While content-adaptive schemes have delivered notable advances in image super-resolution (SR), existing approaches typically focus on texture complexity and ignore intrinsic degradation factors (e.g., blur kernels or noise patterns), leading to suboptimal computation allocation and reconstruction performance. To remedy this, we propose DPAMixerSR, a degradation-pattern-aware framework that enables efficient SR through adaptive sparse computation. We design a lightweight Perceptual Degradation Ranking (PDR) module partitions the image into severely and mildly degraded patches, which are routed to the Adaptive Sparse Processing (ASP) and a lightweight convolutional branch, respectively. ASP performs structure-aligned, multi-scale sparse propagation and bidirectional refinement, while the convolutional branch enhances efficiency in mildly degraded regions. By coupling degradation-driven routing with structure-aligned sparse processing, DPAMixerSR establishes a self-regulating framework that dynamically balances computational efficiency and reconstruction fidelity. Extensive experiments on various SR tasks demonstrate that our DPAMixerSR achieves superior structural restoration and perceptual fidelity with markedly reduced computational overhead, providing a novel and scalable framework for degradation-aware, resource-efficient SR.
Figures & tables
Figure 1: Visual and quantitative comparison across regions. Texture-rich areas ( blue box ) with mild degradation can be well restored by lightweight processing, whereas smooth areas ( red box ) with severe structural degradation require heavier computation. Conventional texture-based allocation overlooks degradation semantics, while our degradation-aware mechanism adaptively aligns computation with restoration fidelity.
Figure 2: DPAMixerSR comprises four key components: (1) Multi-scale Feature Extraction (MFE), which integrates hierarchical information for robust feature representation; (2) Perceptual Degradation Ranking (PDR), which dynamically assesses degradation severity to guide reconstruction; (3) Adaptive Sparse Processing (ASP) module, specialized for handling severely degraded patches (SDP); and (4) Convolution Branch, optimized for efficiently restoring mildly degraded patches (MDP).
Figure 3: Our PDR model adopts a two-stage training scheme: a high-capacity PDS model first estimates degradation severity, and its outputs supervise the learning of a lightweight PDR model through ranking-aware objectives. To enhance degradation representation, the Spatial Structure Adjustment (SSA) module removes redundant features via SSAB blocks. In the ASP branch, SSAB blocks are combined with feed-forward network (FFN) layers and normalization (Norm) to enable adaptive restoration of complex regions. The aggregation (AGG) module performs bidirectional feature aggregation over structure-guided graphs, integrating local and contextual cues to improve reconstruction quality.
ξi=Ui(xi+∑j∈N(i)αijξj),αij=softmax(wij)
Algorithm 2 Spatial Structure Adjustment
Method
Scale
#Params
#MAdds
Set5
Set14
BSD100
Urban100
Manga109
PSNR
SSIM
PSNR
SSIM
PSNR
SSIM
PSNR
SSIM
PSNR
SSIM
SESR [ 4 ]
×4
729K
36.9G
31.54
0.8866
28.12
0.7712
27.31
0.7277
25.31
0.7604
29.04
0.8901
IMDN [ 13 ]
×4
715K
40.9G
32.21
0.8948
28.58
0.7811
27.56
0.7353
26.04
0.7838
30.45
0.9075
LatticeNet [ 25 ]
×4
777K
43.6G
32.18
0.8943
28.61
0.7812
27.57
0.7355
26.14
0.7844
-
-
FDIWN [ 11 ]
×4
664K
28.4G
32.23
0.8955
28.66
0.7829
27.62
0.7380
26.28
0.7919
-
-
SwinIR-light [ 20 ]
×4
930K
61.7G
32.44
0.8976
28.47
0.7823
27.57
0.7402
26.47
0.7980
30.92
0.9051
Table 1: We benchmark state-of-the-art super-resolution methods using average PSNR/SSIM, parameter count, and Multiply-Adds (MAdds), computed under a 1280×720 upscaling setting. Bold and underlined values denote the best and second-best results, respectively. “ Rel. Imp ” denotes the relative improvements compared with the previous SOTA method.
Datasets
Metrics
Methods
MAT [ 43 ]
FoundIR [ 19 ]
ResShift [ 47 ]
StableSR [ 34 ]
SeeSR [ 41 ]
DiffBIR [ 22 ]
OSEDiff [ 40 ]
SUPIR [ 46 ]
TVT [ 45 ]
DiT4SR [ 10 ]
DPAMixerSR
DrealSR
LPIPS ↓
0.282
0.274
0.353
0.273
0.317
0.452
0.297
0.419
0.354
0.365
0.259
MUSIQ ↑
65.665
52.737
52.392
58.512
65.077
57.341
64.692
59.744
44.047
64.950
66.841
MANIQA ↑
0.490
0.475
0.476
0.559
0.605
0.455
0.590
0.552
0.631
0.627
0.645
ClipIQA ↑
0.401
0.396
0.379
0.438
0.543
0.479
0.519
0.512
0.576
0.548
0.587
LIQE ↑
2.927
2.745
2.798
3.243
3.401
3.894
3.942
3.728
4.129
3.964
4.254
Table 2: Quantitative comparison with state-of-the-art Real-ISR methods on four real-world benchmarks. Best and second best performance are highlighted in red and blue , respectively.
