Construction of high dynamic range image based on gradient information transformation

Liu, Yanyu; Zhou, Dongming; Nie, Rencan; R, Hou; Ding, Zhiwei

doi:10.1049/iet-ipr.2019.0118

Cited by 14 publications

(9 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Using the gradient, the luminance was reconstructed based on the Harr wavelet. In [58], according to local contrast, brightness, and spatial structure, the author first calculated three weights of the input images and combined them using the multi-scale Laplacian pyramid. The dense scale-invariant feature transformation was used to compute the local contrast around each pixel position and measure the weight maps.…”

Section: Gradient-based Methodsmentioning

confidence: 99%

“…Regardless, it can be used as an evaluation reference. Huang [1]; Yang [13]; MEF-GAN [17]; Liu [58]; Yang [62]; Martorell [64]; Li [77]; Liu [80]; Chen [81]; Deepfuse [84]; MEFNet [86]; U2fusion [88]; Gao [89]; LXN [123]; Shao [134]; Wu [135]; Merianos [136] The larger, the better 2 Q AB/F Nie [6]; Liu [38]; LST [42]; Hayat [115]; Shao [134] The larger, the better 3 MEF-SSIMc Martorell [64]; UMEF [87]; Shao [134] The larger, the better 4 Mutual information (MI) Nie [6]; Wang [34]; Gao [89]; Choi [137] The larger, the better 5 Peak signal-to-noise ratio (PSNR) Kim [7]; MEF-GAN [17]; Chen [81]; U2fusion [88]; Gao [89]; Shao [134] The larger, the better 6 Natural image quality evaluator (NIQE) Huang [1]; Hayat [115]; Wu [135]; Xu [138] The smaller, the better 7 Standard deviation (SD) MEF-GAN [17]; Gao [89]; Wu [135] The larger, the better 8 Entropy (EN) Gao [89]; Wu…”

Section: Objective Quantitative Comparisonmentioning

confidence: 99%

See 1 more Smart Citation

Multi-Exposure Image Fusion Techniques: A Comprehensive Review

Liu

Song

et al. 2022

Remote Sensing

View full text Add to dashboard Cite

Multi-exposure image fusion (MEF) is emerging as a research hotspot in the fields of image processing and computer vision, which can integrate images with multiple exposure levels into a full exposure image of high quality. It is an economical and effective way to improve the dynamic range of the imaging system and has broad application prospects. In recent years, with the further development of image representation theories such as multi-scale analysis and deep learning, significant progress has been achieved in this field. This paper comprehensively investigates the current research status of MEF methods. The relevant theories and key technologies for constructing MEF models are analyzed and categorized. The representative MEF methods in each category are introduced and summarized. Then, based on the multi-exposure image sequences in static and dynamic scenes, we present a comparative study for 18 representative MEF approaches using nine commonly used objective fusion metrics. Finally, the key issues of current MEF research are discussed, and a development trend for future research is put forward.

show abstract

Section: Gradient-based Methodsmentioning

confidence: 99%

Section: Objective Quantitative Comparisonmentioning

confidence: 99%

Multi-Exposure Image Fusion Techniques: A Comprehensive Review

Liu

Song

et al. 2022

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Besides having the traditional graphics rendering function, modern image processors are also providing support in various technological fields such as image processing, virtual reality, artificial intelligence and biomedicine [20]. With the continuous enhancement of the computing power of the image processors and the further popularization of the image processor programming technology, image processors will be needed in more and more fields to improve their work efficiency in the future [21,22]. Like a central processing unit (CPU), an image processor communicates with the outside world through a bus, and it also has its own cache system and computing unit, but these two are applied in different scenarios and to process different objects [23].…”

Section: Parallel Acceleration Technology For Image Processorsmentioning

confidence: 99%

Application of Dynamic Image Fusion Technology in Development of Innovation Capabilities of College Students Majoring in Art Design

Wang

2021

Int. J. Emerg. Technol. Learn.

View full text Add to dashboard Cite

Art design is based on art and images, in which dynamic images require pre-processing and fusion. This paper explores how to apply the dynamic image fusion technology in the development of the innovation capabilities of college students majoring in art design. The research results show that image sequences with different exposures can be directly formed into low dynamic range images through multi-exposure fusion, or the high dynamic range can be restored first, and then converted to a low dynamic range image through tone mapping. The main principles for innovation development in art design education include conformity, student-centeredness, and overall optimization. The dynamic image fusion enhancement algorithm is mainly divided into five functional modules, which are used to calculate multi-scale gradient, structure tensor, and target gradient for the fused image, solve the steepest descent method and output normalized images, respectively

show abstract

“…Image enhancement [1][2][3][4] is a major research hotspot of interest within the various computer vision tasks [5,6]. Very often, partial darkness or even global darkness are likely to exist in photos taken by the camera for some reasons, such as weak light, low exposure and backlight.…”

Section: Introductionmentioning

confidence: 99%

AMBCR: Low‐light image enhancement via attention guided multi‐branch construction and Retinex theory

Zhou

Nie

et al. 2021

IET Image Processing

Self Cite

View full text Add to dashboard Cite

Due to different lighting environments and equipment limitations, low-light images have high noise, low contrast and unobvious colours. The main purpose of low-light image enhancement is to preserve the details and suppress noise as much as possible while improving the contrast of the image. Here, different networks are first combined to construct a multi-branch module for features extraction, and use the module and Retinex theory to extract the reflection map of the image. Then an attention mechanism is introduced into the multi-branch construction to balance the feature weight of each branch, and get the final result by the reconstruction module. The Retinex theory is used to calculate the L 1 loss and the gradient loss for the intermediate feature map of the entire model to train our framework. The entire process is completed in an end-to-end-way, which avoids the handcrafted reconstruction rules and reduces the workload. What's more, a large number of experiments demonstrate that the proposed framework performs better results than stateof-the-art algorithms in both quantitative and qualitative evaluations of image enhancement.

show abstract

Construction of high dynamic range image based on gradient information transformation

Cited by 14 publications

References 46 publications

Multi-Exposure Image Fusion Techniques: A Comprehensive Review

Multi-Exposure Image Fusion Techniques: A Comprehensive Review

Application of Dynamic Image Fusion Technology in Development of Innovation Capabilities of College Students Majoring in Art Design

AMBCR: Low‐light image enhancement via attention guided multi‐branch construction and Retinex theory

Contact Info

Product

Resources

About