29,759 research outputs found
Single Image LDR to HDR Conversion using Conditional Diffusion
Digital imaging aims to replicate realistic scenes, but Low Dynamic Range
(LDR) cameras cannot represent the wide dynamic range of real scenes, resulting
in under-/overexposed images. This paper presents a deep learning-based
approach for recovering intricate details from shadows and highlights while
reconstructing High Dynamic Range (HDR) images. We formulate the problem as an
image-to-image (I2I) translation task and propose a conditional Denoising
Diffusion Probabilistic Model (DDPM) based framework using classifier-free
guidance. We incorporate a deep CNN-based autoencoder in our proposed framework
to enhance the quality of the latent representation of the input LDR image used
for conditioning. Moreover, we introduce a new loss function for LDR-HDR
translation tasks, termed Exposure Loss. This loss helps direct gradients in
the opposite direction of the saturation, further improving the results'
quality. By conducting comprehensive quantitative and qualitative experiments,
we have effectively demonstrated the proficiency of our proposed method. The
results indicate that a simple conditional diffusion-based method can replace
the complex camera pipeline-based architectures
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Όλ¬Έ (λ°μ¬) -- μμΈλνκ΅ λνμ : 곡과λν μ κΈ°Β·μ»΄ν¨ν°κ³΅νλΆ, 2020. 8. μ‘°λ¨μ΅.This dissertation presents a deep end-to-end network for high dynamic range (HDR) imaging of dynamic scenes with background and foreground motions. Generating an HDR image from a sequence of multi-exposure images is a challenging process when the images have misalignments by being taken in a dynamic situation. Hence, recent methods first align the multi-exposure images to the reference by using patch matching, optical flow, homography transformation, or attention module before the merging. In this dissertation, a deep network that synthesizes the aligned images as a result of blending the information from multi-exposure images is proposed, because explicitly aligning photos with different exposures is inherently a difficult problem. Specifically, the proposed network generates under/over-exposure images that are structurally aligned to the reference, by blending all the information from the dynamic multi-exposure images. The primary idea is that blending two images in the deep-feature-domain is effective for synthesizing multi-exposure images that are structurally aligned to the reference, resulting in better-aligned images than the pixel-domain blending or geometric transformation methods. Specifically, the proposed alignment network consists of a two-way encoder for extracting features from two images separately, several convolution layers for blending deep features, and a decoder for constructing the aligned images. The proposed network is shown to generate the aligned images with a wide range of exposure differences very well and thus can be effectively used for the HDR imaging of dynamic scenes. Moreover, by adding a simple merging network after the alignment network and training the overall system end-to-end, a performance gain compared to the recent state-of-the-art methods is obtained.
This dissertation also presents a deep end-to-end network for video super-resolution (VSR) of frames with motions. To reconstruct an HR frame from a sequence of adjacent frames is a challenging process when the images have misalignments. Hence, recent methods first align the adjacent frames to the reference by using optical flow or adding spatial transformer network (STN). In this dissertation, a deep network that synthesizes the aligned frames as a result of blending the information from adjacent frames is proposed, because explicitly aligning frames is inherently a difficult problem. Specifically, the proposed network generates adjacent frames that are structurally aligned to the reference, by blending all the information from the neighbor frames. The primary idea is that blending two images in the deep-feature-domain is effective for synthesizing frames that are structurally aligned to the reference, resulting in better-aligned images than the pixel-domain blending or geometric transformation methods. Specifically, the proposed alignment network consists of a two-way encoder for extracting features from two images separately, several convolution layers for blending deep features, and a decoder for constructing the aligned images. The proposed network is shown to generate the aligned frames very well and thus can be effectively used for the VSR. Moreover, by adding a simple reconstruction network after the alignment network and training the overall system end-to-end, A performance gain compared to the recent state-of-the-art methods is obtained.
