computer-vision-11

Tone Mapping

3260 papers • 126 benchmarks • 313 datasets

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Most implemented papers

Unprocessing Images for Learned Raw Denoising

B. Mildenhall, J. Barron, Tianfan Xue, Tim Brooks, Jiawen Chen, Dillon Sharlet•Mon Nov 26 2018

This work presents 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.

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A Two-stage Deep Network for High Dynamic Range Image Reconstruction

R. A. Naqvi, Mithun Biswas, S. Sharif, Kim Sungjun•Sun Apr 18 2021

The qualitative and quantitative comparisons demonstrate that the proposed method can outperform the existing LDR to HDR works with a marginal difference and can reconstruct plausible HDR images without presenting any visual artefacts.

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Dirty Pixels: Towards End-to-end Image Processing and Perception

V. Sitzmann, Felix Heide, Gordon Wetzstein, Steven Diamond, Stephen P. Boyd, Frank D. Julca-Aguilar•Sun Jan 22 2017

This work proposes an end-to-end differentiable architecture that jointly performs demosaicking, denoising, deblurring, tone-mapping, and classification and shows that state-of-the-art ISPs discard information that is essential in corner cases, where conventional imaging and perception stacks fail.

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Deep Recursive HDRI: Inverse Tone Mapping Using Generative Adversarial Networks

Siyeong Lee, Gwon Hwan An, Suk-ju Kang•Fri Sep 07 2018

The proposed method is the first framework to create high dynamic range images based on the estimated multi-exposure stack using the conditional generative adversarial network structure and is significantly similar to the ground truth than other state-of-the-art algorithms.

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ExpandNet: A Deep Convolutional Neural Network for High Dynamic Range Expansion from Low Dynamic Range Content

Thomas Bashford-rogers, Kurt Debattista, Demetris Marnerides, Jonathan Hatchett•Mon Mar 05 2018

This paper presents a method for generating HDR content from LDR content based on deep Convolutional Neural Networks (CNNs) termed ExpandNet, which accepts LDR images as input and generates images with an expanded range in an end‐to‐end fashion.

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Deep SR-ITM: Joint Learning of Super-Resolution and Inverse Tone-Mapping for 4K UHD HDR Applications

Jihyong Oh, Munchurl Kim, S. Kim•Wed Apr 24 2019

This paper proposes a joint super-resolution (SR) and inverse tone-mapping (ITM) framework, called Deep SR-ITM, which learns the direct mapping from LR SDR video to their HR HDR version, and shows good subjective quality with increased contrast and details, outperforming the previous joint SR and ITM method.

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Side Window Filtering

Hui Yin, Yuanhao Gong, G. Qiu•Thu May 16 2019

This work proposes a new Side Window Filtering (SWF) technique which aligns the window's side or corner with the pixel being processed and demonstrates that implementing the SWF principle can effectively prevent artifacts such as color leakage associated with the conventional implementation.

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

Jihyong Oh, Munchurl Kim, S. Kim•Mon Sep 09 2019

This paper takes 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 subnet and a local contrast enhancement (LCE) subnet.

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Image Demoireing with Learnable Bandpass Filters

A. Leonardis, Shanxin Yuan, Bolun Zheng, G. Slabaugh•Tue Mar 31 2020

Experimental results on two public datasets show that the novel multiscale bandpass convolutional neural network (MBCNN) outperforms state-of-the-art methods by a large margin.

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STAR: A Structure-aware Lightweight Transformer for Real-time Image Enhancement

Ping Luo, Yitong Jiang, Jun Jiang, Jinwei Gu, Zhaoyang Zhang, Xiaogang Wang•Thu Sep 30 2021

Image and video enhancement such as color constancy, low light enhancement, and tone mapping on smartphones is challenging, because high-quality images should be achieved efficiently with a limited resource budget. Unlike prior works that either used very deep CNNs or large Trans-former models, we propose a structure-aware lightweight Transformer, termed STAR, for real-time image enhancement. STAR is formulated to capture long-range dependencies between image patches, which naturally and implicitly captures the structural relationships of different regions in an image. STAR is a general architecture that can be easily adapted to different image enhancement tasks. Extensive experiments show that STAR can effectively boost the quality and efficiency of many tasks such as illumination enhancement, auto white balance, and photo retouching, which are indispensable components for image processing on smartphones. For example, STAR reduces model complexity and improves image quality compared to the recent state-of-the-art [19] on the MIT-Adobe FiveK dataset [7] (i.e., 1.8dB PSNR improvements with 25% parameters and 13% float operations.)

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