scholarly journals A Weighted Histogram-Based Tone Mapping Algorithm for CT Images

Algorithms ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 111 ◽  
Author(s):  
David Völgyes ◽  
Anne Martinsen ◽  
Arne Stray-Pedersen ◽  
Dag Waaler ◽  
Marius Pedersen
Keyword(s):  
Dynamic Range ◽  
High Dynamic Range ◽  
Histogram Equalization ◽  
Ct Images ◽  
Tone Mapping ◽  
Local Contrast ◽  
Kernel Size ◽  
Mapping Algorithm ◽  
High Dynamic ◽  
Weighted Histogram

Computed Tomography (CT) images have a high dynamic range, which makes visualization challenging. Histogram equalization methods either use spatially invariant weights or limited kernel size due to the complexity of pairwise contribution calculation. We present a weighted histogram equalization-based tone mapping algorithm which utilizes Fast Fourier Transform for distance-dependent contribution calculation and distance-based weights. The weights follow power-law without distance-based cut-off. The resulting images have good local contrast without noticeable artefacts. The results are compared to eight popular tone mapping operators.

New segmentation-based tone mapping algorithm for high dynamic range image

2017 ◽  
Author(s):  
Weiwei Duan ◽  
Huinan Guo ◽  
Zuofeng Zhou ◽  
Huimin Huang ◽  
Jianzhong Cao
Keyword(s):  
Dynamic Range ◽  
High Dynamic Range ◽  
Tone Mapping ◽  
Range Image ◽  
High Dynamic Range Image ◽  
Mapping Algorithm ◽  
High Dynamic

scholarly journals K-means clustering based tone-mapping operator for high dynamic range video

2021 ◽  
Author(s):  
Negar Taherian
Keyword(s):  
Clustering Algorithm ◽  
Dynamic Range ◽  
State Of The Art ◽  
High Dynamic Range ◽  
Tone Mapping ◽  
Scene Change Detection ◽  
Mapping Algorithms ◽  
Mapping Algorithm ◽  
High Dynamic ◽  
Video Input

The field of high dynamic range (HDR) imaging deals with capturing the luminance of a natural scene, usually varying between 10−3 to 105 cd/m2 and displaying it on digital devices with much lower dynamic range. Here, we present a novel tone mapping algorithm that is based on K-means clustering. Our algorithm takes into account the color information within a frame and using k-means clustering algorithm it builds clusters on the intensities within an image and shifts the values within each cluster to a displayable dynamic range. We also implement a scene change detection to reduce the running time of our algorithm by using the cluster information from the previous frame for frames within the same scene. To reduce the flicker effect, we proposed a new method that multiplies a leaky integer to the centroid values of our clustering results. Our algorithm runs in O( N logK + K logK ) for an image with N input luminance levels and K output levels. We also show how to extend the method to handle video input. We display that our algorithm gives comparable results to state-of-the- art tone mapping algorithms. We test our algorithm on a number of standard high dynamic range images and video sequences and provide qualitative and quantitative comparisons to a number of state-of-the-art tone mapping algorithms for videos.

scholarly journals JSI-GAN: GAN-Based Joint Super-Resolution and Inverse Tone-Mapping with Pixel-Wise Task-Specific Filters for UHD HDR Video

2020 ◽  
Vol 34 (07) ◽  
pp. 11287-11295
Author(s):  
Soo Ye Kim ◽  
Jihyong Oh ◽  
Munchurl Kim
Keyword(s):  
Contrast Enhancement ◽  
Dynamic Range ◽  
Super Resolution ◽  
High Dynamic Range ◽  
Divide And Conquer ◽  
Tone Mapping ◽  
Local Contrast ◽  
Joint Learning ◽  
High Dynamic ◽  
Inverse Tone Mapping

Joint learning of super-resolution (SR) and inverse tone-mapping (ITM) has been explored recently, to convert legacy low resolution (LR) standard dynamic range (SDR) videos to high resolution (HR) high dynamic range (HDR) videos for the growing need of UHD HDR TV/broadcasting applications. However, previous CNN-based methods directly reconstruct the HR HDR frames from LR SDR frames, and are only trained with a simple L2 loss. In this paper, we take a divide-and-conquer approach in designing a novel GAN-based joint SR-ITM network, called JSI-GAN, which is composed of three task-specific subnets: an image reconstruction subnet, a detail restoration (DR) subnet and a local contrast enhancement (LCE) subnet. We delicately design these subnets so that they are appropriately trained for the intended purpose, learning a pair of pixel-wise 1D separable filters via the DR subnet for detail restoration and a pixel-wise 2D local filter by the LCE subnet for contrast enhancement. Moreover, to train the JSI-GAN effectively, we propose a novel detail GAN loss alongside the conventional GAN loss, which helps enhancing both local details and contrasts to reconstruct high quality HR HDR results. When all subnets are jointly trained well, the predicted HR HDR results of higher quality are obtained with at least 0.41 dB gain in PSNR over those generated by the previous methods. The official Tensorflow code is available at https://github.com/JihyongOh/JSI-GAN.

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