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Progressive Image Inpainting with Full-Resolution Residual Network
Zongyu Guo, Zhibo Chen, Tao Yu, Jiale Chen, Sen Liu
TL;DR
Learning-based inpainting methods often lack progressive completion, while earlier progressive approaches have difficulty with irregular holes. This paper proposes FRRN, using residual blocks and a strategy to maintain intermediate restoration quality. The authors report better quantitative and qualitative results than previous algorithms on Places2.
Problem
Many learning-based inpainting methods do not use progressive completion, and the cited progressive generative approach cannot handle irregular masks or squared masks of varying sizes.
Source: §1 · p. 2Method
FRRN progressively fills irregular holes with full-resolution residual blocks, using multiple blocks per dilation step and step loss to support intermediate restoration quality.
Source: §1 · p. 2Results
FRRN performs much better than previous algorithms quantitatively and qualitatively on Places2, and shows promising performance on CelebA.
Source: §1 · p. 2Takeaways & Limitations
The authors present FRRN as an effective method for progressively filling irregular holes while preserving intermediate reconstruction quality.
Source: §1 · p. 2, §5 · p. 8The paper identifies error propagation in diffusion-based progressive inpainting: a failed restoration step can worsen later results.
Source: §1 · p. 2
Abstract
from arXiv · showhide
Recently, learning-based algorithms for image inpainting achieve remarkable progress dealing with squared or irregular holes. However, they fail to generate plausible textures inside damaged area because there lacks surrounding information. A progressive inpainting approach would be advantageous for eliminating central blurriness, i.e., restoring well and then updating masks. In this paper, we propose full-resolution residual network (FRRN) to fill irregular holes, which is proved to be effective for progressive image inpainting. We show that well-designed residual architecture facilitates feature integration and texture prediction. Additionally, to guarantee completion quality during progressive inpainting, we adopt N Blocks, One Dilation strategy, which assigns several residual blocks for one dilation step. Correspondingly, a step loss function is applied to improve the performance of intermediate restorations. The experimental results demonstrate that the proposed FRRN framework for image inpainting is much better than previous methods both quantitatively and qualitatively. Our codes are released at: \url{https://github.com/ZongyuGuo/Inpainting_FRRN}.
1 INTRODUCTION
The introduction frames progressive restoration of irregular holes as a challenging but practical need, then presents FRRN and its intermediate-quality strategy. The authors report strong results against prior methods.
1 INTRODUCTION
- Progressive diffusion-based restoration can propagate errors when a failed step worsens subsequent restorations.The introduction positions this as a risk of step-by-step restoration.Source: §1 · p. 2
- Existing learning-based methods generally do not use progressive inpainting, while the cited progressive generative networks cannot handle irregular masks or squared masks of varying sizes.The introduction identifies adaptive completion of irregular holes while maintaining restoration quality throughout the process as a practical challenge.Source: §1 · p. 2
- The proposed FRRB maintains a full-resolution branch that facilitates feature integration and texture prediction.The block generates residuals and can update masks; its full-resolution branch is also reported to accelerate convergence.Source: §1 · p. 2
- FRRN uses N Blocks, One Dilation and step loss to preserve intermediate restoration quality while progressively updating masks.The authors report that both choices contribute to the method’s state-of-the-art performance.Source: §1 · p. 2
- FRRN performs much better than previous algorithms quantitatively and qualitatively on Places2, with promising performance on CelebA.The framework progressively fills irregular holes using full-resolution residual blocks.Source: §1 · p. 2
2 RELATED WORK
Related work reviews traditional and learning-based inpainting, then motivates residual architecture as a fit for progressive completion.
2 RELATED WORK
- The related-work section organizes inpainting research into traditional and learning-based methods, then considers residual architectures within DNNs.Source: §2 · p. 2
2.1 Traditional Methods
- Traditional inpainting methods are divided into diffusion-based and patch-based approaches, with local propagation and patch copying as their respective strategies.The review notes that diffusion methods struggle with large missing regions, while patch-based methods can be computationally costly.Source: §2.1 · p. 2
2.2 Learning-based Methods
- Learning-based methods use DNN and GAN semantic information, but most cited approaches do not progressively complete holes; the cited exception cannot handle irregular masks or varying mask sizes.The review describes Zhang et al.’s method as dividing hole filling into phases, while noting its mask-shape and size constraints.Source: §2.2 · pp. 2–3
2.3 Residual Architecture
- Residual blocks are presented as suitable for progressive inpainting because each can target a specified region, and stacking them matches the stepwise strategy.The review also connects residual architectures with network convergence and task reconstruction.Source: §2.3 · p. 3
3 APPROACH
The approach section introduces FRRN for progressive inpainting of irregular holes and outlines its components and training losses.
