A Dictionary Based Approach for Removing Out-of-Focus Blur

• URL: http://arxiv.org/abs/2406.11330v1
• Date: Mon, 17 Jun 2024 08:42:03 GMT
• Title: A Dictionary Based Approach for Removing Out-of-Focus Blur
• Authors: Uditangshu Aurangabadkar, Anil Kokaram,
• Abstract summary: We propose an extension of the Rapid and Accurate Image Super-Resolution algorithm for the task of out-of-focus blur removal. A metric based blending strategy based on asset allocation management is also proposed. Our method demonstrates an average increase of approximately 13% (PSNR) and 10% (SSIM) compared to popular deblurring methods.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The field of image deblurring has seen tremendous progress with the rise of deep learning models. These models, albeit efficient, are computationally expensive and energy consuming. Dictionary based learning approaches have shown promising results in image denoising and Single Image Super-Resolution. We propose an extension of the Rapid and Accurate Image Super-Resolution (RAISR) algorithm introduced by Isidoro, Romano and Milanfar for the task of out-of-focus blur removal. We define a sharpness quality measure which aligns well with the perceptual quality of an image. A metric based blending strategy based on asset allocation management is also proposed. Our method demonstrates an average increase of approximately 13% (PSNR) and 10% (SSIM) compared to popular deblurring methods. Furthermore, our blending scheme curtails ringing artefacts post restoration.

Related papers

• Learning Efficient and Effective Trajectories for Differential Equation-based Image Restoration [59.744840744491945]
  We reformulate the trajectory optimization of this kind of method, focusing on enhancing both reconstruction quality and efficiency. We propose cost-aware trajectory distillation to streamline complex paths into several manageable steps with adaptable sizes. Experiments showcase the significant superiority of the proposed method, achieving a maximum PSNR improvement of 2.1 dB over state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-10-07T07:46:08Z)
• Efficient Diffusion Model for Image Restoration by Residual Shifting [63.02725947015132]
  This study proposes a novel and efficient diffusion model for image restoration. Our method avoids the need for post-acceleration during inference, thereby avoiding the associated performance deterioration. Our method achieves superior or comparable performance to current state-of-the-art methods on three classical IR tasks.
  arXiv  Detail & Related papers  (2024-03-12T05:06:07Z)
• ACDMSR: Accelerated Conditional Diffusion Models for Single Image Super-Resolution [84.73658185158222]
  We propose a diffusion model-based super-resolution method called ACDMSR. Our method adapts the standard diffusion model to perform super-resolution through a deterministic iterative denoising process. Our approach generates more visually realistic counterparts for low-resolution images, emphasizing its effectiveness in practical scenarios.
  arXiv  Detail & Related papers  (2023-07-03T06:49:04Z)
• Generalized Expectation Maximization Framework for Blind Image Super Resolution [28.108363151431877]
  We propose an end-to-end learning framework for the blind SISR problem. The proposed method integrates learning techniques into the generalized expectation-maximization (GEM) algorithm and infers HR images from the maximum likelihood estimation (MLE)
  arXiv  Detail & Related papers  (2023-05-23T10:01:58Z)
• Perception-Distortion Balanced ADMM Optimization for Single-Image Super-Resolution [29.19388490351459]
  We propose a novel super-resolution model with a low-frequency constraint (LFc-SR) We introduce an ADMM-based alternating optimization method for the non-trivial learning of the constrained model. Experiments showed that our method, without cumbersome post-processing procedures, achieved the state-of-the-art performance.
  arXiv  Detail & Related papers  (2022-08-05T05:37:55Z)
• Hierarchical Similarity Learning for Aliasing Suppression Image Super-Resolution [64.15915577164894]
  A hierarchical image super-resolution network (HSRNet) is proposed to suppress the influence of aliasing. HSRNet achieves better quantitative and visual performance than other works, and remits the aliasing more effectively.
  arXiv  Detail & Related papers  (2022-06-07T14:55:32Z)
• Patch-based image Super Resolution using generalized Gaussian mixture model [0.0]
  Single Image Super Resolution (SISR) methods aim to recover the clean images in high resolution from low resolution observations. A family of patch-based approaches have received considerable attention and development. This paper proposes an algorithm to learn a jointgeneralized Gaussian mixture model (GGMM) from a pair of the low resolution patches and the corresponding high resolution patches fromthe reference data.
  arXiv  Detail & Related papers  (2022-06-07T07:40:05Z)
• TextureWGAN: Texture Preserving WGAN with MLE Regularizer for Inverse Problems [4.112614964808275]
  Among all proposed methods, the most popular and effective method is the convolutional neural network (CNN) with mean square error (MSE) We proposed a new method based on Wasserstein GAN (WGAN) for inverse problems. We showed that the WGAN-based method was effective to preserve image texture. It also used a maximum likelihood estimation (MLE) regularizer to preserve pixel fidelity.
  arXiv  Detail & Related papers  (2020-08-11T17:06:34Z)
• Depth image denoising using nuclear norm and learning graph model [107.51199787840066]
  Group-based image restoration methods are more effective in gathering the similarity among patches. For each patch, we find and group the most similar patches within a searching window. The proposed method is superior to other current state-of-the-art denoising methods in both subjective and objective criterion.
  arXiv  Detail & Related papers  (2020-08-09T15:12:16Z)
• The Power of Triply Complementary Priors for Image Compressive Sensing [89.14144796591685]
  We propose a joint low-rank deep (LRD) image model, which contains a pair of complementaryly trip priors. We then propose a novel hybrid plug-and-play framework based on the LRD model for image CS. To make the optimization tractable, a simple yet effective algorithm is proposed to solve the proposed H-based image CS problem.
  arXiv  Detail & Related papers  (2020-05-16T08:17:44Z)

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.