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Pixel to Gaussian: Ultra-Fast Continuous Super-Resolution with 2D Gaussian Modeling

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
Continuous Super-Resolution; 2DGS; Fast Model
Abstract:

Arbitrary-scale super-resolution (ASSR) aims to reconstruct high-resolution (HR) images from low-resolution (LR) inputs with arbitrary upsampling factors using a single model, addressing the limitations of traditional SR methods constrained to fixed-scale factors (\textit{e.g.}, ×\times 2). Recent advances leveraging implicit neural representation (INR) have achieved great progress by modeling coordinate-to-pixel mappings. However, the efficiency of these methods may suffer from repeated upsampling and decoding, while their reconstruction fidelity and quality are constrained by the intrinsic representational limitations of coordinate-based functions. To address these challenges, we propose a novel ContinuousSR framework with a Pixel-to-Gaussian paradigm, which explicitly reconstructs 2D continuous HR signals from LR images using Gaussian Splatting. This approach eliminates the need for time-consuming upsampling and decoding, enabling extremely fast ASSR. Once the Gaussian field is built in a single pass, ContinuousSR can perform arbitrary-scale rendering in just 1ms per scale. Our method introduces several key innovations. Through statistical analysis, we uncover the Deep Gaussian Prior (DGP) and propose DGP-Driven Covariance Weighting, which dynamically optimizes covariance via adaptive weighting. Additionally, we present Adaptive Position Drifting, which refines the positional distribution of the Gaussian space based on image content, further enhancing reconstruction quality. Extensive experiments on seven benchmarks demonstrate that our ContinuousSR delivers significant improvements in SR quality across all scales, with an impressive 19.5× speedup when continuously upsampling an image across forty scales.

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This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
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Overview

Overall Novelty Assessment

The paper proposes ContinuousSR, a Gaussian Splatting-based framework for arbitrary-scale super-resolution that reconstructs continuous 2D signals from low-resolution images. It resides in the 'Gaussian Splatting-Based Methods' leaf under 'Implicit Neural Representation-Based Methods', which contains only two papers total: the original work and one sibling (GaussianSR). This is a notably sparse research direction within the broader taxonomy of fifty papers, suggesting that Gaussian-based modeling for arbitrary-scale super-resolution remains an emerging and relatively unexplored approach compared to the more populated coordinate-based implicit function methods.

The taxonomy reveals that the paper's immediate parent branch, 'Implicit Neural Representation-Based Methods', is the most crowded category, containing multiple sibling leaves such as 'Standard INR Architectures' (five papers), 'Position Encoding Enhanced INR' (two papers), and 'Attention-Based INR' (two papers). These neighboring directions focus on coordinate-to-pixel mappings via MLPs and various architectural enhancements. The paper diverges from these by replacing coordinate-based decoding with explicit Gaussian primitives, positioning itself at the intersection of implicit representations and probabilistic modeling. This places the work in a distinct niche that bridges continuous function modeling with explicit spatial distributions.

Among thirty candidates examined, the core 'Pixel-to-Gaussian paradigm' contribution shows one refutable candidate out of ten examined, indicating some overlap with prior Gaussian-based work (likely GaussianSR). The two technical innovations—'Deep Gaussian Prior and DGP-Driven Covariance Weighting' and 'Adaptive Position Drifting'—each examined ten candidates with zero refutations, suggesting these specific mechanisms appear more novel within the limited search scope. The analysis reflects a focused literature search rather than exhaustive coverage, so the findings characterize novelty relative to the top-thirty semantically similar papers and the immediate taxonomy neighborhood.

