Tone mapping

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

Improvement and evaluation of real-time tone mapping for high dynamic range images using gaze information

Abstract: Using gaze information in designing tone-mapping operatorshas many potentials over traditional global tone-mapping operators. Inthis paper, we evaluate a recently proposed real-time tone mapping operatorbased on gaze information and show that it is highly dependenton the input scene. We propose an important modification to the evaluatedmethod to relief this dependency and to enhance the appearance ofthe resultant images using smaller processing area. Experimental resultsshow that our method outperforms the evaluated technique.

Image enhancement for extremely low light conditions

Abstract: In this paper, a novel methodology is proposed for contrast enhancement and noise reduction in very noisy data with low dynamic range on images captured by surveillance camera under extremely low light condition. For the initial noise reduction, a motion adaptive temporal filtering based on the Kalman filter is employed. Then, the denoised image is first inverted and subsequently dehazed as a tone mapping to enhance the visibility based on the observation that the inverted low light image presents quite similar characteristics to hazy image. Finally, the remaining noise is removed using the Non-local means (NLM) denoising step. The overall approach essentially transforms very dark images progressively into more visible form and effectively reduces the high intensity noise generated by the tone mapping process. From the experimental results, effectiveness of the proposed method is validated by comparing with the most recent and leading conventional method.

A LDR image expansion method for displaying on HDR screen

Abstract: With its excellent performance, the High Dynamic Range (HDR) display becomes more and more popular in many fields. As its availability and upcoming rapid development, the large amount of Low Dynamic Range (LDR) images needs to be expanded to benefit the advantages of HDR display. This urges the research of inverse tone mapping. In this paper, we propose a concise and effective approach to extend the dynamic range of LDR image. The scheme consists of three steps, first a non-linear expansion is performed to the LDR image for initial scaling, then the local average luminance of the original image is computed, finally an classic dodging and burning operation is used to extend the dynamic range from both the low and high levels. The implementation results show that the proposed method works well for incorrect exposed images. The image quality metric results suggest that our algorithm has better performance than other methods considered in the comparison.

Learning-Based Dequantization For Image Restoration Against Extremely Poor Illumination.

Abstract: All existing image enhancement methods, such as HDR tone mapping, cannot recover A/D quantization losses due to insufficient or excessive lighting, (underflow and overflow problems). The loss of image details due to A/D quantization is complete and it cannot be recovered by traditional image processing methods, but the modern data-driven machine learning approach offers a much needed cure to the problem. In this work we propose a novel approach to restore and enhance images acquired in low and uneven lighting. First, the ill illumination is algorithmically compensated by emulating the effects of artificial supplementary lighting. Then a DCNN trained using only synthetic data recovers the missing detail caused by quantization.