Tone mapping

About: Tone mapping is a research topic. Over the lifetime, 1713 publications have been published within this topic receiving 48490 citations.

Automatic exposure correction of consumer photographs

Abstract: We study the problem of automatically correcting the exposure of an input image. Generic auto-exposure correction methods usually fail in individual over-/under-exposed regions. Interactive corrections may fix this issue, but adjusting every photograph requires skill and time. This paper will automate the interactive correction technique by estimating the image specific S-shaped non-linear tone curve that best fits the input image. Our first contribution is a new Zone-based region-level optimal exposure evaluation, which would consider both the visibility of individual regions and relative contrast between regions. Then a detail-preserving S-curve adjustment is applied based on the optimal exposure to obtain the final output. We show that our approach enables better corrections comparing with popular image editing tools and other automatic methods.

Globally Optimized Linear Windowed Tone Mapping

Abstract: This paper introduces a new tone mapping operator that performs local linear adjustments on small overlapping windows over the entire input image. While each window applies a local linear adjustment that preserves the monotonicity of the radiance values, the problem is implicitly cast as one of global optimization that satisfies the local constraints defined on each of the overlapping windows. Local constraints take the form of a guidance map that can be used to effectively suppress local high contrast while preserving details. Using this method, image structures can be preserved even in challenging high dynamic range (HDR) images that contain either abrupt radiance change, or relatively smooth but salient transitions. Another benefit of our formulation is that it can be used to synthesize HDR images from low dynamic range (LDR) images.

Evaluating HDR rendering algorithms

Abstract: A series of three experiments has been performed to test both the preference and accuracy of high dynamic-range (HDR) rendering algorithms in digital photography application. The goal was to develop a methodology for testing a wide variety of previously published tone-mapping algorithms for overall preference and rendering accuracy. A number of algorithms were chosen and evaluated first in a paired-comparison experiment for overall image preference. A rating-scale experiment was then designed for further investigation of individual image attributes that make up overall image preference. This was designed to identify the correlations between image attributes and the overall preference results obtained from the first experiments. In a third experiment, three real-world scenes with a diversity of dynamic range and spatial configuration were designed and captured to evaluate seven HDR rendering algorithms for both of their preference and accuracy performance by comparing the appearance of the physical scenes and the corresponding tone-mapped images directly. In this series of experiments, a modified Durand and Dorsey's bilateral filter technique consistently performed well for both preference and accuracy, suggesting that it is a good candidate for a common algorithm that could be included in future HDR algorithm testing evaluations. The results of these experiments provide insight for understanding of perceptual HDR image rendering and should aid in design strategies for spatial processing and tone mapping. The results indicate ways to improve and design more robust rendering algorithms for general HDR scenes in the future. Moreover, the purpose of this research was not simply to find out the “best” algorithms, but rather to find a more general psychophysical experiment based methodology to evaluate HDR image-rendering algorithms. This paper provides an overview of the many issues involved in an experimental framework that can be used for these evaluations.

Exposure fusion using boosting Laplacian pyramid.

Abstract: This paper proposes a new exposure fusion approach for producing a high quality image result from multiple exposure images. Based on the local weight and global weight by considering the exposure quality measurement between different exposure images, and the just noticeable distortion-based saliency weight, a novel hybrid exposure weight measurement is developed. This new hybrid weight is guided not only by a single image's exposure level but also by the relative exposure level between different exposure images. The core of the approach is our novel boosting Laplacian pyramid, which is based on the structure of boosting the detail and base signal, respectively, and the boosting process is guided by the proposed exposure weight. Our approach can effectively blend the multiple exposure images for static scenes while preserving both color appearance and texture structure. Our experimental results demonstrate that the proposed approach successfully produces visually pleasing exposure fusion images with better color appearance and more texture details than the existing exposure fusion techniques and tone mapping operators.