Topic
High-dynamic-range imaging
About: High-dynamic-range imaging is a research topic. Over the lifetime, 766 publications have been published within this topic receiving 22577 citations.
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01 Dec 2016
TL;DR: This work introduces an objective function consisting of data fidelity and gradient-based constraint functions, and HDR images are produced via minimizing it based on the estimated CRF and the fused gradients to estimate irradiance values and gradients of HDR images.
Abstract: We propose a fusion method for high dynamic range (HDR) imaging based on the estimated camera response function (CRF) and fused gradients from input multi-exposure images. We introduce an objective function consisting of data fidelity and gradient-based constraint functions, and HDR images are produced via minimizing it. These functions are respectively defined based on the estimated CRF and the fused gradients to estimate irradiance values and gradients of HDR images. Consequently, the proposed method produces natural HDR images inferred from their multi-exposure images with fine details. Through simulations, we show that the proposed method outperforms previous ones objectively and perceptually.
1 citations
TL;DR: Wang et al. as mentioned in this paper proposed a multi-level threshold bitmap and brightness bitmap to compensate for the ghost artifacts in multi-exposure image fusion, and two weight maps were applied at each multi-exposed image and combined to get the pseudo-HDR image.
Abstract: HDR(High dynamic range) imaging is a technique to represent a dynamic range of real world. Exposure fusion is a method to obtain a pseudo-HDR image and it directly fuses multi-exposure images instead of generating the true-HDR image. However, it results ghost artifacts while fusing the multi-exposure images with moving objects. To solve this drawback, temporal consistency assessment is proposed to remove moving objects. Firstly, multi-level threshold bitmap and brightness bitmap are proposed. In addition, hue-angle constancy map between multi-exposure images is proposed for compensating a bitmap. Then, two bitmaps are combined as a temporal weight map. Spatial domain image quality assessment is used to generate a spatial weight map. Finally, two weight maps are applied at each multi-exposure image and combined to get the pseudo-HDR image. In experiments, the proposed method reduces ghost artifacts more than previous methods. The quantitative ghost-free evaluation of the proposed method is also less than others.
1 citations
06 Oct 2011
TL;DR: A non-parametric high dynamic range (HDR) fusion approach is proposed that works on raw images of single-sensor color imaging devices which incorporate the Bayer pattern, whereby the non-linear opto-electronic conversion function (OECF) is recovered before color demosaicing, so that interpolation artifacts do not aect the photometric calibration.
Abstract: A non-parametric high dynamic range (HDR) fusion approach is proposed that works on raw images of single- sensor color imaging devices which incorporate the Bayer pattern. Thereby the non-linear opto-electronic con- version function (OECF) is recovered before color demosaicing, so that interpolation artifacts do not affect the photometric calibration. Graph-based segmentation greedily clusters the exposure set into regions of roughly constant radiance in order to regularize the OECF estimation. The segmentation works on Gaussian-blurred sensor images, whereby the artificial gray value edges caused by the Bayer pattern are smoothed away. With the OECF known the 32-bit HDR radiance map is reconstructed by weighted summation from the differently exposed raw sensor images. Because the radiance map contains lower sensor noise than the individual images, it is finally demosaiced by weighted bilinear interpolation which prevents the interpolation across edges. Here, the previous segmentation results from the photometric calibration are utilized. After demosaicing, tone mapping is applied, whereby remaining interpolation artifacts are further damped due to the coarser tonal quantization of the resulting image.
1 citations
11 Jul 2016
TL;DR: This work presents a robust HDR imaging system which can deal with blurry LDR images, overcoming the limitations of most existing HDR methods.
Abstract: High dynamic range (HDR) images can show more details and luminance information in general display device than low dynamic image (LDR) images. We present a robust HDR imaging system which can deal with blurry LDR images, overcoming the limitations of most existing HDR methods. Experiments on real images show the effectiveness and competitiveness of the proposed method.
1 citations
TL;DR: In this article, a conditional adversarial generative network composed of a U-Net generator and patchGAN discriminator was designed to adaptively convert HDR images into low-dynamic range (LDR) images.
Abstract: Tone mapping is one of the main techniques to convert high-dynamic range (HDR) images into low-dynamic range (LDR) images. We propose to use a variant of generative adversarial networks to adaptively tone map images. We designed a conditional adversarial generative network composed of a U-Net generator and patchGAN discriminator to adaptively convert HDR images into LDR images. We extended previous work to include additional metrics such as tone-mapped image quality index (TMQI), structural similarity index measure, Frechet inception distance, and perceptual path length. In addition, we applied face detection on the Kalantari dataset and showed that our proposed adversarial tone mapping operator generates the best LDR image for the detection of faces. One of our training schemes, trained via 256 × 256 resolution HDR–LDR image pairs, results in a model that can generate high TMQI low-resolution 256 × 256 and high-resolution 1024 × 2048 LDR images. Given 1024 × 2048 resolution HDR images, the TMQI of the generated LDR images reaches a value of 0.90, which outperforms all other contemporary tone mapping operators.
1 citations