AdaRevD: Adaptive Patch Exiting Reversible Decoder Pushes the Limit of Image Deblurring

• URL: http://arxiv.org/abs/2406.09135v1
• Date: Thu, 13 Jun 2024 14:06:12 GMT
• Title: AdaRevD: Adaptive Patch Exiting Reversible Decoder Pushes the Limit of Image Deblurring
• Abstract summary: This paper proposes a pioneering work, Adaptive Patch Exiting Reversible Decoder (AdaRevD) Our reversible structure gradually disentangles high-level degradation degree and low-level blur pattern. Experiments show that our AdaRevD pushes the limit of image deblurring, achieving 34.60 dB in PSNR on GoPro dataset.
• Score: 14.101870441572618
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Despite the recent progress in enhancing the efficacy of image deblurring, the limited decoding capability constrains the upper limit of State-Of-The-Art (SOTA) methods. This paper proposes a pioneering work, Adaptive Patch Exiting Reversible Decoder (AdaRevD), to explore their insufficient decoding capability. By inheriting the weights of the well-trained encoder, we refactor a reversible decoder which scales up the single-decoder training to multi-decoder training while remaining GPU memory-friendly. Meanwhile, we show that our reversible structure gradually disentangles high-level degradation degree and low-level blur pattern (residual of the blur image and its sharp counterpart) from compact degradation representation. Besides, due to the spatially-variant motion blur kernels, different blur patches have various deblurring difficulties. We further introduce a classifier to learn the degradation degree of image patches, enabling them to exit at different sub-decoders for speedup. Experiments show that our AdaRevD pushes the limit of image deblurring, e.g., achieving 34.60 dB in PSNR on GoPro dataset.

