Tone mapping

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

System and process for generating high dynamic range video

Abstract: A system and process for generating High Dynamic Range (HDR) video is presented which involves first capturing a video image sequence while varying the exposure so as to alternate between frames having a shorter and longer exposure. The exposure for each frame is set prior to it being captured as a function of the pixel brightness distribution in preceding frames. Next, for each frame of the video, the corresponding pixels between the frame under consideration and both preceding and subsequent frames are identified. For each corresponding pixel set, at least one pixel is identified as representing a trustworthy pixel. The pixel color information associated with the trustworthy pixels is then employed to compute a radiance value for each pixel set to form a radiance map. A tone mapping procedure can then be performed to convert the radiance map into an 8-bit representation of the HDR frame.

Evaluation of tone mapping operators using a High Dynamic Range display

Abstract: Tone mapping operators are designed to reproduce visibility and the overall impression of brightness, contrast and color of the real world onto limited dynamic range displays and printers. Although many tone mapping operators have been published in recent years, no thorough psychophysical experiments have yet been undertaken to compare such operators against the real scenes they are purporting to depict. In this paper, we present the results of a series of psychophysical experiments to validate six frequently used tone mapping operators against linearly mapped High Dynamic Range (HDR) scenes displayed on a novel HDR device. Individual operators address the tone mapping issue using a variety of approaches and the goals of these techniques are often quite different from one another. Therefore, the purpose of this investigation was not simply to determine which is the "best" algorithm, but more generally to propose an experimental methodology to validate such operators and to determine the participants' impressions of the images produced compared to what is visible on a high contrast ratio display.

Video enhancement using per-pixel virtual exposures

Abstract: We enhance underexposed, low dynamic range videos by adaptively and independently varying the exposure at each photoreceptor in a post-process. This virtual exposure is a dynamic function of both the spatial neighborhood and temporal history at each pixel. Temporal integration enables us to expand the image's dynamic range while simultaneously reducing noise. Our non-linear exposure variation and denoising filters smoothly transition from temporal to spatial for moving scene elements. Our virtual exposure framework also supports temporally coherent per frame tone mapping. Our system outputs restored video sequences with significantly reduced noise, increased exposure time of dark pixels, intact motion, and improved details.

Deep reverse tone mapping

Abstract: Inferring a high dynamic range (HDR) image from a single low dynamic range (LDR) input is an ill-posed problem where we must compensate lost data caused by under-/over-exposure and color quantization. To tackle this, we propose the first deep-learning-based approach for fully automatic inference using convolutional neural networks. Because a naive way of directly inferring a 32-bit HDR image from an 8-bit LDR image is intractable due to the difficulty of training, we take an indirect approach; the key idea of our method is to synthesize LDR images taken with different exposures (i.e., bracketed images) based on supervised learning, and then reconstruct an HDR image by merging them. By learning the relative changes of pixel values due to increased/decreased exposures using 3D deconvolutional networks, our method can reproduce not only natural tones without introducing visible noise but also the colors of saturated pixels. We demonstrate the effectiveness of our method by comparing our results not only with those of conventional methods but also with ground-truth HDR images.

A perceptual framework for contrast processing of high dynamic range images

Abstract: Image processing often involves an image transformation into a domain that is better correlated with visual perception, such as the wavelet domain, image pyramids, multiscale-contrast representations, contrast in retinex algorithms, and chroma, lightness, and colorfulness predictors in color-appearance models. Many of these transformations are not ideally suited for image processing that significantly modifies an image. For example, the modification of a single band in a multiscale model leads to an unrealistic image with severe halo artifacts. Inspired by gradient domain methods, we derive a framework that imposes constraints on the entire set of contrasts in an image for a full range of spatial frequencies. This way, even severe image modifications do not reverse the polarity of contrast. The strengths of the framework are demonstrated by aggressive contrast enhancement and a visually appealing tone mapping, which does not introduce artifacts. In addition, we perceptually linearize contrast magnitudes using a custom transducer function. The transducer function has been derived especially for the purpose of HDR images, based on the contrast-discrimination measurements for high-contrast stimuli.