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SeeSR: Towards Semantics-Aware Real-World Image Super-Resolution
Rongyuan Wu, Tao Yang, Lingchen Sun, Zhengqiang Zhang, Shuai Li, Lei Zhang
TL;DR
Real-world LR degradation can destroy local structures and make image semantics ambiguous, causing semantic errors in diffusion-based super-resolution. SeeSR learns degradation-aware soft and hard prompts and injects LR information into initial sampling noise; experiments report more realistic details and better semantic preservation.
Problem
Heavy degradation in real-world LR images can destroy local structures and create ambiguous semantics, leading to semantic errors in reconstructed HR images.
Method
SeeSR trains a degradation-aware prompt extractor that generates hard tag prompts and soft representation prompts, then combines them with LR images to control a pretrained T2I model and uses LR embedding during inference.
Results
SeeSR consistently achieves the best FID, CLIPIQA, and MUSIQ scores across four synthetic and real-world datasets, while user studies report significantly higher selection rates.
Takeaways & Limitations
SeeSR reproduces more realistic image details while preserving the semantics of Real-ISR outputs.
Takeaways & Limitations
DAPE may predict incorrect tags and inaccurately align tags with LR regions under severe degradation, while small-scale scene text remains challenging.
Abstract
from arXiv · showhide
Owe to the powerful generative priors, the pre-trained text-to-image (T2I) diffusion models have become increasingly popular in solving the real-world image super-resolution problem. However, as a consequence of the heavy quality degradation of input low-resolution (LR) images, the destruction of local structures can lead to ambiguous image semantics. As a result, the content of reproduced high-resolution image may have semantic errors, deteriorating the super-resolution performance. To address this issue, we present a semantics-aware approach to better preserve the semantic fidelity of generative real-world image super-resolution. First, we train a degradation-aware prompt extractor, which can generate accurate soft and hard semantic prompts even under strong degradation. The hard semantic prompts refer to the image tags, aiming to enhance the local perception ability of the T2I model, while the soft semantic prompts compensate for the hard ones to provide additional representation information. These semantic prompts encourage the T2I model to generate detailed and semantically accurate results. Furthermore, during the inference process, we integrate the LR images into the initial sampling noise to mitigate the diffusion model's tendency to generate excessive random details. The experiments show that our method can reproduce more realistic image details and hold better the semantics. The source code of our method can be found at https://github.com/cswry/SeeSR.
1. Introduction
Real-world image super-resolution must reconstruct perceptually realistic HR images from LR observations with complex, unknown degradation. SeeSR addresses semantic ambiguity in diffusion-based Real-ISR by learning degradation-aware prompts and incorporating LR information during inference.
- Motivation: Real-world LR images have complex, unknown degradations that create artifacts when models trained on synthetic data are applied to them.Real-ISR seeks perceptually realistic HR reconstruction despite this domain gap.
- Motivation: Pretrained T2I models provide powerful natural-image priors for Real-ISR, but semantic prompts extracted from heavily degraded LR images can be inaccurate.The paper identifies prompt coverage and degradation awareness as key requirements for reliable semantic restoration.
- Approach: SeeSR trains a degradation-aware prompt extractor that produces soft and hard semantic prompts for controlling a pretrained T2I model.The two-stage approach also incorporates the LR image into initial sampling noise to reduce excessive random details.
- Outcome: SeeSR’s experiments report more realistic detail generation while preserving image semantics in Real-ISR outputs.This is the paper’s stated overall experimental conclusion.
2. Related Work
Prior Real-ISR methods either model complex degradations or use pretrained T2I priors with semantic guidance. SeeSR is motivated by limitations in the coverage and reliability of existing semantic prompts.
- GAN-based Real-ISR: Traditional ISR methods assume simple, known degradations, limiting their effectiveness on complex and unknown real-world degradations.Recent Real-ISR work instead explores more complex degradation models to approximate real-world conditions.
- Semantic guidance: PASD uses high-level models for semantic guidance, but limited object recognition and incomplete captions can reduce semantic coverage.The paper therefore investigates more effective prompts for eliciting pretrained T2I models’ potential.
3. Methodology
SeeSR combines degradation-aware semantic prompting with a controlled T2I diffusion model for Real-ISR. Its pipeline learns prompts from degraded inputs, guides generation with soft and hard cues, and injects LR information during inference.
- 3.1. Motivation and Framework Overview: Classification-style prompts are degradation-robust but provide only global information, making them weak for scenes with multiple local objects.Their Real-ISR outputs are reported as nearly indistinguishable from null-prompt results.
