基于自适应大核注意力的轻量级图像超分辨率网络

吉林大学学报(工学版) ›› 2025, Vol. 55 ›› Issue (3): 1015-1027.

• 计算机科学与技术 • 上一篇

基于自适应大核注意力的轻量级图像超分辨率网络

程德强¹(),刘规¹,寇旗旗³,张剑英¹,江鹤^1,²()

^1.中国矿业大学信息与控制工程学院，江苏徐州 221116
^2.成都大学模式识别与智能信息处理四川省高校重点实验室，成都 610106
^3.中国矿业大学计算机科学与技术学院，江苏徐州 221116

收稿日期:2023-10-26 出版日期:2025-03-01 发布日期:2025-05-20
通讯作者: 江鹤 E-mail:cdqcumt@126.com;jianghe@cumt.edu.cn
作者简介:程德强（1979-），男，教授，博士.研究方向：图像智能检测与模式识别，图像处理与视频编码.E-mail：cdqcumt@126.com
基金资助:
国家自然科学基金项目(52304182);四川省数据恢复重点实验室开放基金项目(DRN2408)

Lightweight image super⁃resolution network based on adaptive large kernel attention fusion

De-qiang CHENG¹(),Gui LIU¹,Qi-qi KOU³,Jian-ying ZHANG¹,He JIANG^1,²()

^1.School of Information and Control Engineering，China University of Mining and Technology，Xuzhou 221116，China
^2.Key Laboratory of Pattern Recognition and Intelligent Information Processing，Institutions of Higher Education of Sichuan Province，Chengdu 610106，China
^3.School of Computer Science and Technology，China University of Mining and Technology，Xuzhou 221116，China

Received:2023-10-26 Online:2025-03-01 Published:2025-05-20
Contact: He JIANG E-mail:cdqcumt@126.com;jianghe@cumt.edu.cn

摘要/Abstract

摘要：

针对高性能图像超分辨网络通常参数量庞大的问题，提出了一种轻量级模型。首先，集成了3种大核注意力，即双路大核注意力、大核像素注意力和大核通道注意力，旨在扩大模型感受野，建立像素的长程依赖性。其次，引入了自适应注意力融合机制，增强了特征的表征能力，提升了模型性能。实验证明：本文模型在视觉感知和量化测试上表现优异。在Urban100数据集上，与目前流行的ARRFN算法相比，4倍重建结果的峰值信噪比均值提高了0.25 dB。重建图像视觉效果更逼真、纹理更为清晰和自然，充分证明了该算法的有效性。

关键词: 计算机视觉, 超分辨, 大核注意力, 轻量化, 自适应特征融合

Abstract:

To address the issue of large parameter quantities in high-performance image super-resolution networks， a lightweight model was proposed. First， three kinds of large kernel attention were integrated， namely dual-path large kernel attention， large kernel pixel attention and large kernel channel attention， aiming to expand the model perceptual field and establish the long-range dependence of pixels. Second， an adaptive attention fusion mechanism was introduced to enhance the characterization of features and improve the model performance. Experiments demonstrate that the model performs well on visual perception and quantitative tests. On the Urban100 dataset， the mean value of peak signal-to-noise ratio of ×4 reconstruction results was improved by 0.25 dB compared with the currently popular ARRFN algorithm. The reconstructed images have more realistic visual effects， clearer and more natural texture information， which fully demonstrates the proposed algorithm effectiveness.

Key words: computer version, super-resolution, large kernel attention, lightweight, adaptive feature fusion

中图分类号:

TP391.41

程德强,刘规,寇旗旗,张剑英,江鹤. 基于自适应大核注意力的轻量级图像超分辨率网络[J]. 吉林大学学报(工学版), 2025, 55(3): 1015-1027.

De-qiang CHENG,Gui LIU,Qi-qi KOU,Jian-ying ZHANG,He JIANG. Lightweight image super⁃resolution network based on adaptive large kernel attention fusion[J]. Journal of Jilin University(Engineering and Technology Edition), 2025, 55(3): 1015-1027.

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表6

缩放因子分别为2、3、4时在基准数据集上的指标对比"

