Resolution-Dependent Artifacts

• Resolution-dependent artifacts are degradations in images or signals whose severity and characteristics change with spatial, temporal, or spectral resolution.
• They originate from sampling limitations, compression, and model mismatches, impacting fields from medical imaging to generative modeling.
• Advanced techniques—such as PSF filtering, wavelet-domain analysis, and adaptive neural networks—are employed to detect and mitigate these artifacts.

Resolution-dependent artifacts are a class of image and signal degradations whose form, severity, or visibility change with the spatial, temporal, or spectral resolution of the acquisition, representation, or reconstruction process. Such artifacts arise from both intrinsic limitations in data sampling and processing and from mismatches between the design assumptions (e.g., fixed processing models or noise schedules) and the varying resolution conditions encountered in practical imaging or generative scenarios. The suppression, calibration, or explicit exploitation of resolution-dependent artifacts is an active area of research in computer vision, medical imaging, microscopy, radio astronomy, and generative modeling, as documented across multiple research efforts.

1. Origins and Types of Resolution-Dependent Artifacts

Resolution-dependent artifacts manifest due to the interaction between underlying sampling theory, information loss during compression, or architectural choices in neural and signal processing models. Key sources and types include:

• Aliasing artifacts: When downsampling or spatial warping changes frequency content and insufficient anti-aliasing is applied, high-frequency content folds into lower frequencies, resulting in grid-like or moiré patterns. This is particularly acute in medical image resampling and computer vision pipelines that do not respect the spatial sampling theorem (Cardoso et al., 2021).
• Blocking, ringing, and blurring: Lossy compression methods such as JPEG and HEVC introduce artifacts that are especially visible at higher display or reconstruction resolutions. Blocking arises due to block-based transform coding, ringing from coarse quantization of high-frequencies, and blurring due to aggressive low-pass operations. The visibility of these effects increases with higher resolution displays (Yu et al., 2016, Prangnell et al., 2016).
• Coherence and deconvolution artifacts: In holography and tomographic imaging, coherent interference or miscalibrated system geometry can lead to spatially varying distortions whose character depends on the system’s resolution and the physical coherence of the illumination (Eliezer et al., 2020, Liu et al., 2023, Liu et al., 2022).
• Neural resampling and generative model instabilities: Upsampling kernels that are too small or not designed with sufficient spatial context propagate or generate aliasing artifacts during pixel-wise prediction (restoration, segmentation, etc.) (Agnihotri et al., 2023). Diffusion models with fixed noise schedules misalign signal degradation at lower resolution, causing pronounced artifacts in low-res generation (He et al., 2 Oct 2025).
• Task-specific artifact propagation: In video super-resolution, hidden state recurrences or lack of explicit state-cleaning result in accumulation and temporal propagation of resolution-dependent artifacts across frames, especially in uncontrolled, real-world degradations (Xie et al., 2022).

2. Quantitative Modeling and Detection Strategies

The quantitative paper and detection of resolution-dependent artifacts leverage various mathematical models, detection pipelines, and evaluation metrics:

• Point Spread Function (PSF) and scale-matched filtering: The “scale factor point spread function” (sfPSF) method computes, for each map between source and target spatial grids, the Gaussian kernel necessary to match the effective resolution, suppressing aliasing (Cardoso et al., 2021). The PSF formalism is central for imaging physics and ensures that resampling preserves the continuous scene content faithfully.
• Wavelet-domain and subband analysis: Differentiating genuine high-frequency details from artifacts can be better achieved in the wavelet domain, where losses and adversarial discriminators operate on multi-scale, orientation-selective representations. Weighted wavelet losses allow targeted suppression of artifact-prone frequency bands (Korkmaz et al., 29 Feb 2024).
• Explicit artifact mapping: Neural methods have developed locally discriminative mechanisms for detecting GAN-generated artifacts, leveraging local residual variance statistics to construct per-pixel or per-region penalty maps that are then used to regularize model training or output selection (Liang et al., 2022, Zheng et al., 25 Mar 2024). Automated patch-based artifact detection is also applied in fields such as digital pathology, using foundation models or domain-specific hand-crafted features for tissue slide assessment (Kahaki et al., 23 Jun 2025).
• Performance metrics: Quantitative measures like PSNR, SSIM, FID, and VMAF capture artifact-induced losses in image similarity and perceptual quality, while clinical and technical impact is measured by accuracy in segmentation, volumetric preservation, or downstream task metrics such as OCR accuracy or face detection mAP (Xiang et al., 2020, Cardoso et al., 2021, Özer et al., 7 Aug 2025).

3. Correction and Suppression Methodologies

Modern research addresses resolution-dependent artifacts through design innovations, adaptive algorithms, and explicit calibration:

