Tone mapping

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

Color HDR video processing architecture for smart camera

Abstract: This paper presents a novel FPGA architecture of high dynamic range (HDR) video processing pipeline, based on the capturing of a sequence of differently exposed images. An acquisition process enabling multi-exposure HDR as well as fast implementation of local tone mapping operator involving bilateral filtering is proposed. The HDR acquisition process is enhanced by the application of novel deghosting method, which is dedicated for hardware implementation and proposed in this paper. The hardware processing pipeline is designed with regards to efficiency and performance and the calculations are performed in fixed point arithmetic. The pipeline is suitable for programmable hardware (FPGA—Field Programmable Gate Arrays) implementation and it achieves real-time performance on full HD HDR video which overcomes state-of-the-art solutions that use local tone mapping and deghosting algorithm.

High dynamic range ultrasound imaging

Abstract: High dynamic range (HDR) imaging is a popular computational photography technique that has found its way into every modern smartphone and camera. In HDR imaging, images acquired at different exposures are combined to increase the luminance range of the final image, thereby extending the limited dynamic range of the camera. Ultrasound imaging suffers from limited dynamic range as well; at higher power levels, the hyperechogenic tissue is overexposed, whereas at lower power levels, hypoechogenic tissue details are not visible. In this work, we apply HDR techniques to ultrasound imaging, where we combine ultrasound images acquired at different power levels to improve the level of detail visible in the final image. Ultrasound images of ex vivo and in vivo tissue are acquired at different acoustic power levels and then combined to generate HDR ultrasound (HDR-US) images. The performance of five tone mapping operators is quantitatively evaluated using a similarity metric to determine the most suitable mapping for HDR-US imaging. The ex vivo and in vivo results demonstrated that HDR-US imaging enables visualizing both hyper- and hypoechogenic tissue at once in a single image. The Durand tone mapping operator preserved the most amount of detail across the dynamic range. Our results strongly suggest that HDR-US imaging can improve the utility of ultrasound in image-based diagnosis and procedure guidance.

Unprocessing Images for Learned Raw Denoising

Abstract: Machine learning techniques work best when the data used for training resembles the data used for evaluation. This holds true for learned single-image denoising algorithms, which are applied to real raw camera sensor readings but, due to practical constraints, are often trained on synthetic image data. Though it is understood that generalizing from synthetic to real data requires careful consideration of the noise properties of image sensors, the other aspects of a camera's image processing pipeline (gain, color correction, tone mapping, etc) are often overlooked, despite their significant effect on how raw measurements are transformed into finished images. To address this, we present a technique to "unprocess" images by inverting each step of an image processing pipeline, thereby allowing us to synthesize realistic raw sensor measurements from commonly available internet photos. We additionally model the relevant components of an image processing pipeline when evaluating our loss function, which allows training to be aware of all relevant photometric processing that will occur after denoising. By processing and unprocessing model outputs and training data in this way, we are able to train a simple convolutional neural network that has 14%-38% lower error rates and is 9x-18x faster than the previous state of the art on the Darmstadt Noise Dataset, and generalizes to sensors outside of that dataset as well.

Reverse tone mapping algorithm based on frequency domain

Abstract: The invention provides a reverse tone mapping algorithm based on the frequency domain. Reverse tone mapping is conducted on a low dynamic range (LDR) image, and a high dynamic range (HDR) image is generated. The reverse tone mapping algorithm comprises the following steps that firstly, the LDR image is decomposed into a color image and a brightness image; secondly, the brightness image passes through a bilateral filter, and a high-frequency part and a low-frequency part are separated and represent a detail map layer and a basic map layer respectively; thirdly, brightness enhancing (BEF) is conducted on high-brightness pixel points of the basic map layer; fourthly, the basic map layer conducts reverse tone mapping through an index function; fifthly, the basic map layer, the detail map layer and the graph color are merged, and an HDR image is generated. According to the reverse tone mapping algorithm based on the frequency domain, the high-frequency part and the low-frequency part of the image are processed in a separated mode, and image details are well reserved. Meanwhile, people can store the image details, and the image details can be transplanted into other images with unclear outlines when needed, and in addition, brightness is supplemented through a brightness enhancing method. The PSNR effect of the HDR images generated with the method is good.

Local tone mapping using sub-band decomposed multi-scale retinex for high dynamic range images

Abstract: This paper presents a tone mapping algorithm based on subband decomposed multiscale retinex (SD-MSR) to enhance details in the synthesized HDR image. The proposed algorithm consists of three steps: global compression step, local contrast enhancement step, and color processing step. We apply SD-MSR theory for effective local contrast enhancement. The experimental results show that the proposed algorithm provides outstanding results while preserving details and reducing halo effects.