- 3.1. Motivation and Framework Overview: Caption-style prompts provide richer descriptions but may scatter attention and contain degradation-induced semantic errors.The paper gives a case where an incorrect caption causes a ship to be reconstructed as a bird.
- 3.1. Motivation and Framework Overview: Tag-style prompts describe all image objects and can align with corresponding regions, but degradation can produce erroneous tags and semantic distortion.This motivates adapting tag-style prompts to be degradation-aware.
- 3.2. Degradation-Aware Prompt Extractor: DAPE aligns LR representation and logits embeddings with HR outputs from a frozen tag model, using MSE and cross-entropy losses.This alignment trains DAPE to predict high-quality semantic prompts from corrupted inputs.
- 3.2. Degradation-Aware Prompt Extractor: DAPE supplies hard tag-text prompts and soft representation embeddings, with soft prompts compensating for threshold-dependent hard-prompt recall.Hard prompts enhance local understanding through the T2I text encoder, while soft prompts avoid threshold effects.
- 3.3. Training of SeeSR Model: The controlled T2I model uses LR latents, hard prompts, and soft prompts to estimate noise for denoising HR latent representations.ControlNet and representation cross-attention provide the conditioning structure, while the pretrained SD parameters remain frozen.
- 3.4. LR Embedding in Inference: LR embedding reduces training–inference noise discrepancy, which can otherwise make diffusion models treat degradation as content, especially in smooth sky regions.The figure reports removal of many falsely generated sky details when LRE is used.
4. Experiments
SeeSR is evaluated on synthetic and real-world Real-ISR benchmarks using quantitative metrics, qualitative comparisons, user studies, and semantic restoration tasks. Across these evaluations, it produces strong perceptual quality and semantic fidelity, though diffusion-based methods trade some fidelity for realistic details.
- Experimental settings: The experiments cover ×4 Real-ISR, with evaluation on synthetic and real-world benchmarks plus COCO-based semantic restoration tests.The evaluation also includes qualitative comparisons and user studies on synthetic and real-world data.
- Quantitative comparisons: SeeSR achieves the best FID, CLIPIQA, and MUSIQ scores across all four synthetic and real-world datasets.On DIV2K-Val, it also surpasses the second-best method by more than 0.6% in LPIPS and 7.0% in DISTS.
- Quantitative comparisons: Diffusion-based methods generate more realistic details but sacrifice fidelity, whereas GAN-based methods obtain better PSNR/SSIM scores.SeeSR combines better no-reference perceptual scores with competitive full-reference measures relative to other diffusion-based methods.
- Qualitative comparisons: Qualitative comparisons show SeeSR preserving accurate scene semantics and rich details under severe degradation, unlike competing methods that produce ambiguity, omissions, or incorrect objects.Its DAPE prompts identify objects such as ships and buildings, while soft prompting can recover details such as tires even when tags omit them.
- User study: SeeSR obtains a 38.6% confusion rate on synthetic data and a 57.1% best selection rate on real-world data, both leading the user studies.The real-world selection rate is approximately 3.5 times higher than the second-ranked method, while the synthetic confusion rate is three times higher than second place.
- Semantics preservation test: SeeSR delivers a 3 ∼4 times improvement over Zoomed LR across all four detection and segmentation tasks, surpassing existing Real-ISR methods.The tests use 5K COCO-Val images and OpenSeeD as the detector and segmentor.
5. Conclusion and Limitation
SeeSR combines semantic prompting with LR-conditioned diffusion to synthesize semantically correct Real-ISR images. The method remains limited by tag and region errors under severe degradation and by difficulty reconstructing small-scale scene text.
- Conclusion: SeeSR uses semantic prompts to enhance pretrained T2I diffusion models for Real-ISR.Its DAPE extractor produces soft and hard prompts, while LRE embeds LR latent information at diffusion initialization.
- Conclusion: DAPE reduces degradation effects on prompts and outputs soft and hard semantics to guide diffusion-based super-resolution.Image tags improve local perception, while soft prompts provide additional semantic representation.
- Conclusion: LRE embeds LR latent information at the diffusion starting point to avoid artifacts in smooth areas.The strategy addresses training-test inconsistency in diffusion models.
- Limitation: DAPE may predict incorrect tags for heavily degraded images, causing wrongly restored objects.This limitation directly affects semantic correctness when degradation is severe.
- Limitation: Tag-to-region alignment can become inaccurate under severe degradation, although extra mask information may alleviate the issue.SeeSR also faces challenges reconstructing small-scale scene text images, as do other SD-based methods.