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Open AccessJournal ArticleDOI

Retina inspired tone mapping method for high dynamic range images

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TLDR
The proposed tone mapping method performs robustly well on a wide variety of images, providing competitive results against the state-of-the-art methods in terms of visual inspection, objective metrics and observer scores.
Abstract
The limited dynamic range of regular screens restricts the display of high dynamic range (HDR) images. Inspired by retinal processing mechanisms, we propose a tone mapping method to address this problem. In the retina, horizontal cells (HCs) adaptively adjust their receptive field (RF) size based on the local stimuli to regulate the visual signals absorbed by photoreceptors. Using this adaptive mechanism, the proposed method compresses the dynamic range locally in different regions, and has the capability of avoiding halo artifacts around the edges of high luminance contrast. Moreover, the proposed method introduces the center-surround antagonistic RF structure of bipolar cells (BCs) to enhance the local contrast and details. Extensive experiments show that the proposed method performs robustly well on a wide variety of images, providing competitive results against the state-of-the-art methods in terms of visual inspection, objective metrics and observer scores.

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Citations
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Journal ArticleDOI

Vision models fine-tuned by cinema professionals for High Dynamic Range imaging in movies

TL;DR: This work proposes effective tone mapping and inverse tone mapping algorithms for production, post-production and exhibition and believes these methods bring the field closer to having fully automated solutions for important challenges for the cinema industry that are currently solved manually or sub-optimally.
Journal ArticleDOI

Learning to Adapt to Light

TL;DR: LA-Net as discussed by the authors proposes a biologically inspired method to handle light-related image enhancement tasks with a unified network (called LA-Net), which decouples light adaptation from multiple lightrelated tasks with frequency-based decomposition, and a unified module is built inspired by biological visual adaptation to achieve light adaptation in the low-frequency pathway.
Proceedings ArticleDOI

CNN-based morphological decomposition of X-ray images for details and defects contrast enhancement

TL;DR: In this article, two CNN branches are used to decompose an input image into its two morphological components, i.e., local textures and piecewise smooth (cartoon) parts.
Journal ArticleDOI

Personal Communication Technologies for Smart Spaces Density-Based Clustering for Content and Color Adaptive Tone Mapping

TL;DR: An effective way of clustering-based content and color adaptive tone mapping algorithm by using fast search and find of density peak clustering, which reduces the loss of local structure and allows better adaption of color in images.
Journal ArticleDOI

Unsupervised HDR Image and Video Tone Mapping via Contrastive Learning

TL;DR: Cao et al. as mentioned in this paper proposed a unified framework (IVTMNet) for unsupervised image and video tone mapping, which combines domain and instance-based contrastive learning loss.
References
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Journal ArticleDOI

LIME: Low-Light Image Enhancement via Illumination Map Estimation

TL;DR: Experiments on a number of challenging low-light images are present to reveal the efficacy of the proposed LIME and show its superiority over several state-of-the-arts in terms of enhancement quality and efficiency.
Journal ArticleDOI

Naturalness Preserved Enhancement Algorithm for Non-Uniform Illumination Images

TL;DR: Experimental results demonstrate that the proposed enhancement algorithm can not only enhance the details but also preserve the naturalness for non-uniform illumination images.
Journal ArticleDOI

Shape google: Geometric words and expressions for invariant shape retrieval

TL;DR: This article uses multiscale diffusion heat kernels as “geometric words” to construct compact and informative shape descriptors by means of the “bag of features” approach, and shows that shapes can be efficiently represented as binary codes.
Journal ArticleDOI

Eye smarter than scientists believed: Neural computations in circuits of the retina

TL;DR: In this review, recent progress in understanding the computations performed in the vertebrate retina and how they are implemented by the neural circuitry are summarized.
Journal ArticleDOI

Learning a Deep Single Image Contrast Enhancer from Multi-Exposure Images

TL;DR: This paper proposes to use the convolutional neural network (CNN) to train a SICE enhancer, and builds a large-scale multi-exposure image data set, which contains 589 elaborately selected high-resolution multi-Exposure sequences with 4,413 images.
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