Single Exposure High Dynamic Range Image Reconstruction Based on Deep Dual-Branch Network

Ye, Nianjin; Huo, Yongqing; Liu, Shuaicheng; Li, Hanlin

doi:10.1109/access.2021.3049480

Cited by 5 publications

(12 citation statements)

References 43 publications

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“…However, there is a significant risk that inference will significantly change if even one network is not properly learned, since this method utilizes various networks. Ye et al [23] showed that the bright and dark region characteristics differed, suggesting that different restoration methods should be employed for the different regions.…”

Section: B Inverse Tone Mapping Methodsmentioning

confidence: 99%

“…Cai et al [20] segmented the image through pre-processing, but based on only the frequency component. Eilertsen et al [19] and Ye et al [23] divided the image based on the brightness value, but [19] divided the image after learning, and [23] segmented it just before the final HDR image was generated. Therefore, it is difficult to claim that [19] and [23] can effectively restore the saturated pixels since CNN weights only consider creating an image similar to the ground truth image.…”

Section: Maskmentioning

confidence: 99%

“…Eilertsen et al [19] and Ye et al [23] divided the image based on the brightness value, but [19] divided the image after learning, and [23] segmented it just before the final HDR image was generated. Therefore, it is difficult to claim that [19] and [23] can effectively restore the saturated pixels since CNN weights only consider creating an image similar to the ground truth image. Efficient reconstruction of saturated pixels becomes difficult using raw LDR image inputs.…”

Section: Maskmentioning

confidence: 99%

“…However, previous ITM methods are insufficient for finding a function similar to the inverse camera response function (CRF), and obtaining parameters that satisfy all of the photo environments is difficult. Deep learning methods have been proposed to overcome this problem, including convolutional neural network (CNN)based MEF methods [12] - [18] and CNN-based ITM methods [19] - [23]. CNN-based MEF methods [12] - [18] are able to uniformly restore the saturated pixels by generating and merging multi-exposure images.…”

Section: Introductionmentioning

confidence: 99%

“…However, they [12] - [18] have difficulty restoring the saturated pixels which do not exist in the brightness range of multi-exposure images. CNN-based ITM methods [19] - [23] have emerged to reconstruct HDR images from a single LDR image. These methods map the LDR image to the HDR image through a CNN that acts as an inverse CRF.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

HDR Image Reconstruction Using Segmented Image Learning

Lee¹,

Sunwoo

2021

IEEE Access

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Converting a low dynamic range (LDR) image into a high dynamic range (HDR) image produces an image that closely depicts the real world without requiring expensive devices. Recent deep learning developments can produce highly realistic and sophisticated HDR images. This paper proposes a deep learning method to segment the bright and dark regions from an input LDR image and reconstruct the corresponding HDR image with similar dynamic ranges in the real world. The proposed multi-stage deep learning network brightens bright regions and darkens dark regions, and features with extended brightness range are combined to form the HDR image. Dividing the LDR image into the bright and dark regions effectively implements information on lost over-exposed and under-exposed areas, reconstructing a natural HDR image with color and appearance that is similar to reality. Experimental results confirm that the proposed method achieves an 8.52% higher HDR visual difference predictor (HDR-VDP) and a 41.2% higher log exposure range than current methods. Qualitative evaluation also verifies that the proposed method generates images that are close in quality to the ground truth. INDEX TERMSHigh dynamic range imaging, inverse tone mapping, deep learning, single exposure imageFIGURE 1. Luminance ranges for high dynamic range (HDR, green), low dynamic range (LDR, blue) and human perception (red). Night Moon Indoor Sunlight Luminace (cd/m 2 ) Human perception LDR image HDR image

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Section: B Inverse Tone Mapping Methodsmentioning

confidence: 99%

Section: Maskmentioning

confidence: 99%

Section: Maskmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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HDR Image Reconstruction Using Segmented Image Learning

Lee¹,

Sunwoo

2021

IEEE Access

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LHDR: HDR Reconstruction for Legacy Content Using a Lightweight DNN

Guo

Jiang

2023

Lecture Notes in Computer Science

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HDR-DANet: Single HDR Image Reconstruction via Dual Attention

Ma,

Zhang

2024

Preprint

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Restoring high dynamic range images with a single low dynamic range image is a challenging task due to the limited brightness range captured by most consumer camera sensors, leading to missing details in overexposed or underexposed areas of the image. The current methods suffer from weak reconstruction ability for inconspicuous complex features in the extreme exposure area, resulting in arti-facts still appearing in the extreme exposure area after reconstruction. This paper proposes an end-to-end network based on dual attention to solve this problem. Specifically, this paper designs a pixel-level spatial attention mechanism with luminance mask to refine the spatial attention to the pixel level, and can focus on inconspicuous feature areas in the overexposed and underexposed regions. This paper also designs a concentrated channel attention mechanism to maximize the use of image context information, accurately assigning the channel weight and effectively extracting the complex features in the image. Qualitative and quantitative evaluations show the superiority of our approach over state-of-the-art methods.

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Single Exposure High Dynamic Range Image Reconstruction Based on Deep Dual-Branch Network

Cited by 5 publications

References 43 publications

HDR Image Reconstruction Using Segmented Image Learning

HDR Image Reconstruction Using Segmented Image Learning

LHDR: HDR Reconstruction for Legacy Content Using a Lightweight DNN

HDR-DANet: Single HDR Image Reconstruction via Dual Attention

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