• Deep artifact reduction networks (AR-CNN, CAJNN): AR-CNN introduces explicit feature enhancement layers to “clean” feature maps extracted from compressed images, using operations such as PReLU activations and multi-stage convolutions. These are tailored to target blocking, ringing, and blurring, and are enhanced via acceleration (layer decomposition, large-stride convolution-deconvolution) and transfer learning strategies (Yu et al., 2016).
• Adaptation of quantization strategies: In high-resolution video coding, adaptive quantization matrices (AQM) are constructed to weight transform coefficients according to perceptual importance and display resolution, directly modifying the quantization weights to minimize artifacts (Prangnell et al., 2016). The AQM weights are computed as $H'_{i,j} = H_{i,j} / A_{i,j}$ with $A_{i,j} = \exp(w d_{i,j})$, where $w$ embodies the display resolution and $d_{i,j}$ the distance from the DC coefficient.
• Frequency-dependent transmit/receive parameters: In ultrasound imaging, frequency-dependent F-numbers adapt the transmit and receive aperture sizes as a function of spatial frequency, maximizing lateral resolution while suppressing grating lobes. Closed-form expressions balance the trade-off between depth of field, artifact suppression, and spatial resolution, yielding empirical improvements up to 24% in lateral FWHM (Schiffner et al., 2021, Schiffner, 2 Oct 2024).
• Context-aware upsampling and calibration: For neural upsampling, increasing kernel sizes and receptive fields (e.g., via Large Context Transposed Convolutions) reduces the prominence of aliasing artifacts that smaller or naive kernels introduce (Agnihotri et al., 2023). In holography, dynamically tuning the spatial coherence of illumination via a degenerate cavity laser (DCL) enables suppression of artifacts by averaging uncorrelated speckle patterns, at the expense of some edge sharpness (Eliezer et al., 2020).
• Noise scheduler calibration in diffusion models: NoiseShift recalibrates the noise level conditioning for each resolution to match the perceptual effect of noise on images at different scales, correcting for the fact that identical noise magnitudes erase more structure in lower resolution images. This technique yields up to 15.89% FID improvements for Stable Diffusion 3.5 at 128×128 (He et al., 2 Oct 2025).

4. Artifact Impact Across Domains and Applications

Resolution-dependent artifacts are significant across domains:

Application Area	Primary Artifact Type	Impact Mechanism
Medical imaging (MRI, OPT)	Aliasing, geometric, motion	Bias in quantitative metrics, loss of anatomical detail
Computer vision, SR	Blocking, ringing, GAN artifacts	Impaired perceptual quality, task-specific performance loss
Holography, microscopy	Coherent speckle, boundary effects	Image instability, spatial resolution degradation
Video coding / streaming	Compression, blurring	Perceptual artifacts at low bitrates or with VBR fluctuation
Digital pathology	Folds, blur, scan artifacts	Diagnostic error, failure in automated tissue analysis
Radio astronomy	Deconvolution, spectral mismatch	Spurious polarization or flux underestimation

In each field, artifacts may interact with the image or signal’s resolution in complex ways—becoming more apparent at high display resolutions, propagating more strongly in multi-frame video or recurrent analysis, or inducing bias in downstream analytics.

5. Experimental Validation and Critical Findings

Research demonstrates that both theoretical rigor and practical evaluation are essential for mitigating resolution-dependent artifacts:

• Medical imaging resampling performed with sfPSF-matched Gaussian filtering achieves >94% suppression of out-of-band power and statistically significant reductions in clinical volume bias (p < 1e-4) relative to standard approaches (Cardoso et al., 2021).
• Adaptive quantization in HEVC achieves up to 56.5% BD-Rate reduction in the enhancement layer for UHD content, with associated SSIM gains (Prangnell et al., 2016).
• Neural artifact reduction architectures, when accelerated and tuned, maintain visual and quantitative performance (e.g., AR‑CNN achieves <1% loss in PSNR while being 7.5x faster) (Yu et al., 2016).
• Application of NoiseShift to state-of-the-art diffusion models universally improves FID scores for low-resolution generation, with concrete mean improvements ranging 2.4%–15.8% depending on the backbone and dataset, all without retraining or architectural changes (He et al., 2 Oct 2025).
• HistoART demonstrates that models pre-trained on large-scale WSI datasets and fine-tuned with artifact-specific data can achieve patch-level artifact detection AUROC of 0.995, supporting robust digital pathology pipelines with quantifiable reporting (Kahaki et al., 23 Jun 2025).

6. Open Challenges and Future Research Directions

Despite substantial progress, several challenges remain:

• Differentiation of artifacts from genuine details at high frequencies is an unsolved problem. Even wavelet-domain and locally discriminative methods may misclassify structured or rare textures as artifacts (Korkmaz et al., 29 Feb 2024, Liang et al., 2022).
• Generalization to novel or real-world artifact distributions requires scalable training, transfer learning, and flexible, content-adaptive parametrizations.
• Trade-offs between artifact suppression and detail preservation: Over-suppression leads to over-smoothing and information loss, while under-suppression leaves residual artifacts. Fine-tuning hyperparameters, layer design, and domain-specific loss weightings remains an open area (Korkmaz et al., 29 Feb 2024, Zheng et al., 25 Mar 2024).
• Computational and memory complexity: Techniques such as full 3D Faraday synthesis (Gustafsson et al., 31 Mar 2025) and foundation model-based WSI assessment (Kahaki et al., 23 Jun 2025) address ever-larger data, necessitating algorithmic acceleration, parallelization, or INR-based parameter reductions (Özer et al., 7 Aug 2025).
• Integration of perceptual and artifact-aware metrics into model selection, training, and certification pipelines is needed to match human visual criteria and clinical task requirements.

7. Summary

Resolution-dependent artifacts are a pervasive and multifaceted challenge in modern imaging, vision, and generative modeling. They originate from fundamental limits in sampling, compression, physical system design, and neural model mismatch, with their severity and character shaped by the specific resolution context. Addressing these artifacts requires principled mathematical frameworks, calibrated and adaptive model architectures, tailored loss functions, and domain-aware evaluation procedures. Continued research aims to produce methods that can both suppress detrimental artifacts and retain or restore the essential details needed for downstream analysis and human interpretation, while operating efficiently across varying resolutions and data modalities.