Fidelity-Constrained Anchoring for Black-Box Denoisers

• URL: http://arxiv.org/abs/2608.13194v1
• Date: Thu, 13 Aug 2026 12:59:32 GMT
• Title: Fidelity-Constrained Anchoring for Black-Box Denoisers
• Abstract summary: We propose a framework that anchors a black-box denoiser to its input without retraining and with little additional computation.<n>The proposed anchoring strategy provides effective fidelity control while balancing denoising performance and statistical naturalness.<n>In particular, SSIM-based anchoring yields more consistent behavior across noise levels than PSNR-based anchoring.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a fidelity-constrained framework that anchors the output of a black-box denoiser to its input without retraining and with little additional computation. The method linearly blends the denoised image with the input and selects the maximum blending factor that satisfies a prescribed local fidelity constraint using Peak Signal-to-Noise Ratio (PSNR) or Structural Similarity Index (SSIM). For PSNR control, a closed-form solution is obtained under a local constant-blending assumption. For SSIM control, we derive a tractable formulation based on inverse SSIM under the same assumption and solve it efficiently using iterative root finding. Experiments on DIV2K images with synthetic Gaussian noise and outputs from Real-ESRGAN and a non-local means denoiser show that the proposed anchoring strategy provides effective fidelity control while balancing denoising performance and statistical naturalness, as measured by the excess kurtosis of residual noise. In particular, SSIM-based anchoring yields more consistent behavior across noise levels than PSNR-based anchoring.

Related papers

• ENCORE: Efficient Noise Context-Aware Representation for Low-Dose CT Denoising [1.539942973115038]
  deep learning-based denoising has become widely adopted in low-dose CT.<n>Conventional models fail to account for non-stationary and spatially correlated CT noise characteristics.<n>We propose an Efficient Noise COntext-aware REpresentation framework.
  arXiv  Detail & Related papers  (2026-08-11T00:57:39Z)
• Drift-Aware RL-based Wavelet Denoising for Network-Traffic Anomaly Detection [1.5469452301122173]
  Traffic-utilisation measurements for network monitoring are corrupted by additive noise and statistical drift.<n>We propose a drift-aware framework treating adaptive wavelet denoising as a preprocessing layer optimised for two tasks.
  arXiv  Detail & Related papers  (2026-07-22T10:52:05Z)
• SATB-VR: Training Few-Step Video Restoration Diffusion Model using SNR-Aware Trajectory Blending [55.764018145256216]
  We present SATB-VR, a few-step paradigm that jump-starts the denoising process via an auxiliary predictor.<n>We show that SATB-VR performs favorably against existing approaches on synthetic, real-world, and AIGC benchmarks.
  arXiv  Detail & Related papers  (2026-06-27T01:32:16Z)
• Clipping Bottleneck: Stabilizing RLVR via Stochastic Recovery of Near-Boundary Signals [83.0127582612634]
  Near-boundary Rescue (NSR) is a minimal, plug-and-play modification that retains out-of-bound tokens to recover lost signals.<n>NSR substantially improves training stability and delivers consistent gains over strong baselines such as DAPO and GSPO.
  arXiv  Detail & Related papers  (2026-05-21T16:45:31Z)
• Spatial-Spectral Adaptive Fidelity and Noise Prior Reduction Guided Hyperspectral Image Denoising [11.513560890234864]
  We propose a framework that integrates noise prior reduction and spatial-spectral adaptive fidelity term.<n>Within this framework, we develop a fast and robust pixel-wise model combined with the representative coefficient total variation regularizer.<n>Experiments on simulated and real-world datasets demonstrate that the proposed model achieves superior denoising performance.
  arXiv  Detail & Related papers  (2026-04-14T11:27:23Z)
• Noise Augmented Fine Tuning for Mitigating Hallucinations in Large Language Models [1.0579965347526206]
  Large language models (LLMs) often produce inaccurate or misleading content-hallucinations.<n>Noise-Augmented Fine-Tuning (NoiseFiT) is a novel framework that leverages adaptive noise injection to enhance model robustness.<n>NoiseFiT selectively perturbs layers identified as either high-SNR (more robust) or low-SNR (potentially under-regularized) using a dynamically scaled Gaussian noise.
  arXiv  Detail & Related papers  (2025-04-04T09:27:19Z)
• Gradient Normalization Provably Benefits Nonconvex SGD under Heavy-Tailed Noise [60.92029979853314]
  We investigate the roles of gradient normalization and clipping in ensuring the convergence of Gradient Descent (SGD) under heavy-tailed noise. Our work provides the first theoretical evidence demonstrating the benefits of gradient normalization in SGD under heavy-tailed noise. We introduce an accelerated SGD variant incorporating gradient normalization and clipping, further enhancing convergence rates under heavy-tailed noise.
  arXiv  Detail & Related papers  (2024-10-21T22:40:42Z)
• Advancing Unsupervised Low-light Image Enhancement: Noise Estimation, Illumination Interpolation, and Self-Regulation [55.07472635587852]
  Low-Light Image Enhancement (LLIE) techniques have made notable advancements in preserving image details and enhancing contrast. These approaches encounter persistent challenges in efficiently mitigating dynamic noise and accommodating diverse low-light scenarios. We first propose a method for estimating the noise level in low light images in a quick and accurate way. We then devise a Learnable Illumination Interpolator (LII) to satisfy general constraints between illumination and input.
  arXiv  Detail & Related papers  (2023-05-17T13:56:48Z)
• Latent Class-Conditional Noise Model [54.56899309997246]
  We introduce a Latent Class-Conditional Noise model (LCCN) to parameterize the noise transition under a Bayesian framework. We then deduce a dynamic label regression method for LCCN, whose Gibbs sampler allows us efficiently infer the latent true labels. Our approach safeguards the stable update of the noise transition, which avoids previous arbitrarily tuning from a mini-batch of samples.
  arXiv  Detail & Related papers  (2023-02-19T15:24:37Z)
• CFNet: Conditional Filter Learning with Dynamic Noise Estimation for Real Image Denoising [37.29552796977652]
  This paper considers real noise approximated by heteroscedastic Gaussian/Poisson Gaussian distributions with in-camera signal processing pipelines. We propose a novel conditional filter in which the optimal kernels for different feature positions can be adaptively inferred by local features from the image and the noise map. Also, we bring the thought that alternatively performs noise estimation and non-blind denoising into CNN structure, which continuously updates noise prior to guide the iterative feature denoising.
  arXiv  Detail & Related papers  (2022-11-26T14:28:54Z)
• Zero-shot Blind Image Denoising via Implicit Neural Representations [77.79032012459243]
  We propose an alternative denoising strategy that leverages the architectural inductive bias of implicit neural representations (INRs) We show that our method outperforms existing zero-shot denoising methods under an extensive set of low-noise or real-noise scenarios.
  arXiv  Detail & Related papers  (2022-04-05T12:46:36Z)
• Simultaneous Denoising and Dereverberation Using Deep Embedding Features [64.58693911070228]
  We propose a joint training method for simultaneous speech denoising and dereverberation using deep embedding features. At the denoising stage, the DC network is leveraged to extract noise-free deep embedding features. At the dereverberation stage, instead of using the unsupervised K-means clustering algorithm, another neural network is utilized to estimate the anechoic speech.
  arXiv  Detail & Related papers  (2020-04-06T06:34:01Z)

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.