Tone mapping

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

Evaluation of reverse tone mapping through varying exposure conditions

Abstract: Most existing image content has low dynamic range (LDR), which necessitates effective methods to display such legacy content on high dynamic range (HDR) devices. Reverse tone mapping operators (rTMOs) aim to take LDR content as input and adjust the contrast intelligently to yield output that recreates the HDR experience. In this paper we show that current rTMO approaches fall short when the input image is not exposed properly. More specifically, we report a series of perceptual experiments using a Brightside HDR display and show that, while existing rTMOs perform well for under-exposed input data, the perceived quality degrades substantially with over-exposure, to the extent that in some cases subjects prefer the LDR originals to images that have been treated with rTMOs. We show that, in these cases, a simple rTMO based on gamma expansion avoids the errors introduced by other methods, and propose a method to automatically set a suitable gamma value for each image, based on the image key and empirical data. We validate the results both by means of perceptual experiments and using a recent image quality metric, and show that this approach enhances visible details without causing artifacts in incorrectly-exposed regions. Additionally, we perform another set of experiments which suggest that spatial artifacts introduced by rTMOs are more disturbing than inaccuracies in the expanded intensities. Together, these findings suggest that when the quality of the input data is unknown, reverse tone mapping should be handled with simple, non-aggressive methods to achieve the desired effect.

A Hybrid l1-l0 Layer Decomposition Model for Tone Mapping

Abstract: Tone mapping aims to reproduce a standard dynamic range image from a high dynamic range image with visual information preserved. State-of-the-art tone mapping algorithms mostly decompose an image into a base layer and a detail layer, and process them accordingly. These methods may have problems of halo artifacts and over-enhancement, due to the lack of proper priors imposed on the two layers. In this paper, we propose a hybrid l1-l0 decomposition model to address these problems. Specifically, an l1 sparsity term is imposed on the base layer to model its piecewise smoothness property. An l0 sparsity term is imposed on the detail layer as a structural prior, which leads to piecewise constant effect. We further propose a multiscale tone mapping scheme based on our layer decomposition model. Experiments show that our tone mapping algorithm achieves visually compelling results with little halo artifacts, outperforming the state-of-the-art tone mapping algorithms in both subjective and objective evaluations.

Bilateral guided upsampling

Abstract: We present an algorithm to accelerate a large class of image processing operators. Given a low-resolution reference input and output pair, we model the operator by fitting local curves that map the input to the output. We can then produce a full-resolution output by evaluating these low-resolution curves on the full-resolution input. We demonstrate that this faithfully models state-of-the-art operators for tone mapping, style transfer, and recoloring. The curves are computed by lifting the input into a bilateral grid and then solving for the 3D array of affine matrices that best maps input color to output color per x, y, intensity bin. We enforce a smoothness term on the matrices which prevents false edges and noise amplification. We can either globally optimize this energy, or quickly approximate a solution by locally fitting matrices and then enforcing smoothness by blurring in grid space. This latter option reduces to joint bilateral upsampling [Kopf et al. 2007] or the guided filter [He et al. 2013], depending on the choice of parameters. The cost of running the algorithm is reduced to the cost of running the original algorithm at greatly reduced resolution, as fitting the curves takes about 10 ms on mobile devices, and 1--2 ms on desktop CPUs, and evaluating the curves can be done with a simple GPU shader.

Interactive Tone Mapping

Abstract: Tone mapping and visual adaptation are crucial for the generation of static, photorealistic images. A largely unexplored problem is the simulation of adaptation and its changes over time on the visual appearance of a scene. These changes are important in interactive applications, including walkthroughs or games, where effects such as dazzling, slow dark-adaptation, or more subtle effects of visual adaptation can greatly enhance the immersive impression. In applications such as driving simulators, these changes must be modeled in order to reproduce the visibility conditions of real-world situations. In this paper, we address the practical issues of interactive tone mapping and propose a simple model of visual adaptation. We describe a multi-pass interactive rendering method that computes the average luminance in a first pass and renders the scene with a tone mapping operator in the second pass. We also propose several extensions to the tone mapping operator of Ferwerda et al. [FPSG96]. We demonstrate our model for the display of global illumination solutions and for interactive walkthroughs.

Generalized Equalization Model for Image Enhancement

Abstract: In this paper, we propose a generalized equalization model for image enhancement. Based on our analysis on the relationships between image histogram and contrast enhancement/white balancing, we first establish a generalized equalization model integrating contrast enhancement and white balancing into a unified framework of convex programming of image histogram. We show that many image enhancement tasks can be accomplished by the proposed model using different configurations of parameters. With two defining properties of histogram transform, namely contrast gain and nonlinearity, the model parameters for different enhancement applications can be optimized. We then derive an optimal image enhancement algorithm that theoretically achieves the best joint contrast enhancement and white balancing result with trading-off between contrast enhancement and tonal distortion. Subjective and objective experimental results show favorable performances of the proposed algorithm in applications of image enhancement, white balancing and tone correction. Computational complexity of the proposed method is also analyzed.