On Measuring and Controlling the Spectral Bias of the Deep Image Prior

• URL: http://arxiv.org/abs/2107.01125v1
• Date: Fri, 2 Jul 2021 15:10:42 GMT
• Title: On Measuring and Controlling the Spectral Bias of the Deep Image Prior
• Authors: Zenglin Shi, Pascal Mettes, Subhransu Maji, and Cees G. M. Snoek
• Abstract summary: The deep image prior has demonstrated the remarkable ability that untrained networks can address inverse imaging problems. It requires an oracle to determine when to stop the optimization as the performance degrades after reaching a peak. We study the deep image prior from a spectral bias perspective to address these problems.
• Score: 63.88575598930554
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The deep image prior has demonstrated the remarkable ability that untrained networks can address inverse imaging problems, such as denoising, inpainting and super-resolution, by optimizing on just a single degraded image. Despite its promise, it suffers from two limitations. First, it remains unclear how one can control the prior beyond the choice of the network architecture. Second, it requires an oracle to determine when to stop the optimization as the performance degrades after reaching a peak. In this paper, we study the deep image prior from a spectral bias perspective to address these problems. By introducing a frequency-band correspondence measure, we observe that deep image priors for inverse imaging exhibit a spectral bias during optimization, where low-frequency image signals are learned faster and better than high-frequency noise signals. This pinpoints why degraded images can be denoised or inpainted when the optimization is stopped at the right time. Based on our observations, we propose to control the spectral bias in the deep image prior to prevent performance degradation and to speed up optimization convergence. We do so in the two core layer types of inverse imaging networks: the convolution layer and the upsampling layer. We present a Lipschitz-controlled approach for the convolution and a Gaussian-controlled approach for the upsampling layer. We further introduce a stopping criterion to avoid superfluous computation. The experiments on denoising, inpainting and super-resolution show that our method no longer suffers from performance degradation during optimization, relieving us from the need for an oracle criterion to stop early. We further outline a stopping criterion to avoid superfluous computation. Finally, we show that our approach obtains favorable restoration results compared to current approaches, across all tasks.

Related papers

• Chasing Better Deep Image Priors between Over- and Under-parameterization [63.8954152220162]
  We study a novel "lottery image prior" (LIP) by exploiting DNN inherent sparsity. LIPworks significantly outperform deep decoders under comparably compact model sizes. We also extend LIP to compressive sensing image reconstruction, where a pre-trained GAN generator is used as the prior.
  arXiv  Detail & Related papers  (2024-10-31T17:49:44Z)
• Alternating Phase Langevin Sampling with Implicit Denoiser Priors for Phase Retrieval [1.7767466724342065]
  We present a way leveraging the prior implicitly learned by a denoiser to solve phase retrieval problems by incorporating it in a classical framework. Compared to performant denoising-based algorithms for phase retrieval, we showcase competitive performance with notable measurements on in-distribution images and notable out-of-distribution images.
  arXiv  Detail & Related papers  (2022-11-02T05:08:50Z)
• Wiener Guided DIP for Unsupervised Blind Image Deconvolution [10.440495513371747]
  Blind deconvolution is an ill-posed problem arising in various fields ranging from microscopy to astronomy. Deep learning architectures can serve as an image generation prior during unsupervised blind deconvolution optimization. We propose to use Wiener-deconvolution to guide the image generator during optimization by providing it a sharpened version of the blurry image.
  arXiv  Detail & Related papers  (2021-12-19T22:19:13Z)
• Rethinking Deep Image Prior for Denoising [23.140599133203292]
  We analyze the DIP by the notion of effective degrees of freedom (DF) to monitor the optimization progress. We propose a principled stopping criterion before fitting to noise without access of a paired ground truth image for Gaussian noise. Our approach outperforms prior arts in LPIPS by large margins with comparable PSNR and SSIM on seven different datasets.
  arXiv  Detail & Related papers  (2021-08-29T13:34:31Z)
• Deep Unfolded Recovery of Sub-Nyquist Sampled Ultrasound Image [94.42139459221784]
  We propose a reconstruction method from sub-Nyquist samples in the time and spatial domain, that is based on unfolding the ISTA algorithm. Our method allows reducing the number of array elements, sampling rate, and computational time while ensuring high quality imaging performance.
  arXiv  Detail & Related papers  (2021-03-01T19:19:38Z)
• Optimal Sequential Detection of Signals with Unknown Appearance and Disappearance Points in Time [64.26593350748401]
  The paper addresses a sequential changepoint detection problem, assuming that the duration of change may be finite and unknown. We focus on a reliable maximin change detection criterion of maximizing the minimal probability of detection in a given time (or space) window. The FMA algorithm is applied to detecting faint streaks of satellites in optical images.
  arXiv  Detail & Related papers  (2021-02-02T04:58:57Z)
• Learned Block Iterative Shrinkage Thresholding Algorithm for Photothermal Super Resolution Imaging [52.42007686600479]
  We propose a learned block-sparse optimization approach using an iterative algorithm unfolded into a deep neural network. We show the benefits of using a learned block iterative shrinkage thresholding algorithm that is able to learn the choice of regularization parameters.
  arXiv  Detail & Related papers  (2020-12-07T09:27:16Z)
• Blind Image Restoration with Flow Based Priors [19.190289348734215]
  In a blind setting with unknown degradations, a good prior remains crucial. We propose using normalizing flows to model the distribution of the target content and to use this as a prior in a maximum a posteriori (MAP) formulation. To the best of our knowledge, this is the first work that explores normalizing flows as prior in image enhancement problems.
  arXiv  Detail & Related papers  (2020-09-09T21:40:11Z)
• 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)
• Class-Specific Blind Deconvolutional Phase Retrieval Under a Generative Prior [8.712404218757733]
  The problem arises in various imaging modalities such as Fourier ptychography, X-ray crystallography, and in visible light communication. We propose to solve this inverse problem using alternating gradient descent algorithm under two pretrained deep generative networks as priors. The proposed recovery algorithm strives to find a sharp image and a blur kernel in the range of the respective pre-generators that textitbest explain the forward measurement model.
  arXiv  Detail & Related papers  (2020-02-28T07:36:28Z)

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.