Image Contrast Enhancement with High Dynamic Range using Singlescale Retinex

2021 ◽  
Author(s):  
Hideaki Tanaka ◽  
Akira Taguchi
Keyword(s):  
Contrast Enhancement ◽  
Dynamic Range ◽  
High Dynamic Range ◽  
Image Contrast ◽  
Image Contrast Enhancement ◽  
High Dynamic

P-108: Contrast Enhancement based on Advanced Local Dimming System for High Dynamic Range LCDs

2017 ◽  
Vol 48 (1) ◽  
pp. 1667-1669 ◽  
Author(s):  
Ooksang Yoo ◽  
Sangjin Nam ◽  
JoungMi Choi ◽  
Seungjin Yoo ◽  
Kyu-Jin Kim ◽  
...  
Keyword(s):  
Contrast Enhancement ◽  
Dynamic Range ◽  
High Dynamic Range ◽  
High Dynamic ◽  
Local Dimming

Memetic Algorithm Image Enhancement for Preserving Mean Brightness Without Losing Image Features

2019 ◽  
Vol 19 (04) ◽  
pp. 1950020
Author(s):  
Mitra Montazeri
Keyword(s):  
Contrast Enhancement ◽  
Dynamic Range ◽  
Memetic Algorithm ◽  
Image Features ◽  
Image Contrast ◽  
Consumer Electronics ◽  
Major Step ◽  
Processing Application ◽  
Low Contrast ◽  
Image Contrast Enhancement

In the image processing application, contrast enhancement is a major step. Conventional contrast enhancement methods such as Histogram Equalization (HE) do not have satisfactory results on many different low contrast images and they also cannot automatically handle different images. These problems result in specifying parameters manually to produce high contrast images. In this paper, an automatic image contrast enhancement on Memetic algorithm (MA) is proposed. In this study, simple exploiter is proposed to improve the current image contrast. The proposed method accomplishes multi goals of preserving brightness, retaining the shape features of the original histogram and controlling excessive enhancement rate, suiting for applications of consumer electronics. Simulation results shows that in terms of visual assessment, peak signal-to-noise (PSNR) and Absolute Mean Brightness Error (AMBE) the proposed method is better than the literature methods. It improves natural looking images specifically in images with high dynamic range and the output images were applicable for products of consumer electronic.

High dynamic range for contrast enhancement

2006 ◽  
Vol 52 (4) ◽  
pp. 1454-1462 ◽  
Author(s):  
Bo Lim ◽  
Rae-hong Park ◽  
Sunghee Kim
Keyword(s):  
Contrast Enhancement ◽  
Dynamic Range ◽  
High Dynamic Range ◽  
High Dynamic

Contrast Enhancement Effect on High Dynamic Range Image Registration Using Mutual Information

2018 ◽  
Vol 2018 (1) ◽  
pp. 355-1-355-6
Author(s):  
İbrahim Atlı ◽  
Ahmet Saraçoğlu ◽  
O. Serdar Gedik
Keyword(s):  
Image Registration ◽  
Mutual Information ◽  
Contrast Enhancement ◽  
Dynamic Range ◽  
High Dynamic Range ◽  
Enhancement Effect ◽  
Range Image ◽  
High Dynamic Range Image ◽  
High Dynamic ◽  
Range Image Registration

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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