Given the sparse population of the Gaussian Splatting leaf and the limited search scope, the work appears to introduce substantive technical contributions in an underexplored direction. The single refutation for the core framework likely reflects the close relationship with GaussianSR, while the absence of refutations for the two technical mechanisms suggests they extend beyond existing Gaussian-based approaches. However, the analysis is constrained by the thirty-candidate search and does not cover the full breadth of implicit representation or generative modeling literature.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
30
Contribution Candidate Papers Compared
1
Refutable Paper

Research Landscape Overview

Core task: arbitrary-scale image super-resolution aims to reconstruct high-resolution images at any desired magnification factor from low-resolution inputs, rather than being limited to fixed integer scales. The field has evolved into several major branches that reflect different modeling philosophies. Implicit Neural Representation-Based Methods treat the image as a continuous function, enabling smooth interpolation at arbitrary coordinates; within this branch, recent Gaussian Splatting-Based approaches like GaussianSR[1] and Pixel to Gaussian[0] leverage probabilistic primitives for flexible upsampling. Scale-Adaptive Feature Extraction Methods focus on dynamically adjusting network parameters or attention mechanisms to handle varying scale factors, while Multi-Scale and Hierarchical Processing Methods exploit pyramid-like architectures to capture information across resolutions. Degradation-Aware and Perceptual Quality Methods emphasize realistic texture synthesis and robustness to diverse degradation types, and Specialized Application Domain Methods target niche scenarios such as face or hyperspectral imaging. Alternative Upsampling and Interpolation Approaches explore novel resampling strategies beyond standard bilinear or bicubic kernels, and Benchmarks and Methodological Studies provide evaluation frameworks and comparative analyses. A particularly active line of work centers on implicit neural representations, where methods like Local Implicit Flow[7] and CiaoSR[17] use coordinate-based networks to achieve continuous-scale reconstruction. Within this landscape, Gaussian splatting has emerged as a promising direction: GaussianSR[1] introduced Gaussian primitives for super-resolution, and Pixel to Gaussian[0] extends this idea by converting pixel-level features into Gaussian distributions that can be rendered at arbitrary scales. Compared to GaussianSR[1], which focuses on leveraging Gaussian splatting for efficient upsampling, Pixel to Gaussian[0] emphasizes a tighter integration of pixel-to-Gaussian mapping to enhance detail preservation and scale flexibility. Meanwhile, other implicit methods such as Latent Diffusion Implicit[39] and Nexus-INR[45] explore generative priors and modular architectures, highlighting ongoing debates about the trade-offs between computational efficiency, perceptual quality, and the ability to generalize across diverse scale factors and degradation conditions.

Claimed Contributions

ContinuousSR framework with Pixel-to-Gaussian paradigm

The authors introduce a framework that reconstructs continuous high-resolution signals from low-resolution images via 2D Gaussian modeling. This eliminates repeated upsampling and decoding steps, enabling fast arbitrary-scale super-resolution through a single-pass Gaussian field construction followed by lightweight rendering.

10 retrieved papers
Can Refute
Deep Gaussian Prior (DGP) and DGP-Driven Covariance Weighting

Through statistical analysis of 40,000 natural images, the authors discover that Gaussian field parameters follow a Gaussian distribution with predictable ranges. They leverage this prior to construct pre-defined Gaussian kernels and introduce an adaptive weighting mechanism that simplifies covariance optimization and guides the model toward better solutions.

10 retrieved papers
Adaptive Position Drifting

The authors propose a method that dynamically adjusts the spatial positions of Gaussian kernels by learning content-dependent offsets from low-resolution features. This allows the model to adaptively place kernels more densely in texture-rich regions, enhancing reconstruction quality while maintaining efficient optimization.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

ContinuousSR framework with Pixel-to-Gaussian paradigm

The authors introduce a framework that reconstructs continuous high-resolution signals from low-resolution images via 2D Gaussian modeling. This eliminates repeated upsampling and decoding steps, enabling fast arbitrary-scale super-resolution through a single-pass Gaussian field construction followed by lightweight rendering.

Contribution

Deep Gaussian Prior (DGP) and DGP-Driven Covariance Weighting

Through statistical analysis of 40,000 natural images, the authors discover that Gaussian field parameters follow a Gaussian distribution with predictable ranges. They leverage this prior to construct pre-defined Gaussian kernels and introduce an adaptive weighting mechanism that simplifies covariance optimization and guides the model toward better solutions.

Contribution

Adaptive Position Drifting

The authors propose a method that dynamically adjusts the spatial positions of Gaussian kernels by learning content-dependent offsets from low-resolution features. This allows the model to adaptively place kernels more densely in texture-rich regions, enhancing reconstruction quality while maintaining efficient optimization.

Table of Contents