Distillation-Supervised Convolutional Low-Rank Adaptation for Efficient Image Super-Resolution

• URL: http://arxiv.org/abs/2504.11271v1
• Date: Tue, 15 Apr 2025 15:12:57 GMT
• Title: Distillation-Supervised Convolutional Low-Rank Adaptation for Efficient Image Super-Resolution
• Authors: Xinning Chai, Yao Zhang, Yuxuan Zhang, Zhengxue Cheng, Yingsheng Qin, Yucai Yang, Li Song,
• Abstract summary: Convolutional neural networks (CNNs) have been widely used in efficient image super-resolution.<n>We propose Distillation-Supervised Convolutional Low-Rank Adaptation (DSCLoRA), which improves model performance without increasing architectural complexity or inference costs.
• Score: 19.22142805041799
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Convolutional neural networks (CNNs) have been widely used in efficient image super-resolution. However, for CNN-based methods, performance gains often require deeper networks and larger feature maps, which increase complexity and inference costs. Inspired by LoRA's success in fine-tuning large language models, we explore its application to lightweight models and propose Distillation-Supervised Convolutional Low-Rank Adaptation (DSCLoRA), which improves model performance without increasing architectural complexity or inference costs. Specifically, we integrate ConvLoRA into the efficient SR network SPAN by replacing the SPAB module with the proposed SConvLB module and incorporating ConvLoRA layers into both the pixel shuffle block and its preceding convolutional layer. DSCLoRA leverages low-rank decomposition for parameter updates and employs a spatial feature affinity-based knowledge distillation strategy to transfer second-order statistical information from teacher models (pre-trained SPAN) to student models (ours). This method preserves the core knowledge of lightweight models and facilitates optimal solution discovery under certain conditions. Experiments on benchmark datasets show that DSCLoRA improves PSNR and SSIM over SPAN while maintaining its efficiency and competitive image quality. Notably, DSCLoRA ranked first in the Overall Performance Track of the NTIRE 2025 Efficient Super-Resolution Challenge. Our code and models are made publicly available at https://github.com/Yaozzz666/DSCF-SR.

Related papers

• C2D-ISR: Optimizing Attention-based Image Super-resolution from Continuous to Discrete Scales [6.700548615812325]
  We propose a novel framework, textbfC2D-ISR, for optimizing attention-based image super-resolution models.<n>Our approach is based on a two-stage training methodology and a hierarchical encoding mechanism.<n>In addition, we generalize the hierarchical encoding mechanism with existing attention-based network structures.
  arXiv  Detail & Related papers  (2025-03-17T21:52:18Z)
• VELoRA: A Low-Rank Adaptation Approach for Efficient RGB-Event based Recognition [54.27379947727035]
  This paper proposes a novel PEFT strategy to adapt the pre-trained foundation vision models for the RGB-Event-based classification. The frame difference of the dual modalities is also considered to capture the motion cues via the frame difference backbone network. The source code and pre-trained models will be released on urlhttps://github.com/Event-AHU/VELoRA.
  arXiv  Detail & Related papers  (2024-12-28T07:38:23Z)
• Binarized Diffusion Model for Image Super-Resolution [61.963833405167875]
  Binarization, an ultra-compression algorithm, offers the potential for effectively accelerating advanced diffusion models (DMs) Existing binarization methods result in significant performance degradation. We introduce a novel binarized diffusion model, BI-DiffSR, for image SR.
  arXiv  Detail & Related papers  (2024-06-09T10:30:25Z)
• A-SDM: Accelerating Stable Diffusion through Redundancy Removal and Performance Optimization [54.113083217869516]
  In this work, we first explore the computational redundancy part of the network. We then prune the redundancy blocks of the model and maintain the network performance. Thirdly, we propose a global-regional interactive (GRI) attention to speed up the computationally intensive attention part.
  arXiv  Detail & Related papers  (2023-12-24T15:37:47Z)
• Iterative Soft Shrinkage Learning for Efficient Image Super-Resolution [91.3781512926942]
  Image super-resolution (SR) has witnessed extensive neural network designs from CNN to transformer architectures. This work investigates the potential of network pruning for super-resolution iteration to take advantage of off-the-shelf network designs and reduce the underlying computational overhead. We propose a novel Iterative Soft Shrinkage-Percentage (ISS-P) method by optimizing the sparse structure of a randomly network at each and tweaking unimportant weights with a small amount proportional to the magnitude scale on-the-fly.
  arXiv  Detail & Related papers  (2023-03-16T21:06:13Z)
• Residual Local Feature Network for Efficient Super-Resolution [20.62809970985125]
  In this work, we propose a novel Residual Local Feature Network (RLFN) The main idea is using three convolutional layers for residual local feature learning to simplify feature aggregation. In addition, we won the first place in the runtime track of the NTIRE 2022 efficient super-resolution challenge.
  arXiv  Detail & Related papers  (2022-05-16T08:46:34Z)
• MPRNet: Multi-Path Residual Network for Lightweight Image Super Resolution [2.3576437999036473]
  A novel lightweight super resolution network is proposed, which improves the SOTA performance in lightweight SR. The proposed architecture also contains a new attention mechanism, Two-Fold Attention Module, to maximize the representation ability of the model.
  arXiv  Detail & Related papers  (2020-11-09T17:11:15Z)
• Accurate and Lightweight Image Super-Resolution with Model-Guided Deep Unfolding Network [63.69237156340457]
  We present and advocate an explainable approach toward SISR named model-guided deep unfolding network (MoG-DUN) MoG-DUN is accurate (producing fewer aliasing artifacts), computationally efficient (with reduced model parameters), and versatile (capable of handling multiple degradations) The superiority of the proposed MoG-DUN method to existing state-of-theart image methods including RCAN, SRDNF, and SRFBN is substantiated by extensive experiments on several popular datasets and various degradation scenarios.
  arXiv  Detail & Related papers  (2020-09-14T08:23:37Z)
• Iterative Network for Image Super-Resolution [69.07361550998318]
  Single image super-resolution (SISR) has been greatly revitalized by the recent development of convolutional neural networks (CNN) This paper provides a new insight on conventional SISR algorithm, and proposes a substantially different approach relying on the iterative optimization. A novel iterative super-resolution network (ISRN) is proposed on top of the iterative optimization.
  arXiv  Detail & Related papers  (2020-05-20T11:11:47Z)

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.