Beyond the Ground Truth: Enhanced Supervision for Image Restoration

• URL: http://arxiv.org/abs/2512.03932v2
• Date: Thu, 04 Dec 2025 05:45:19 GMT
• Title: Beyond the Ground Truth: Enhanced Supervision for Image Restoration
• Abstract summary: We propose a novel framework that enhances existing ground truth images to provide higher-quality supervision for real-world restoration.<n>Our framework generates perceptually enhanced ground truth images using super-resolution by incorporating adaptive frequency masks.<n>The enhanced ground truth images are used to train a lightweight output refinement network that can be seamlessly integrated with existing restoration models.
• Score: 34.09421585138677
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep learning-based image restoration has achieved significant success. However, when addressing real-world degradations, model performance is limited by the quality of ground-truth images in datasets due to practical constraints in data acquisition. To address this limitation, we propose a novel framework that enhances existing ground truth images to provide higher-quality supervision for real-world restoration. Our framework generates perceptually enhanced ground truth images using super-resolution by incorporating adaptive frequency masks, which are learned by a conditional frequency mask generator. These masks guide the optimal fusion of frequency components from the original ground truth and its super-resolved variants, yielding enhanced ground truth images. This frequency-domain mixup preserves the semantic consistency of the original content while selectively enriching perceptual details, preventing hallucinated artifacts that could compromise fidelity. The enhanced ground truth images are used to train a lightweight output refinement network that can be seamlessly integrated with existing restoration models. Extensive experiments demonstrate that our approach consistently improves the quality of restored images. We further validate the effectiveness of both supervision enhancement and output refinement through user studies. Code is available at https://github.com/dhryougit/Beyond-the-Ground-Truth.

Related papers

• OmniRefiner: Reinforcement-Guided Local Diffusion Refinement [10.329465965964571]
  VAE-based latent compression discards subtle texture information, causing identity- and attribute-specific cues to vanish.<n>We introduce ourMthd, a detail-aware refinement framework that performs two consecutive stages of reference-driven correction.<n>Experiments demonstrate that ourMthd significantly improves reference alignment and fine-grained detail preservation.
  arXiv  Detail & Related papers  (2025-11-25T06:57:49Z)
• A Generative Data Framework with Authentic Supervision for Underwater Image Restoration and Enhancement [51.382274157144714]
  We develop a generative data framework based on unpaired image-to-image translation.<n>The framework constructs synthetic datasets with precise ground-truth labels.<n>Experiments show that models trained on our synthetic data achieve comparable or superior color restoration and generalization performance to those trained on existing benchmarks.
  arXiv  Detail & Related papers  (2025-11-18T14:20:17Z)
• LAFR: Efficient Diffusion-based Blind Face Restoration via Latent Codebook Alignment Adapter [52.93785843453579]
  Blind face restoration from low-quality (LQ) images is a challenging task that requires high-fidelity image reconstruction and the preservation of facial identity.<n>We propose LAFR, a novel codebook-based latent space adapter that aligns the latent distribution of LQ images with that of HQ counterparts.<n>We show that lightweight finetuning of diffusion prior on just 0.9% of FFHQ dataset is sufficient to achieve results comparable to state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-05-29T14:11:16Z)
• Leveraging Diffusion Knowledge for Generative Image Compression with Fractal Frequency-Aware Band Learning [16.077768397480902]
  generative image compression approaches produce detailed, realistic images instead of the only sharp-looking reconstructions.<n>We propose a novel deep learning-based generative image compression method injected with diffusion knowledge.<n>Our method achieves better distortions at high realism and better realism at low distortion than ever before.
  arXiv  Detail & Related papers  (2025-03-14T11:41:33Z)
• Enhancing Image Matting in Real-World Scenes with Mask-Guided Iterative Refinement [4.006320049969407]
  Mask2Alpha is an iterative refinement framework designed to enhance semantic comprehension, instance awareness, and fine-detail recovery in image matting.<n>Our framework leverages self-supervised Vision Transformer features as semantic priors, strengthening contextual understanding in complex scenarios.<n>Mask2Alpha consistently achieves state-of-the-art results, showcasing its effectiveness in accurate and efficient image matting.
  arXiv  Detail & Related papers  (2025-02-24T12:16:28Z)
• ReFIR: Grounding Large Restoration Models with Retrieval Augmentation [52.00990126884406]
  We propose a solution called the Retrieval-augmented Framework for Image Restoration (ReFIR) Our ReFIR incorporates retrieved images as external knowledge to extend the knowledge boundary of existing LRMs. Our experiments demonstrate that ReFIR can achieve not only high-fidelity but also realistic restoration results.
  arXiv  Detail & Related papers  (2024-10-08T01:27:45Z)
• Diff-Restorer: Unleashing Visual Prompts for Diffusion-based Universal Image Restoration [19.87693298262894]
  We propose Diff-Restorer, a universal image restoration method based on the diffusion model. We utilize the pre-trained visual language model to extract visual prompts from degraded images. We also design a Degradation-aware Decoder to perform structural correction and convert the latent code to the pixel domain.
  arXiv  Detail & Related papers  (2024-07-04T05:01:10Z)
• Self-Adaptive Reality-Guided Diffusion for Artifact-Free Super-Resolution [47.29558685384506]
  Artifact-free super-resolution (SR) aims to translate low-resolution images into their high-resolution counterparts with a strict integrity of the original content. Traditional diffusion-based SR techniques are prone to artifact introduction during iterative procedures. We propose Self-Adaptive Reality-Guided Diffusion to identify and mitigate the propagation of artifacts.
  arXiv  Detail & Related papers  (2024-03-25T11:29:19Z)
• ENTED: Enhanced Neural Texture Extraction and Distribution for Reference-based Blind Face Restoration [51.205673783866146]
  We present ENTED, a new framework for blind face restoration that aims to restore high-quality and realistic portrait images. We utilize a texture extraction and distribution framework to transfer high-quality texture features between the degraded input and reference image. The StyleGAN-like architecture in our framework requires high-quality latent codes to generate realistic images.
  arXiv  Detail & Related papers  (2024-01-13T04:54:59Z)
• Enhancing Low-Light Images in Real World via Cross-Image Disentanglement [58.754943762945864]
  We propose a new low-light image enhancement dataset consisting of misaligned training images with real-world corruptions. Our model achieves state-of-the-art performances on both the newly proposed dataset and other popular low-light datasets.
  arXiv  Detail & Related papers  (2022-01-10T03:12:52Z)
• Frequency Consistent Adaptation for Real World Super Resolution [64.91914552787668]
  We propose a novel Frequency Consistent Adaptation (FCA) that ensures the frequency domain consistency when applying Super-Resolution (SR) methods to the real scene. We estimate degradation kernels from unsupervised images and generate the corresponding Low-Resolution (LR) images. Based on the domain-consistent LR-HR pairs, we train easy-implemented Convolutional Neural Network (CNN) SR models.
  arXiv  Detail & Related papers  (2020-12-18T08:25:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.