缩放因子	模型	参数量/M	浮点运算量/G	Set5^［24］ PSNR/SSIM	Set14^［25］ PSNR/SSIM	B100^［26］ PSNR/SSIM	Urban100^［27］ PSNR/SSIM
x₂	SRCNN^［10］	0.05	52.7	36.66/0.9542	32.42/0.9063	31.36/0.8879	29.50/0.8946
	FSRCNN^［28］	0.01	6.0	37.00/0.9558	32.63/0.9088	31.53/0.8920	29.88/0.9020
	VDSR^［29］	0.66	612.6	37.53/0.9587	33.03/0.9124	31.90/0.8960	30.76/0.9140
	DRCN^［30］	1.77	17974.0	37.63/0.9588	33.04/0.9118	31.85/0.8942	30.75/0.9133
	CARN^［31］	1.59	222.8	37.76/0.9590	33.52/0.9166	32.09/0.8978	31.92/0.9256
	EDSR-baseline^［12］	1.37	316.2	37.99/0.9604	33.57/0.9175	32.16/0.8994	31.98/0.9272
	IMDN^［32］	0.69	158.8	38.00/0.9605	33.63/0.9177	32.19/0.8996	32.17/0.9283
	LAMRN^［35］	1.39	320.1	38.09/0.9608	33.78^/0.9195^	32.22/0.9003	32.31/0.9299
	FMEN^［34］	0.75	172.0	38.10^/0.9609^	33.75^/0.9192^	32.26^/0.9007^	32.41/0.9311
	A²F-L^［33］	1.36	306.1	38.09/0.9607	33.78^*/0.9192^	32.23/0.9002	32.46^/0.9313^
	ARRFN^［36］	0.98	-	38.01/0.9606	33.66/0.9179	32.20/0.8999	32.27/0.9295
	ALSR（本文）	1.51	189.3	38.14^/0.9616^	33.74/0.9187	32.24^/0.9004^	32.48^/0.9317^
x₃	SRCNN^［10］	0.05	52.7	32.75/0.9090	29.28/0.8209	28.41/0.7863	26.24/0.7989
	FSRCNN^［28］	0.01	5.0	33.16/0.9140	29.43/0.8242	28.53/0.7910	26.43/0.8080
	VDSR^［29］	0.66	612.6	33.66/0.9213	29.77/0.8314	28.82/0.7976	27.14/0.8279
	DRCN^［30］	1.77	17974.0	33.82/0.9226	29.76/0.8311	28.80/0.7963	27.15/0.8276
	CARN^［31］	1.59	118.8	34.29/0.9255	30.29/0.8407	29.06/0.8034	28.06/0.8493
	EDSR-baseline^［12］	1.55	160.4	34.37/0.9270	30.28/0.8417	29.09/0.8052	28.15/0.8527
	IMDN^［32］	0.70	71.5	34.36/0.9270	30.32/0.8417	29.09/0.8046	28.17/0.8519
	LAMRN^［35］	1.41	145.3	34.55^/0.9285^	30.41^*/0.8431	29.17^/0.8067^	28.43^*/0.8571
	FMEN^［34］	0.76	77.2	34.45/0.9275	30.40/0.8435	29.17^*/0.8063	28.33/0.8562
	A²F-L^［33］	1.37	136.3	34.54/0.9283	30.41^/0.8436^	29.14/0.8062	28.40/0.8574^*
	ARRFN^［36］	0.99	-	34.38/0.9272	30.36/0.8422	29.09/0.8050	28.22/0.8533
	ALSR（本文）	1.69	122.5	34.58^/0.9293^	30.47^/0.8451^	29.18^/0.8069^	28.44^/0.8576^
x₄	SRCNN^［10］	0.05	52.7	30.48/0.8628	27.49/0.7503	26.90/0.7101	24.52/0.7221
	FSRCNN^［28］	0.01	4.6	30.71/0.8657	27.59/0.7535	26.98/0.7150	24.62/0.7280
	VDSR^［29］	0.66	612.6	31.35/0.8838	28.01/0.7674	27.29/0.7251	25.18/0.7524
	DRCN^［30］	1.77	17974.0	31.53/0.8854	28.02/0.7670	27.23/0.7233	25.14 /0.7510
	CARN^［31］	1.59	90.9	32.13/0.8937	28.60/0.7806	27.58/0.7349	26.07/0.7837
	EDSR-baseline^［12］	1.51	114.2	32.09/0.8938	28.58/0.7813	27.57/0.7357	26.04/0.7849
	IMDN^［32］	0.70	40.9	32.21/0.8948	28.58/0.7811	27.56/0.7353	26.04/0.7838
	LAMRN^［35］	1.41	85.0	32.37^/0.8971^	28.70^/0.7839^	27.61/0.7384^*	26.32^*/0.7925
	FMEN^［34］	0.77	44.2	32.24/0.8955	28.70^/0.7839^	27.63/0.7379	26.28/0.7908
	A²F-L^［33］	1.37	77.2	32.32/0.8964	28.67/0.7839	27.62^*/0.7379	26.32^/0.7931^
	ARRFN^［36］	1.00	-	32.22/0.8952	28.60/0.7817	27.57/0.7355	26.09/0.7858
	ALSR（本文）	1.66	69.9	32.40^/0.8976^	28.71^/0.7843^	27.65^/0.7386^	26.34^/0.7937^

