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计算机视觉 - 图像修复概述

最编程 2024-10-06 07:09:47
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目录

1. Deterministic Image Inpainting 判别器图像修复

1.1. sigle-shot framework

(1) Generators

(2) training objects / Loss Functions

1.2. two-stage framework

2. Stochastic Image Inpainting 随机图像修复

2.1. VAE-based methods

2.2. GAN-based methods

2.3. Flow-based methods

2.4. MLM-based methods

2.5. Diffusion model-based methods

3. text-guided image inpainting ⽂本引导的图像修复

4. Inpainting Mask 掩码机制

(1) regular mask

(2) irregular mask

5. Loss Function 损失函数

6. Dataset 图像修复领域数据集

(1) faces(CelebA & CelebA-HQ)

(2) real-world encountered scenes(Places2)

(3) street scenes(Paris)

(4) texture(DTD)

(5) objects (ImageNet)

7. Evaluation Protocol 评估指标

7.1. pixel-aware metrics

7.2. (human) perception-aware metriics

8. Performance Evaluation 表现评估

8.1 Representative Image Inpainting Methods

8.2 Loss Functions 

9. Inpainting-based Application 基于图像修复的领域应⽤

(1) Object Removal

(2) Text Editing

(3) Old Photo Restoration

(4) Image Compression

(5) Text-guided image editing

Reference

1. Deterministic Image Inpainting 判别器图像修复

1.1. sigle-shot framework
(1) Generators
1) mask-aware design
2) attention mechanism
3) multi-scale aggregation
4) transform domain
5) encoder-decoder connection
6) deep prior guidance
(2) training objects / Loss Functions
1) Pixel-wise reconstruction loss
2) perceptual loss
3) style loss
4) adversarial loss
5) prevalent training objectives
1.2. two-stage framework
(1) coarse-to-fiine methods
(2) structure-then-texture methods

2. Stochastic Image Inpainting 随机图像修复

2.1. VAE-based methods
2.2. GAN-based methods
2.3. Flow-based methods
2.4. MLM-based methods
2.5. Diffusion model-based methods
(1) sample stratage design
(2) computational cost reduction

3. text-guided image inpainting ⽂本引导的图像修复

4. Inpainting Mask 掩码机制

(1) regular mask
(2) irregular mask

5. Loss Function 损失函数

同1-1.1-(2) training objects

6. Dataset 图像修复领域数据集

(1) faces(CelebA & CelebA-HQ)
(2) real-world encountered scenes(Places2)
(3) street scenes(Paris)
(4) texture(DTD)
(5) objects (ImageNet)

7. Evaluation Protocol 评估指标

7.1. pixel-aware metrics

focus on the precision of reconstructed pixels

(1) l1 error
(1) l2 error
(3) PSNR(peak signal-to-noise ratio)
(4) SSIM(the structure similarity index)
(5) MS-SSIM(muti-scale SSIM)
7.2. (human) perception-aware metriics

the visual perception quality

(1) FID(Frechet Inception diistance)
(2) LPIPS(learned perceptual image patch similarity)
(3) P/U-IDS(pair-unpair Inception discriminative score)

8. Performance Evaluation 表现评估

8.1 Representative Image Inpainting Methods
(1) Models: RFR, MADF, DSI, CR-Fill, CoModGAN, LGNet, RePaint
(2) Dataset: CeleBA-HQ, Places2
(3) Mask: M1, M2, M3, M4, M5, M6
(4) Metrics: l1, PSNR, SSIM, MS-SSIM, FID, LP-IPS
(5) Loss: pixes reconstruction loss, perceptual loss, resnetpl loss, style loss, stylemeanstd,
percept-style loss, lsgan
8.2 Loss Functions 

同1-1.1-(2) training objects

9. Inpainting-based Application 基于图像修复的领域应⽤

(1) Object Removal
(2) Text Editing
(3) Old Photo Restoration
(4) Image Compression
(5) Text-guided image editing

Reference

1. Deep Learning-based Image and Video Inpainting: A Survey

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