3 APPROACH
- The approach targets progressive image inpainting with irregular holes using a full-resolution residual network.Source: §3 · p. 3
- The section first analyzes the full-resolution residual block, a component of FRRN.Source: §3 · p. 3
- The approach section then covers FRRN’s inner architecture, N Blocks, One Dilation network building, and loss functions including middle step loss.Source: §3 · p. 3
3.1 Full-Resolution Residual Block
FRRB is the basic component of FRRN, generating residuals in a limited region during each dilation step. Its full-resolution and feature-integration branches generate residuals and update masks, enabling progressive completion while preserving clean areas.
3.1.1 Functionality of FRRB.
- FRRB adds a generated residual only within the original masked area, leaving clean-area values unchanged.It also updates the mask, supporting progressive image completion.Source: §3.1.1 · p. 3
3.1.2 Specific Architecture of FRRB.
- The two parallel branches both generate residuals and update masks, combining full-resolution processing with feature integration.The low-resolution branch often leads future feature integration, while the full-resolution branch helps preserve important features during inference.Source: §3.1.2 · pp. 3–4, Figure 2 · p. 3
- Together, the branches dilate clean areas by 6 pixels and erode masks by 6 pixels per FRRB.The high-resolution branch reaches 6 pixels; the other branch reaches 6 pixels, sometimes more because of asymmetric mask upsampling and downsampling.Source: §3.1.2 · pp. 3–4
- As missing areas shrink, FRRB’s action scope grows; once all are covered, it becomes a vanilla residual block that refines pixels inside the original holes.This describes the transition from region-limited residual generation to refinement across the originally missing areas.Source: §3.1.2 · p. 4
3.2 Full-Resolution Residual Network
FRRN uses FRRBs to progressively inpaint irregular holes, grouping blocks into dilation steps rather than simply stacking them. The network uses two blocks per step and eight steps in its experimental configuration.
3.2 Full-Resolution Residual Network
- FRRN builds progressive inpainting for irregular holes from FRRBs, grouping blocks into dilation steps rather than simply stacking them.The grouping is intended to guarantee intermediate restoration quality.Source: §3.2 · p. 4
3.2.1 N Blocks, One Dilation Strategy.
- With N = 2, each dilation step contains two FRRBs; the mask remains unchanged after the first and updates after the second.The authors state that one FRRB may not restore adequately, making unconstrained intermediate outputs unacceptable and potentially propagating errors.Source: §3.2.1 · p. 4
- A corresponding step loss is applied to support plausible completion quality, especially in preceding dilation modules.The passage identifies this loss as an addition to the N Blocks, One Dilation strategy.Source: §3.2.1 · p. 4
3.2.2 Length of Network.
- The experimental network uses eight dilation steps, or 16 FRRBs total, considering GPU memory requirements and mask size.At N = 2, each module can dilate clean areas by 12 pixels; small masks may be covered in one module, while large masks require more.Source: §3.2.2 · p. 4, §3.2.1 · p. 4
3.3 Loss Functions
The loss functions assess pixel-level restoration and global style similarity, while a step loss constrains intermediate completions during progressive inpainting.
3.3 Loss Functions
- The combined losses measure pixel-level recovery and global style similarity, with middle step loss intended to maintain completion quality after each dilation step.The formulation includes reconstruction, style, adversarial, and step losses.Source: §3.3 · p. 5
3.3.1 Step Loss.
- Step loss constrains intermediate recovery and adapts its dissimilarity region to the current mask status.It is accumulated across the eight dilation modules and becomes reconstruction loss over the original masked region once all missing areas are covered.Source: §3.3.1 · p. 5
3.3.2 Reconstruction Loss.
- Reconstruction loss uses an L1 distance between recovered and ground-truth images to constrain pixel-level restoration.The passage identifies the ground-truth image and given mask as inputs to the reconstruction-loss formulation.Source: §3.3.2 · p. 5
3.3.3 Adversarial Loss.
- Adversarial loss trains a discriminator alongside FRRN as generator to preserve natural textures and structures in missing areas.The experiments use spectral normalization to stabilize discriminator convergence and PatchGAN for high-resolution adversarial training.Source: §3.3.3 · p. 5
3.3.4 Style Loss.
- Style loss compares VGG19 feature-map Gram matrices with an L1 norm to harmonize recovered areas with the background.It also constrains consistency across recovered images and has been reported to eliminate checkerboard artifacts [21].Source: §3.3.4 · p. 5
4 EXPERIMENTS
The experiments explore the N Blocks, One Dilation strategy, compare FRRN with prior algorithms, and assess critical structures and techniques.
