region of g w (p) = 1 for a given image. The algorithm for estimating the IMF is graphically shown in Figure 5, where Figure 5a shows the binary map g w (p).…”

Section: Construction Of G Lmentioning

confidence: 99%

“…Finally, the IMF is estimated by comparing only the colored pixels of Figure 5b,c. Figure 5d shows the plot of the pairs of pixel values from these colored regions of Figure 5b,c, and the red line is considered the IMF, which is obtained by curve fitting the dots by the fourth-order polynomial [24,25]. Then, we define the tolerance range (upper and lower blue lines in Figure 5) of intensity variation from the IMF, which means that a dot out of this range is a pair of pixels where one of the pixels possibly belongs to a moving object.…”

Section: Construction Of G Lmentioning

confidence: 99%

“…In addition, a gradient domain approach was introduced that gives smaller weights to inconsistent pixels [23]. The IMF is used to exclude region of inconsistent pixels in the fusion process [24,25], where the images are over-segmented and the IMF is used to detect the inconsistent regions. In our previous work [26], we proposed a method to detect inconsistent pixels based on a test of the reciprocity law of exposure and the measure of zero-mean normalized cross-correlation (ZNCC).…”

Section: Introductionmentioning

confidence: 99%

“…Hence, feature-based methods often give incorrect results in low-contrast regions where the pixel values are about to be saturated due to over-or under-exposure and also in low-textured regions. For these regions, we exploit the IMF between the images, which was successfully used in [13,14,24,25]. In this paper, the IMF is estimated from regions having high ZNCC only, because other regions are saturated or moving object regions that have low credibility in estimating the IMF.…”

Section: Introductionmentioning

confidence: 99%

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Probabilistic motion pixel detection for the reduction of ghost artifacts in high dynamic range images from multiple exposures

Cho

2014

J Image Video Proc

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This paper presents an algorithm for compositing a high dynamic range (HDR) image from multi-exposure images, considering inconsistent pixels for the reduction of ghost artifacts. In HDR images, ghost artifacts may appear when there are moving objects while taking multiple images with different exposures. To prevent such artifacts, it is important to detect inconsistent pixels caused by moving objects in consecutive frames and then to assign zero weights to the corresponding pixels in the fusion process. This problem is formulated as a binary labeling problem based on a Markov random field (MRF) framework, the solution of which is a binary map for each exposure image, which identifies the pixels to be excluded in the fusion process. To obtain the ghost map, the distribution of zeromean normalized cross-correlation (ZNCC) of an image with respect to the reference frame is modeled as a mixture of Gaussian functions, and the parameters of this function are used to design the energy function. However, this method does not well detect faint objects that are in low-contrast regions due to over-or under-exposure, because the ZNCC does not show much difference in such areas. Hence, we obtain an additional ghost map for the low-contrast regions, based on the intensity relationship between the frames. Specifically, the intensity mapping function (IMF) between the frames is estimated using pixels from high-contrast regions without inconsistent pixels, and pixels out of the tolerance range of the IMF are considered moving pixels in the low-contrast regions. As a result, inconsistent pixels in both the low-and high-contrast areas are well found, and thus, HDR images without noticeable ghosts can be obtained.

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Section: Proposed Algorithmmentioning

confidence: 99%

Section: Construction Of G Lmentioning

confidence: 99%

Section: Construction Of G Lmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Probabilistic motion pixel detection for the reduction of ghost artifacts in high dynamic range images from multiple exposures

Cho

2014

J Image Video Proc

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Deep High Dynamic Range Imaging with Large Foreground Motions

Tai

et al. 2018

Lecture Notes in Computer Science

210

250

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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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