Tone mapping

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

Fast template matching in non-linear tone-mapped images

Abstract: We propose a fast pattern matching scheme termed Matching by Tone Mapping (MTM) which allows matching under non-linear tone mappings. We show that, when tone mapping is approximated by a piecewise constant function, a fast computational scheme is possible requiring computational time similar to the fast implementation of Normalized Cross Correlation (NCC). In fact, the MTM measure can be viewed as a generalization of the NCC for non-linear mappings and actually reduces to NCC when mappings are restricted to be linear. The MTM is shown to be invariant to non-linear tone mappings, and is empirically shown to be highly discriminative and robust to noise.

From pixels to physics: Probabilistic color de-rendering

Abstract: Consumer digital cameras use tone-mapping to produce compact, narrow-gamut images that are nonetheless visually pleasing. In doing so, they discard or distort substantial radiometric signal that could otherwise be used for computer vision. Existing methods attempt to undo these effects through deterministic maps that de-render the reported narrow-gamut colors back to their original wide-gamut sensor measurements. Deterministic approaches are unreliable, however, because the reverse narrow-to-wide mapping is one-to-many and has inherent uncertainty. Our solution is to use probabilistic maps, providing uncertainty estimates useful to many applications. We use a non-parametric Bayesian regression technique — local Gaussian process regression — to learn for each pixel's narrow-gamut color a probability distribution over the scene colors that could have created it. Using a variety of consumer cameras we show that these distributions, once learned from training data, are effective in simple probabilistic adaptations of two popular applications: multi-exposure imaging and photometric stereo. Our results on these applications are better than those of corresponding deterministic approaches, especially for saturated and out-of-gamut colors.

Local tone mapping using the K-means algorithm and automatic gamma setting

Abstract: Tone mapping (TM) algorithms reproduce the high dynamic range (HDR) images on low dynamic range (LDR) display devices such as monitors or printers. In this paper, we propose a local TM algorithm, in which the HDR input is segmented using the K-means algorithm and a display gamma parameter is set automatically for each segmented region. The proposed TM algorithm computes the luminance of an input that is the radiance map generated from a set of LDR images acquired with varying exposure settings. Then, according to the bilateral filtered luminance, an image is divided into a number of regions using the K-means algorithm. The display gamma value is set automatically according to the mean value of each region. Then, the tone of HDR image is reproduced by a local TM method with adaptive gamma value. We generate the tone-mapped image using the proposed local TM. Computer simulation with real LDR images shows the effectiveness of the proposed local TM algorithm in terms of the visual quality as well as the local contrast. It can be used for contrast and color enhancement in various display and acquisition devices.

Automatic tone mapping for images

Abstract: An image processing method for processing image data comprises the steps of obtaining scanpath data corresponding to original image data, determining regions of interest for the original image data based on the obtained scanpath data, and mapping tone values of the original image data corresponding to each region of interest in order to obtain tone-mapped image data.

A novel approach for denoising and enhancement of extremely low-light video

Abstract: In this paper, a novel approach for noise reduction and enhancement of extremely low-light video is proposed. For noise removal, a motion adaptive temporal filtering based on a Kalman structured updating is presented. Dynamic range of denoised video is increased by adjustment of RGB histograms using Gamma correction with adaptive clipping thresholds. Finally, residual noise is removed using a nonlocal means (NLM) denoising filter. The proposed method works directly on the color filter array (CFA) raw video for achieving low memory consumption1.