Figure 4: Ablation study on ASP ratio γ .
Figure 5: Qualitative comparison on the real-world dataset.
Methods
FLOPs
LPIPS ↓
MUSIQ ↑
MANIQA ↑
ClipIQA ↑
LIQE ↑
TVT [ 45 ]
1.97 T
0.325
59.396
0.546
0.474
3.221
DPAMixerSR (w/o PDR)
101 G
0.276
65.573
0.624
0.569
4.047
DPAMixerSR (w/o PDR, w/ PDS)
285 G
0.234
71.462
0.683
0.618
4.330
DPAMixerSR (w/o SSA)
68.9 G
0.295
67.463
0.653
0.586
4.171
DPAMixerSR (w/o SSA, w/ TSP)
214 G
0.283
67.821
0.659
0.590
4.178
DPAMixerSR (w/o SSA, w/ EIT-GNN)
259 G
0.287
67.514
0.643
0.573
4.153
Table 3: Ablation studies on our method on RealSR dataset.
Real-world image super-resolution is particularly challenging for diffusion models because real degradations are complex, heterogeneous, and rarely modeled explicitly. We propose a degradation-aware and structure-preserving diffusion framework for real-world SR. Specifically, we introduce Degradation-aware Token Injection, which encodes lightweight degradation statistics from low-resolution inputs and fuses them with semantic conditioning features, enabling explicit degradation-aware restoration. We further propose Spatially Asymmetric Noise Injection, which modulates diffusion noise with local edge strength to better preserve structural regions during training. Both modules are lightweight add-ons to the adopted diffusion SR framework, requiring only minor modifications to the conditioning pipeline. Experiments on DIV2K and RealSR show that our method delivers competitive no-reference perceptual quality and visually more realistic restoration results than recent baselines, while maintaining a favorable perception--distortion trade-off. Ablations confirm the effectiveness of each module and their complementary gains when combined. The code and model are publicly available at https://github.com/jiyang0315/DASP-SR.git.
Yang Ji, Zonghao Chen, Zhihao Xue +1
KUNBYTE Hangzhou, Zhejiang Province, China · GOLDMYE Xuhui District, Shanghai, China
Diffusion prior-based methods have shown impressive results in real-world image super-resolution (ISR), yet two key challenges persist: balancing pixel-level fidelity with semantic quality, and adapting to diverse degradations. Existing dual-branch approaches freeze the pixel module during semantic training, but the semantic branch can still expand capacity within the pixel subspace, precluding genuine perceptual improvement. Moreover, using a single static adapter cannot generalize across heterogeneous real-world corruptions. To address both issues, we propose FreqOrtho-SR, which comprises: Frequency-guided Mixture of LoRA Experts (FreqMoE), it routes inputs to specialized experts via a non-parametric FFT-based degradation-feature extractor that encodes frequency-domain signatures, enabling stable and interpretable specialization across corruption types; and Orthogonal Gradient Projection (OGP), which reframes the dual-objective optimization as a subspace-constrained problem: by extracting the pixel-fidelity subspace via SVD on combined expert weight deltas and projecting semantic gradients onto its null space, OGP guarantees orthogonality between the two objectives, enabling genuinely complementary learning without mutual interference. Experiments show that FreqOrtho-SR achieves competitive overall performance and a strong fidelity-perception trade-off across multiple benchmarks with efficient single-step inference. The source code of our method can be found at sonhm3029/FreqOrtho-SR.
Minh Son Hoang, Dinh Phu Tran, Quyen Nguyen Duc +2
State space models (SSMs) have emerged as a powerful paradigm for efficient single-image super-resolution (SR) due to their linear complexity and long-range modeling capabilities. However, existing Mamba-based methods typically rely on data-agnostic rigid scanning, which reshapes 2D images into 1D sequences over a fixed grid, inevitably disrupting spatial-semantic topology and introducing artifacts. Inspired by the Gestalt perceptual grouping theory, we propose SP-MoMamba, a superpixel-driven mixture of state space experts designed for content-aware SR. Our core idea is to transform the traditional rigid scanning into a semantic-level interaction by treating superpixels as fundamental units. Specifically, we introduce the Superpixel-driven State Space Model (SP-SSM), which compresses semantically homogeneous regions into high-order tokens to preserve global topological consistency. To address the conflict between fixed scanning scales and diverse semantic granularities, we develop the Multi-Scale Superpixel Mixture of State Space Experts (MSS-MoE). This module utilizes a dynamic routing mechanism to adaptively assign scale-specific experts, effectively capturing multi-scale textures while reducing computational redundancy. Furthermore, to prevent the loss of high-frequency details during global abstraction, we introduce a Local Spatial Modulation Expert (LSME) to complement the global modeling, ensuring a precise reconstruction of sharp edges and fine structures. Extensive experiments on standard benchmarks demonstrate that SP-MoMamba achieves superior reconstruction fidelity and a more favorable efficiency-performance trade-off compared to state-of-the-art efficient SR methods.
Wenbin Zou, Yawen Cui, Yi Wang +5
Department of Electrical and Electronic Engineering, The Hong Kong Polytechnic University, Hong Kong SAR · School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China