This paper reviews the NTIRE 2026 challenge on efficient single-image super-resolution with a focus on the proposed solutions and results. The aim of this challenge is to devise a network that reduces one or several aspects, such as runtime, parameters, and FLOPs, while maintaining PSNR of around 26.90 dB on the DIV2K_LSDIR_valid dataset, and 26.99 dB on the DIV2K_LSDIR_test dataset. The challenge had 95 registered participants, and 15 teams made valid submissions. They gauge the state-of-the-art results for efficient single-image super-resolution.
Figures & tables
Teams
PSNR [dB]
Runtime [ms]
#Params
FLOPs
Sub-Track Scores
Main-Track
Val.
Test
Val.
Test
Ave.
[M]
[G]
Runtime
#Params
FLOPs
Overall Score
Ranking
XiaomiMM
26.92
27.00
5.700
4.810
5.256
0.139
9.11
3.95 (1)
6.30 (5)
6.38 (6)
4.43
1
BOE_AIoT
26.90
26.99
6.942
6.500
6.724
0.142
9.25
5.80 (3)
6.56 (6)
6.57 (7)
5.95
2
PKDSR
26.91
27.00
6.946
6.568
6.758
0.144
9.40
5.85 (4)
6.73 (7)
6.77 (8)
6.03
3
DISP
26.90
27.02
6.650
6.238
6.444
0.164
10.69
5.39 (2)
8.78 (9)
8.80 (9)
6.07
4
VARH-AI
26.92
26.99
8.472
7.314
7.892
0.126
8.22
7.87 (5)
5.31 (4)
5.33 (4)
7.36
5
Table 1: Results of Ninth NTIRE 2026 Efficient SR Challenge. The performance of the solutions is compared thoroughly from three perspectives including the runtime, FLOPs, and the number of parameters. The underscript numbers associated with each metric score denote the ranking of the solution in terms of that metric. For runtime, “Val.” is the runtime averaged on DIV2K_LSDIR_valid validation set. “Test” is the runtime averaged on a test set with 200 images from DIV2K_LSDIR_test set, respectively. “Ave.” is averaged on the validation and test datasets. “#Params” is the total number of parameters of a model. “FLOPs” denotes the floating point operations. Main Track combines all three evaluation metrics. The ranking for the main track is based on the score calculated via Eq. 2 , and the ranking for other sub-tracks is based on the score of each metric via Eq. 1 . Please note that this is not a challenge for PSNR improvement. The “validation/testing PSNR” is not ranked. For all the scores, the lower, the better.
Figure 1: Team XiaomiMM : SPANV2 overall architecture. The near-pixel branch (top) provides a pixel-repeat upsampling prior, while five SPABV2 blocks (bottom) extract deep features. The two paths are concatenated (80 ch) and fused by depthwise-separable convolution before PixelShuffle ×4 .
Figure 2: Team XiaomiMM : span_attn_op fuses the 1×1 attention convolution, element-wise addition, and element-wise multiplication into a single CUDA kernel, eliminating 3× redundant DRAM round-trips.
Figure 3: Team ZenoSR : The overall architecture of Adaptive Calibration Network(ACN)
Figure 4: Team CUIT_HTT : Overall architecture of the proposed MambaGate-SR.
Figure 5: Team HAESR : The architecture of HAESR
Figure 6: Team IN2GM : Overview of the proposed RFDN_SPAN architecture.
Figure 7: Team Sunflower : The network architecture of HFENet
Figure 8: Team XuptSR : The whole framework of Variance-Guided Spatial-Channel Context Interaction Network (VSCINet)
Figure 9: Team XuptSR : The details of each component. SCAB: Spatial Channel Attention Block; GSA: Gated Self-Attention; GDFN: Gated Depthwise Feed-Forward Network; GCAB:Global Channel Attention Block; VCSA: Variance-guided Contextual Spatial Attention; MSConv: Multi-scale Convolution; MSRB: Multi-scale Residual Block
Figure 10: Team WMESR : Overview of DWMamba.
Figure 11: Team VARH-AI : The proposed model architecture overview.
Stage
Patch Size
Batch
Iterations
FFT Weight
1
128×128
64
200K
0
2
256×256
64
200K
0→0.5
3
384×384
64
200K
0.5→0
4
512×512
64
200K
0
Table 2: DSCLoRA multi-stage curriculum schedule.
Figure 12: Team XSR : Network architecture of the proposed AMCANet.
Figure 13: Team Just Try : Overall structure of ERRN2
Figure 14: Team MDAP : SAFMN-Deep15 overall architecture. The deep feature extraction path consists of 15 sequential SAFM blocks (utilizing multi-scale spatial pooling and GELU gating). The output is refined and added via a global residual before being finally upsampled by PixelShuffle( ×4 ).
This paper presents the NTIRE 2026 image super-resolution (×4) challenge, one of the associated competitions of the NTIRE 2026 Workshop at CVPR 2026. The challenge aims to reconstruct high-resolution (HR) images from low-resolution (LR) inputs generated through bicubic downsampling with a ×4 scaling factor. The objective is to develop effective super-resolution solutions and analyze recent advances in the field. To reflect the evolving objectives of image super-resolution, the challenge includes two tracks: (1) a restoration track, which emphasizes pixel-wise fidelity and ranks submissions based on PSNR; and (2) a perceptual track, which focuses on visual realism and evaluates results using a perceptual score. A total of 194 participants registered for the challenge, with 31 teams submitting valid entries. This report summarizes the challenge design, datasets, evaluation protocol, main results, and methods of participating teams. The challenge provides a unified benchmark and offers insights into current progress and future directions in image super-resolution.
This paper provides a review of the NTIRE 2026 challenge on mobile real-world image super-resolution, highlighting the proposed solutions and the resulting outcomes. The challenge aims to recover high-resolution (HR) images from low-resolution (LR) counterparts generated through unknown degradations with a x4 scaling factor while ensuring the models remain executable on mobile devices. The objective is to develop effective and efficient network designs or solutions that achieve state-of-the-art real-world image super-resolution performance. The track of the challenge evaluates performance using a weighted combination of image quality assessment (IQA) score and speedup ratios. The competition attracted 108 registrants, with 16 teams achieving a valid score in the final ranking. This collaborative effort advances the performance of mobile real-world image super-resolution while offering an in-depth overview of the latest trends in the field.
This paper presents the NTIRE 2026 Remote Sensing Infrared Image Super-Resolution (x4) Challenge, one of the associated challenges of NTIRE 2026. The challenge aims to recover high-resolution (HR) infrared images from low-resolution (LR) inputs generated through bicubic downsampling with a x4 scaling factor. The objective is to develop effective models or solutions that achieve state-of-the-art performance for infrared image SR in remote sensing scenarios. To reflect the characteristics of infrared data and practical application needs, the challenge adopts a single-track setting. A total of 115 participants registered for the competition, with 13 teams submitting valid entries. This report summarizes the challenge design, dataset, evaluation protocol, main results, and the representative methods of each team. The challenge serves as a benchmark to advance research in infrared image super-resolution and promote the development of effective solutions for real-world remote sensing applications.