4 EXPERIMENTS
4.1 Explore N Blocks, One Dilation Strategy
The experiments select N=2 for the N Blocks, One Dilation strategy after testing a lighter FRRN on CelebA, with larger N offering no obvious improvement.
4.1 Explore N Blocks, One Dilation Strategy
- Simply stacking eight FRRBs can cause error propagation, motivating several residual blocks per dilation module.The study seeks a suitable N for this strategy.Source: §4.1 · p. 5
- Figure 5 presents a comparison with previous work.Source: Figure 5 · p. 6
- The exploratory study uses a three-module light FRRN trained on 200,000 CelebA images and tested on 2,600, with parameter counts held nearly constant across N.The study evaluates masks covering 10%–20% and 20%–30% of image pixels using 12,000 irregular masks.Source: §4.1 · p. 6
- Two Blocks, One Dilation performs best among tested N values, so the final FRRN uses 16 FRRBs across eight dilation modules.The comparison uses PSNR and SSIM; stacking more FRRBs in one dilation module makes no obvious improvement.Source: §4.1 · p. 6
4.2 Comparison
FRRN is evaluated on Places2 and visually compared with recent methods, with results showing plausible structures and stronger quantitative reconstruction quality. Figure 6 also presents an intermediate restoration alongside the final output.
4.2 Comparison.
- The Places2 evaluation uses all 36,500 validation images, with images and masks resized to 256 × 256 for comparison.The final model has 16 FRRBs in eight dilation modules; the network supports input resolutions that are integer multiples of eight.Source: §4.2 · p. 6, §4.2.1 · p. 6
4.2.2 Qualitative Comparison.
- FRRN preserves plausible structures without a separate edge-generation network, and its residual updates progressively complete contours.The authors relate this behavior to morphological dilation and erosion.Source: §4.2.2 · p. 6
4.2.3 Quantitative Comparison.
- FRRN performs much better than other methods on PSNR, SSIM, and L1 reconstruction quality.The comparison uses Places2; the supplied passage does not give the specific metric values.Source: §4.2.3 · p. 6
4.2.2 Qualitative Comparison.
- Figure 6 shows the damaged image, a coarse restoration after one dilation module, the final restoration, and ground truth.The intermediate restorations are reported as satisfactory under the Two Blocks, One Dilation strategy and step loss; examples include complicated portraits as well as Places2 images.Source: §4.2.2 · p. 6, Figure 6 · p. 7
4.3 Ablation Study
The ablations examine the full-resolution branch, step loss, and dilation-module count. The branch improves convergence and PSNR, step loss improves restoration across mask sizes, and larger masks require more dilation steps.
4.3.1 Effective Residual Block: FRRB.
- The full-resolution branch accelerates convergence and achieves higher PSNR than a conventional residual-block replacement in light FRRN.The comparison uses six FRRBs or conventional blocks and masks covering 10%–20% of image regions; Figure 7 reports the initial 6,000 iterations.Source: §4.3.1 · p. 7, Figure 7 · p. 7
4.3.2 Performance of Step Loss.
- Step loss improves PSNR restoration results across mask sizes in light FRRN experiments on CelebA.The authors attribute the result to preventing error propagation associated with free restoration without step loss.Source: §4.3.2 · p. 7, Table 2 · p. 7
4.3.3 Influence of Network Length.
- Three dilation modules nearly reach peak performance for masks covering 10%–20%, but three steps are insufficient for large masks.The final FRRN uses eight modules with two FRRBs each and handles holes up to 96 pixels in diameter; the light three-module network handles holes up to 36 pixels in diameter.Source: §4.3.3 · p. 8, Figure 8 · p. 8
5 CONCLUSION
The conclusion presents FRRN as a progressive method for filling irregular holes, combining full-resolution residual blocks with mask updates. It also notes that very large holes increase parameter requirements.
5 CONCLUSION.
- FRRN is presented as achieving state-of-the-art image-inpainting results with a full-resolution residual network.The conclusion characterizes the framework as effective for progressively filling irregular holes.Source: §5 · p. 8
- FRRB maintains a full-resolution representation that facilitates texture reconstruction and network convergence.The conclusion describes the residual framework as particularly suitable for progressive image completion.Source: §5 · p. 8
- For progressive restoration, FRRN uses a Two Blocks, One Dilation strategy and step loss to maintain intermediate reconstruction quality.The strategy restores well before updating masks.Source: §5 · p. 8
- Very large holes substantially increase the required parameter count; increasing dilation stride or reducing parameters per module are proposed future directions.Source: §5 · p. 8