This paper tackles high-dynamic-range (HDR) image reconstruction given only a single low-dynamic-range (LDR) image as input. While the existing methods focus on minimizing the mean-squared-error (MSE) between the target and reconstructed images, we minimize a hybrid loss that consists of perceptual and adversarial losses in addition to HDR-reconstruction loss. The reconstruction loss instead of MSE is more suitable for HDR since it puts more weight on both over- and under- exposed areas. It makes the reconstruction faithful to the input. Perceptual loss enables the networks to utilize knowledge about objects and image structure for recovering the intensity gradients of saturated and grossly quantized areas. Adversarial loss helps to select the most plausible appearance from multiple solutions. The hybrid loss that combines all the three losses is calculated in logarithmic space of image intensity so that the outputs retain a large dynamic range and meanwhile the learning becomes tractable. Comparative experiments conducted with other state-of-the-art methods demonstrated that our method produces a leap in image quality.
Conventionally, HDR reconstruction has been performed by non-learning-based brightness enhancement through filtering or light-source detection. For example, bilateral filters applied to @math - @math -range three-dimensional grids work as brightness enhancement functions @cite_32 @cite_14 . However, non-learning-based approaches cannot estimate physically accurate amounts of light due to the lack of knowledge about real HDR images; thus, the quality of the estimated HDR images is limited.
@cite_14 @cite_32
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Reverse-tone-mapping operators (rTMOs) enhance low-dynamic-range images and videos for display on high-dynamic-range monitors. A common problem faced by previous rTMOs is the handling of under or overexposed content. Under such conditions, they may not be effective, and even cause loss and reversal of visible contrast. We present an rTMO based on cross-bilateral filtering that generates high-quality HDR images and videos for a wide range of exposures. Experiments performed using an objective image quality metric show that our approach is the only technique available that can gracefully enhance perceived details across a large range of image exposures. This paper presents an automatic technique for producing high-quality brightness-enhancement functions for real-time reverse tone mapping of images and videos. Our approach uses a bilateral filter to obtain smooth results while preserving sharp luminance discontinuities, and can be efficiently implemented on GPUs. We demonstrate the effectiveness of our approach by reverse tone mapping several images and videos. Experiments based on HDR visible difference predicator and on an image distortion metric indicate that the results produced by our method are less prone to visible artifacts than the ones obtained with the state-of-the-art technique for real-time automatic computation of brightness enhancement functions.