End-to-end Alternating Optimization for Blind Super Resolution

• URL: http://arxiv.org/abs/2105.06878v1
• Date: Fri, 14 May 2021 15:05:30 GMT
• Title: End-to-end Alternating Optimization for Blind Super Resolution
• Authors: Zhengxiong Luo, Yan Huang, Shang Li, Liang Wang and Tieniu Tan
• Abstract summary: Two-step solution involves two independently trained models, which may not be well compatible with each other. We adopt an alternating optimization algorithm, which can estimate the blur kernel and restore the SR image in a single model. Our model can largely outperform state-of-the-art methods and produce more visually favorable results at a much higher speed.
• Score: 68.395664154041
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Previous methods decompose the blind super-resolution (SR) problem into two sequential steps: \textit{i}) estimating the blur kernel from given low-resolution (LR) image and \textit{ii}) restoring the SR image based on the estimated kernel. This two-step solution involves two independently trained models, which may not be well compatible with each other. A small estimation error of the first step could cause a severe performance drop of the second one. While on the other hand, the first step can only utilize limited information from the LR image, which makes it difficult to predict a highly accurate blur kernel. Towards these issues, instead of considering these two steps separately, we adopt an alternating optimization algorithm, which can estimate the blur kernel and restore the SR image in a single model. Specifically, we design two convolutional neural modules, namely \textit{Restorer} and \textit{Estimator}. \textit{Restorer} restores the SR image based on the predicted kernel, and \textit{Estimator} estimates the blur kernel with the help of the restored SR image. We alternate these two modules repeatedly and unfold this process to form an end-to-end trainable network. In this way, \textit{Estimator} utilizes information from both LR and SR images, which makes the estimation of the blur kernel easier. More importantly, \textit{Restorer} is trained with the kernel estimated by \textit{Estimator}, instead of the ground-truth kernel, thus \textit{Restorer} could be more tolerant to the estimation error of \textit{Estimator}. Extensive experiments on synthetic datasets and real-world images show that our model can largely outperform state-of-the-art methods and produce more visually favorable results at a much higher speed. The source code is available at \url{https://github.com/greatlog/DAN.git}.

Related papers

• End-to-end Alternating Optimization for Real-World Blind Super Resolution [43.95832398891317]
  Blind Super-Resolution (SR) usually involves two sub-problems. estimating the degradation of the given low-resolution (LR) image and super-resolving the LR image to its high-resolution (HR) counterpart. We propose an alternating optimization algorithm, which can estimate the degradation and restore the SR image in a single model.
  arXiv  Detail & Related papers  (2023-08-17T06:55:09Z)
• Dual-Diffusion: Dual Conditional Denoising Diffusion Probabilistic Models for Blind Super-Resolution Reconstruction in RSIs [6.2678394285548755]
  We propose a novel blind SR framework based on conditional denoising diffusion probabilistic models (DDPM) In our work, we introduce conditional denoising diffusion probabilistic models (DDPM) from two aspects: kernel estimation progress and re-construction progress. We construct a DDPM-based reconstructor to learning the mapping from the LR images to HR images.
  arXiv  Detail & Related papers  (2023-05-20T11:18:38Z)
• Kernelized Back-Projection Networks for Blind Super Resolution [14.422263486129811]
  Super Resolution (SR) fails to super-resolve Low-Resolution (LR) images degraded by arbitrary degradations. This paper reveals that non-blind SR that is trained simply with various blur kernels exhibits comparable performance as those with the degradation model for blind SR.
  arXiv  Detail & Related papers  (2023-02-16T18:35:39Z)
• Towards Lightweight Super-Resolution with Dual Regression Learning [58.98801753555746]
  Deep neural networks have exhibited remarkable performance in image super-resolution (SR) tasks. The SR problem is typically an ill-posed problem and existing methods would come with several limitations. We propose a dual regression learning scheme to reduce the space of possible SR mappings.
  arXiv  Detail & Related papers  (2022-07-16T12:46:10Z)
• Unfolded Deep Kernel Estimation for Blind Image Super-resolution [23.798845090992728]
  Blind image super-resolution (BISR) aims to reconstruct a high-resolution image from its low-resolution counterpart degraded by unknown blur kernel and noise. We propose a novel unfolded deep kernel estimation (UDKE) method, which, for the first time to our best knowledge, explicitly solves the data term with high efficiency.
  arXiv  Detail & Related papers  (2022-03-10T07:54:59Z)
• Mutual Affine Network for Spatially Variant Kernel Estimation in Blind Image Super-Resolution [130.32026819172256]
  Existing blind image super-resolution (SR) methods mostly assume blur kernels are spatially invariant across the whole image. This paper proposes a mutual affine network (MANet) for spatially variant kernel estimation.
  arXiv  Detail & Related papers  (2021-08-11T16:11:17Z)
• Unfolding the Alternating Optimization for Blind Super Resolution [60.39409203244474]
  Two-step solution involves two independently trained models, which may not be well compatible with each other. We adopt an alternating optimization algorithm, which can estimate blur kernel and restore SR image in a single model. Our model can largely outperform state-of-the-art methods and produce more visually favorable results at much higher speed.
  arXiv  Detail & Related papers  (2020-10-06T11:27:27Z)
• Closed-loop Matters: Dual Regression Networks for Single Image Super-Resolution [73.86924594746884]
  Deep neural networks have exhibited promising performance in image super-resolution. These networks learn a nonlinear mapping function from low-resolution (LR) images to high-resolution (HR) images. We propose a dual regression scheme by introducing an additional constraint on LR data to reduce the space of the possible functions.
  arXiv  Detail & Related papers  (2020-03-16T04:23:42Z)

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.