In addition to each HDR imaging and VSR network, this dissertation presents a deep end-to-end network for joint HDR-SR of dynamic scenes with background and foreground motions. The proposed HDR imaging and VSR networks enhace the dynamic range and the resolution of images, respectively. However, they can be enhanced simultaneously by a single network. In this dissertation, the network which has same structure of the proposed VSR network is proposed. The network is shown to reconstruct the final results which have higher dynamic range and resolution. It is compared with several methods designed with existing HDR imaging and VSR networks, and shows both qualitatively and quantitatively better results.λ³Έ νμλ
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2 Related Work 7
2.1 High Dynamic Range Imaging 7
2.1.1 Rejecting Regions with Motions 7
2.1.2 Alignment Before Merging 8
2.1.3 Patch-based Reconstruction 9
2.1.4 Deep-learning-based Methods 9
2.1.5 Single-Image HDRI 10
2.2 Video Super-resolution 11
2.2.1 Deep Single Image Super-resolution 11
2.2.2 Deep Video Super-resolution 12
3 High Dynamic Range Imaging 13
3.1 Motivation 13
3.2 Proposed Method 14
3.2.1 Overall Pipeline 14
3.2.2 Alignment Network 15
3.2.3 Merging Network 19
3.2.4 Integrated HDR imaging network 20
3.3 Datasets 21
3.3.1 Kalantari Dataset and Ground Truth Aligned Images 21
3.3.2 Preprocessing 21
3.3.3 Patch Generation 22
3.4 Experimental Results 23
3.4.1 Evaluation Metrics 23
3.4.2 Ablation Studies 23
3.4.3 Comparisons with State-of-the-Art Methods 25
3.4.4 Application to the Case of More Numbers of Exposures 29
3.4.5 Pre-processing for other HDR imaging methods 32
4 Video Super-resolution 36
4.1 Motivation 36
4.2 Proposed Method 37
4.2.1 Overall Pipeline 37
4.2.2 Alignment Network 38
4.2.3 Reconstruction Network 40
4.2.4 Integrated VSR network 42
4.3 Experimental Results 42
4.3.1 Dataset 42
4.3.2 Ablation Study 42
4.3.3 Capability of DSBN for alignment 44
4.3.4 Comparisons with State-of-the-Art Methods 45
5 Joint HDR and SR 51
5.1 Proposed Method 51
5.1.1 Feature Blending Network 51
5.1.2 Joint HDR-SR Network 51
5.1.3 Existing VSR Network 52
5.1.4 Existing HDR Network 53
5.2 Experimental Results 53
6 Conclusion 58
Abstract (In Korean) 71Docto
Fully-automatic inverse tone mapping algorithm based on dynamic mid-level tone mapping
High Dynamic Range (HDR) displays can show images with higher color contrast levels and peak luminosities than the common Low Dynamic Range (LDR) displays. However, most existing video content is recorded and/or graded in LDR format. To show LDR content on HDR displays, it needs to be up-scaled using a so-called inverse tone mapping algorithm. Several techniques for inverse tone mapping have been proposed in the last years, going from simple approaches based on global and local operators to more advanced algorithms such as neural networks. Some of the drawbacks of existing techniques for inverse tone mapping are the need for human intervention, the high computation time for more advanced algorithms, limited low peak brightness, and the lack of the preservation of the artistic intentions. In this paper, we propose a fully-automatic inverse tone mapping operator based on mid-level mapping capable of real-time video processing. Our proposed algorithm allows expanding LDR images into HDR images with peak brightness over 1000 nits, preserving the artistic intentions inherent to the HDR domain. We assessed our results using the full-reference objective quality metrics HDR-VDP-2.2 and DRIM, and carrying out a subjective pair-wise comparison experiment. We compared our results with those obtained with the most recent methods found in the literature. Experimental results demonstrate that our proposed method outperforms the current state-of-the-art of simple inverse tone mapping methods and its performance is similar to other more complex and time-consuming advanced techniques
High dynamic range imaging for archaeological recording
This paper notes the adoption of digital photography as a primary recording means within archaeology, and reviews some issues and problems that this presents. Particular attention is given to the problems of recording high-contrast scenes in archaeology and High Dynamic Range imaging using multiple exposures is suggested as a means of providing an archive of high-contrast scenes that can later be tone-mapped to provide a variety of visualisations. Exposure fusion is also considered, although it is noted that this has some disadvantages. Three case studies are then presented (1) a very high contrast photograph taken from within a rock-cut tomb at Cala Morell, Menorca (2) an archaeological test pitting exercise requiring rapid acquisition of photographic records in challenging circumstances and (3) legacy material consisting of three differently exposed colour positive (slide) photographs of the same scene. In each case, HDR methods are shown to significantly aid the generation of a high quality illustrative record photograph, and it is concluded that HDR imaging could serve an effective role in archaeological photographic recording, although there remain problems of archiving and distributing HDR radiance map data
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