Single-Shot High Dynamic Range Imaging via Multiscale Convolutional Neural Network

Vien, An Gia; Lee, Chul

doi:10.1109/access.2021.3078457

Cited by 6 publications

(2 citation statements)

References 61 publications

(106 reference statements)

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“…Considering that the sensitivity of the human visual system (HVS) to image noise depends on local brightness, contrast, and structure [34], so the SSIM is used to measures the structural similarity between images. The SSIM between I and d I are described as follows: c are minimal numbers used to avoid the denominator being equal to 0.…”

Section: ) Structural Similaritymentioning

confidence: 99%

Deep Learning for Screen-Shot Image Demoiréing: A Survey

Hou

Wang

et al. 2022

IEEE Access

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Image demoiréing is an important image processing technology in computer vision, used to remove the moiré from images and improve the image quality. In recent years, the image demoiréing technique based on the deep learning method has gained more attention and achieved good results, but it still has some limitations. This paper aims to provide a review and perspective on the recent advances in deep learning-based image demoiréing techniques. First, the definition and production principle of the image moiré pattern are given. Common datasets and image quality evalution methods in demoiréing studies are analyzed. Then two internationally famous competitions in image demoiréing are introduced. Second, the research status of the supervised demoiréing technique is summarized from four dimensions: sampling method, model network design, baseline model, and training learning strategy. Recent progress made by the mainstream model of unsupervised deep learning in the field of image demoiréing is summarized. The typical application of the image demoiréing technique in panel defect detection and digital radiography is analyzed. The performance and the image quality of the above mentioned models based on different data sets are evaluated in detail. Finally, this paper analyzes and forelocks the problems to be solved in the coming years. INDEX TERMS Image demoiréing, screen-shot image, deep learning, convolutional neural networks (CNN)

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Section: ) Structural Similaritymentioning

confidence: 99%

Deep Learning for Screen-Shot Image Demoiréing: A Survey

Hou

Wang

et al. 2022

IEEE Access

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“…Problem 1 causes an artifact called a ''ghost'' in the fused image. A multi-exposure image can be obtained without spanning different points in time by using multiple sensors [18], [19] or spatially varying exposures [20], [21], [22] to avoid the occurrence of Problem 1. However, in these methods, the number of images that can be acquired at one time is limited due to the constraint of the hardware architecture.…”

Section: Introductionmentioning

confidence: 99%

Fast and Effective Scene Segmentation Method for Luminance Adjustment of Multi-Exposure Image

Kojima

Suetake

2023

IEEE Access

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Multi-exposure image fusion is an important task for high dynamic range imaging. The performance of a fusion method is highly dependent on the quality of the input multi-exposure image. However, it is often difficult to obtain a multi-exposure image thoroughly covering the dynamic range of a scene. In such a situation, some areas are unclear in all the images contained by the multi-exposure image, leading to a low-visibility fusion result. To overcome this problem, Kinoshita and Kiya proposed scene segmentation-based luminance adjustment (SSLA). In SSLA, the scene is segmented into regions and the luminance of each region is adjusted so that the region has mid-level brightness. A fusion result with better visibility can be obtained by fusing the luminance adjusted multi-exposure image. Kinoshita and Kiya proposed two scene segmentation approaches for SSLA. One of them is fast but sometimes fails to segment dark areas and bright areas into different regions. The other can generate a better scene segmentation result but it requires a time-consuming iterative process. In this paper, we propose a new scene segmentation method for SSLA. The proposed scene segmentation method uses information obtained from the nonlinear luminance distribution and area occupancy of the segmented regions. A fast implementation for the proposed scene segmentation method is also described. Experimental results show that the proposed scene segmentation method is fast and stably generates good scene segmentation results.INDEX TERMS High dynamic range imaging, multi-exposure image fusion, scene segmentation-based luminance adjustment.

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Exposure-Aware Dynamic Weighted Learning for Single-Shot HDR Imaging

Vien

Lee

2022

Lecture Notes in Computer Science

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Single-Shot High Dynamic Range Imaging via Multiscale Convolutional Neural Network

Cited by 6 publications

References 61 publications

Deep Learning for Screen-Shot Image Demoiréing: A Survey

Deep Learning for Screen-Shot Image Demoiréing: A Survey

Fast and Effective Scene Segmentation Method for Luminance Adjustment of Multi-Exposure Image

Exposure-Aware Dynamic Weighted Learning for Single-Shot HDR Imaging

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