Exploring Diffusion with Test-Time Training on Efficient Image Restoration

• URL: http://arxiv.org/abs/2506.14541v2
• Date: Sun, 22 Jun 2025 14:57:14 GMT
• Title: Exploring Diffusion with Test-Time Training on Efficient Image Restoration
• Authors: Rongchang Lu, Tianduo Luo, Yunzhi Jiang, Conghan Yue, Pei Yang, Guibao Liu, Changyang Gu,
• Abstract summary: DiffRWKVIR is a novel framework unifying Test-Time Training (TTT) with efficient diffusion.<n>Our method establishes a new paradigm for adaptive, high-efficiency image restoration with optimized hardware utilization.
• Score: 1.3830502387127932
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Image restoration faces challenges including ineffective feature fusion, computational bottlenecks and inefficient diffusion processes. To address these, we propose DiffRWKVIR, a novel framework unifying Test-Time Training (TTT) with efficient diffusion. Our approach introduces three key innovations: (1) Omni-Scale 2D State Evolution extends RWKV's location-dependent parameterization to hierarchical multi-directional 2D scanning, enabling global contextual awareness with linear complexity O(L); (2) Chunk-Optimized Flash Processing accelerates intra-chunk parallelism by 3.2x via contiguous chunk processing (O(LCd) complexity), reducing sequential dependencies and computational overhead; (3) Prior-Guided Efficient Diffusion extracts a compact Image Prior Representation (IPR) in only 5-20 steps, proving 45% faster training/inference than DiffIR while solving computational inefficiency in denoising. Evaluated across super-resolution and inpainting benchmarks (Set5, Set14, BSD100, Urban100, Places365), DiffRWKVIR outperforms SwinIR, HAT, and MambaIR/v2 in PSNR, SSIM, LPIPS, and efficiency metrics. Our method establishes a new paradigm for adaptive, high-efficiency image restoration with optimized hardware utilization.

Related papers

• Duplex-GS: Proxy-Guided Weighted Blending for Real-Time Order-Independent Gaussian Splatting [37.17972426764452]
  We propose a dual-hierarchy framework that integrates proxy Gaussian representations with order-independent rendering techniques.<n>By seamlessly combining our framework with Order-Independent Transparency (OIT), we develop a physically inspired weighted sum rendering technique that simultaneously eliminates "popping" and "transparency" artifacts.<n>Our results validate the advantages of the OIT rendering paradigm in Gaussian Splatting, achieving high-quality rendering with an impressive 1.5 to 4 speedup over existing OIT based Gaussian Splatting approaches.
  arXiv  Detail & Related papers  (2025-08-05T07:44:30Z)
• High-Frequency Prior-Driven Adaptive Masking for Accelerating Image Super-Resolution [87.56382172827526]
  High-frequency regions are most critical for reconstruction.<n>We propose a training-free adaptive masking module for acceleration.<n>Our method reduces FLOPs by 24--43% for state-of-the-art models.
  arXiv  Detail & Related papers  (2025-05-11T13:18:03Z)
• Efficient Diffusion as Low Light Enhancer [63.789138528062225]
  Reflectance-Aware Trajectory Refinement (RATR) is a simple yet effective module to refine the teacher trajectory using the reflectance component of images. textbfReflectance-aware textbfDiffusion with textbfDistilled textbfTrajectory (textbfReDDiT) is an efficient and flexible distillation framework tailored for Low-Light Image Enhancement (LLIE)
  arXiv  Detail & Related papers  (2024-10-16T08:07:18Z)
• LeRF: Learning Resampling Function for Adaptive and Efficient Image Interpolation [64.34935748707673]
  Recent deep neural networks (DNNs) have made impressive progress in performance by introducing learned data priors. We propose a novel method of Learning Resampling (termed LeRF) which takes advantage of both the structural priors learned by DNNs and the locally continuous assumption. LeRF assigns spatially varying resampling functions to input image pixels and learns to predict the shapes of these resampling functions with a neural network.
  arXiv  Detail & Related papers  (2024-07-13T16:09:45Z)
• Gaussian Primitives for Deformable Image Registration [9.184092856125067]
  Experimental results on brain MRI, lung CT, and cardiac MRI datasets demonstrate that GaussianDIR outperforms existing DIR methods in both accuracy and efficiency. As a training-free approach, it challenges the stereotype that iterative methods are inherently slow and transcend the limitations of poor generalization.
  arXiv  Detail & Related papers  (2024-06-05T15:44:54Z)
• Efficient LoFTR: Semi-Dense Local Feature Matching with Sparse-Like Speed [42.861344584752]
  Previous detector-free matcher LoFTR has shown remarkable matching capability in handling large-viewpoint change and texture-poor scenarios. We revisit its design choices and derive multiple improvements for both efficiency and accuracy. Our method can achieve higher accuracy compared with competitive semi-dense matchers.
  arXiv  Detail & Related papers  (2024-03-07T18:58:40Z)
• Transforming Image Super-Resolution: A ConvFormer-based Efficient Approach [58.57026686186709]
  We introduce the Convolutional Transformer layer (ConvFormer) and propose a ConvFormer-based Super-Resolution network (CFSR) CFSR inherits the advantages of both convolution-based and transformer-based approaches. Experiments demonstrate that CFSR strikes an optimal balance between computational cost and performance.
  arXiv  Detail & Related papers  (2024-01-11T03:08:00Z)
• Asymmetric Learned Image Compression with Multi-Scale Residual Block, Importance Map, and Post-Quantization Filtering [15.056672221375104]
  Deep learning-based image compression has achieved better ratedistortion (R-D) performance than the latest traditional method, H.266/VVC. Many leading learned schemes cannot maintain a good trade-off between performance and complexity. We propose an effcient and effective image coding framework, which achieves similar R-D performance with lower complexity than the state of the art.
  arXiv  Detail & Related papers  (2022-06-21T09:34:29Z)
• Displacement-Invariant Cost Computation for Efficient Stereo Matching [122.94051630000934]
  Deep learning methods have dominated stereo matching leaderboards by yielding unprecedented disparity accuracy. But their inference time is typically slow, on the order of seconds for a pair of 540p images. We propose a emphdisplacement-invariant cost module to compute the matching costs without needing a 4D feature volume.
  arXiv  Detail & Related papers  (2020-12-01T23:58:16Z)
• Fully Quantized Image Super-Resolution Networks [81.75002888152159]
  We propose a Fully Quantized image Super-Resolution framework (FQSR) to jointly optimize efficiency and accuracy. We apply our quantization scheme on multiple mainstream super-resolution architectures, including SRResNet, SRGAN and EDSR. Our FQSR using low bits quantization can achieve on par performance compared with the full-precision counterparts on five benchmark datasets.
  arXiv  Detail & Related papers  (2020-11-29T03:53:49Z)

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.