Related papers

• ZeroGVC: Zero-Shot Generative Video Compression with Autoregressive Diffusion Priors [52.50325804778549]
  We propose ZeroGVC, a zero-shot generative video compression framework.<n>ZeroGVC encodes the first frame of each group of pictures (GOP) with an image and represents subsequent P-frames through Codebook-Guided Autoregressive Latent Compression.<n>Experiments on standard video compression benchmarks demonstrate that ZeroGVC achieves superior perceptual reconstruction quality at ultra-lows without any additional training.
  arXiv  Detail & Related papers  (2026-06-21T07:46:14Z)
• Ultra-Low Bitrate Perceptual Image Compression with Shallow Encoder [41.0853762937392]
  We propose a novel Asymmetric Extreme Image Compression (AEIC) framework that pursues simultaneously encoding simplicity and decoding quality.<n>AEIC employs moderate or even shallow encoder networks, while leveraging an one-step diffusion decoder to maintain high-fidelity and high-realism.<n>Experiments demonstrate that AEIC not only outperforms existing methods on rate-distortion-perception performance at ultra-lows, but also delivers exceptional encoding efficiency for 35.8 FPS on 1080P input images.
  arXiv  Detail & Related papers  (2025-12-13T07:59:48Z)
• Learning to Restore Multi-Degraded Images via Ingredient Decoupling and Task-Aware Path Adaptation [51.10017611491389]
  Real-world images often suffer from multiple coexisting degradations, such as rain, noise, and haze coexisting in a single image.<n>We propose an adaptive multi-degradation image restoration network that reconstructs images by leveraging decoupled representations of degradation ingredients.<n>The resulting tightly integrated architecture, termed IMDNet, is extensively validated through experiments.
  arXiv  Detail & Related papers  (2025-11-07T01:50:36Z)
• $ε$-VAE: Denoising as Visual Decoding [61.29255979767292]
  In generative modeling, tokenization simplifies complex data into compact, structured representations, creating a more efficient, learnable space. Current visual tokenization methods rely on a traditional autoencoder framework, where the encoder compresses data into latent representations, and the decoder reconstructs the original input. We propose denoising as decoding, shifting from single-step reconstruction to iterative refinement. Specifically, we replace the decoder with a diffusion process that iteratively refines noise to recover the original image, guided by the latents provided by the encoder. We evaluate our approach by assessing both reconstruction (rFID) and generation quality (
  arXiv  Detail & Related papers  (2024-10-05T08:27:53Z)
• HybridFlow: Infusing Continuity into Masked Codebook for Extreme Low-Bitrate Image Compression [51.04820313355164]
  HyrbidFlow combines the continuous-feature-based and codebook-based streams to achieve both high perceptual quality and high fidelity under extreme lows. Experimental results demonstrate superior performance across several datasets under extremely lows.
  arXiv  Detail & Related papers  (2024-04-20T13:19:08Z)
• Enhancing the Rate-Distortion-Perception Flexibility of Learned Image Codecs with Conditional Diffusion Decoders [7.485128109817576]
  We show that conditional diffusion models can lead to promising results in the generative compression task when used as a decoder. In this paper, we show that conditional diffusion models can lead to promising results in the generative compression task when used as a decoder.
  arXiv  Detail & Related papers  (2024-03-05T11:48:35Z)
• Neural Image Compression Using Masked Sparse Visual Representation [17.229601298529825]
  We study neural image compression based on the Sparse Visual Representation (SVR), where images are embedded into a discrete latent space spanned by learned visual codebooks. By sharing codebooks with the decoder, the encoder transfers codeword indices that are efficient and cross-platform robust. We propose a Masked Adaptive Codebook learning (M-AdaCode) method that applies masks to the latent feature subspace to balance and reconstruction quality.
  arXiv  Detail & Related papers  (2023-09-20T21:59:23Z)
• Flexible Neural Image Compression via Code Editing [8.499248314440557]
  Neural image compression (NIC) has outperformed traditional image codecs in ratedistortion (R-D) performance. It usually requires a dedicated encoder-decoder pair for each point on R-D curve, which greatly hinders its practical deployment. We propose Code Editing, a highly flexible coding method for NIC based on semi-amortized inference and adaptive quantization.
  arXiv  Detail & Related papers  (2022-09-19T09:41:43Z)
• Cycle Encoding of a StyleGAN Encoder for Improved Reconstruction and Editability [76.6724135757723]
  GAN inversion aims to invert an input image into the latent space of a pre-trained GAN. Despite the recent advances in GAN inversion, there remain challenges to mitigate the tradeoff between distortion and editability. We propose a two-step approach that first inverts the input image into a latent code, called pivot code, and then alters the generator so that the input image can be accurately mapped into the pivot code.
  arXiv  Detail & Related papers  (2022-07-19T16:10:16Z)
• 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)
• Reducing Redundancy in the Bottleneck Representation of the Autoencoders [98.78384185493624]
  Autoencoders are a type of unsupervised neural networks, which can be used to solve various tasks. We propose a scheme to explicitly penalize feature redundancies in the bottleneck representation. We tested our approach across different tasks: dimensionality reduction using three different dataset, image compression using the MNIST dataset, and image denoising using fashion MNIST.
  arXiv  Detail & Related papers  (2022-02-09T18:48:02Z)
• Restoration of Video Frames from a Single Blurred Image with Motion Understanding [69.90724075337194]
  We propose a novel framework to generate clean video frames from a single motion-red image. We formulate video restoration from a single blurred image as an inverse problem by setting clean image sequence and their respective motion as latent factors. Our framework is based on anblur-decoder structure with spatial transformer network modules.
  arXiv  Detail & Related papers  (2021-04-19T08:32:57Z)
• Modeling Lost Information in Lossy Image Compression [72.69327382643549]
  Lossy image compression is one of the most commonly used operators for digital images. We propose a novel invertible framework called Invertible Lossy Compression (ILC) to largely mitigate the information loss problem.
  arXiv  Detail & Related papers  (2020-06-22T04:04:56Z)

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.