表6

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LKA 3-5-1	LKA 5-7-1	LKA 7-9-1	参数量/K	浮点运算量/G	Set5 PSNR/SSIM	Set14 PSNR/SSIM	B100 PSNR/SSIM	Urban100 PSNR/SSIM
√	×	×	767.96	113.69	37.93/0.9587	33.43/0.9152	32.08/0.8975	31.74/0.9237
×	√	×	778.20	115.20	37.94/0.9592	33.46/0.9159	32.10/0.8978	31.74/0.9238
×	×	√	791.01	117.31	37.93/0.9584	33.44/0.9154	32.09/0.8977	31.73/0.9234
√	√	×	837.08	123.73	37.97/0.9598	33.50/0.9168	32.12/0.8983	31.80/0.9243
×	√	√	864.25	127.35	37.98/0.9601	33.51/0.9171	32.11/0.8981	31.78/0.9238
√	×	√	853.47	125.84	37.96/0.9594	33.50/0.9166	32.10/0.8976	31.77/0.9235
√	√	√	904.38	133.47	37.98/0.9604	33.52/0.9170	32.11/0.8984	31.80/0.9246

1×1Conv	7×7DPConv	1×1Conv	参数量/M	浮点运算量/G	Set5 PSNR/SSIM	Set14 PSNR/SSIM	B100 PSNR/SSIM	Urban100 PSNR/SSIM
× × × √	×	×	0.58	66.56	37.76/0.9590	33.22/0.9135	31.92/0.8960	31.12/0.9171
	×	√	0.60	68.98	37.78/0.9595	33.23/0.9136	31.93/0.8961	31.13/0.9173
	√	×	0.59	68.41	37.79/0.9596	33.24/0.9139	31.93/0.8962	31.13/0.9172
	×	√	0.61	71.37	37.77/0.9592	33.23/0.9136	31.93/0.8962	31.12/0.9172
√	√	√	0.63	73.22	37.81/0.9593	33.26/0.9142	31.94/0.8963	31.14/0.9175

DLKA	LKPA	AWLM	参数量/M	浮点运算量/G	Set5 PSNR/SSIM	Set14 PSNR/SSIM	B100 PSNR/SSIM	Urban100 PSNR/SSIM
× √ × √	×	×	0.74	109.99	37.81/0.9573	33.34/0.9142	32.03/0.8969	31.62/0.9213
	×	×	0.84	123.73	37.97/0.9598	33.50/0.9168	32.12/0.8983	31.80/0.9243
	√	×	0.77	113.37	37.93/0.9589	33.47/0.9161	32.11/0.8980	31.73/0.9234
	√	×	0.86	127.11	38.02/0.9608	33.50/0.9169	32.12/0.8983	31.98/0.9270
√	√	√	0.88	127.18	38.04/0.9610	33.52/0.9171	32.14/0.8987	32.01/0.9275

ALKA	LKCA	参数量/M	浮点运算量/G	Set5 PSNR/SSIM	Set14 PSNR/SSIM	B100 PSNR/SSIM	Urban100 PSNR/SSIM
× √ × √	×	0.74	109.99	37.81/0.9573	33.34/0.9142	32.03/0.8969	31.62/0.9213
	×	0.88	127.18	38.04/0.9609	33.52/0.9171	32.14/0.8987	32.01/0.9275
	√	0.92	116.73	37.96/0.9594	33.43/0.9152	32.06/0.8972	31.65/0.9217
	√	1.06	133.90	38.05/0.9610	33.56/0.9175	32.16/0.8991	32.04/0.9279

尺度因子	LBNet^［37］	ESRT^［18］	NGswin^［38］	ALSR（本文）
2	-	38.03/0.9600	38.05/0.9610	38.14/0.9616
3	34.47/0.9277	34.42/0.9268	34.52/0.9282	34.58/0.9293
4	32.29/0.8960	32.19/0.8947	32.33/0.8963	32.40/0.8976

基于自适应大核注意力的轻量级图像超分辨率网络

Lightweight image super⁃resolution network based on adaptive large kernel attention fusion

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参考文献 39

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LAFB个数	输出通道数	参数量/M	浮点运算量/G	Set5 PSNR/SSIM	Set14 PSNR/SSIM	B100 PSNR/SSIM	Urban100 PSNR/SSIM
4	64	1.06	133.90	38.09/0.9609	33.64/0.9164	32.19/0.8995	32.17/0.9286
4	96	2.33	295.07	38.15/0.9619	33.72/0.9184	32.26/0.9009	32.45/0.9311
6	64	1.51	189.03	38.14/0.9616	33.74/0.9187	32.24/0.9004	32.48/0.9317
6	96	3.33	417.78	38.18/0.9621	33.83/0.9197	32.29/0.9011	32.60/0.9327

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