测试技术学报2026，Vol.40Issue(3)：335-343,9.DOI:10.62756/csjs.1671-7449.2026025
一种高效的光场焦点堆栈的超分辨率网络
An Efficient Super-Resolution Network for Light Field Focal Stack
摘要
Abstract
Light field(LF)cameras capture scenes from multiple perspectives,providing comprehensive image information.However,the resulting data often suffers from low image quality,and current research generally exhibits limitations in underutilizing multi-focus characteristics.The insufficient exploitation of deep feature capabilities directly constrains the visual enhancement effects of LF imaging.To address this issues,converting sub-aperture images(SAIs)is proposed into a focal stack(FS)using depth information and analyzing them.Leveraging the varying depth-of-field characteristics of FS,depth maps are generated through specifically designed filters to classify images as focused or defocused.A selective super-resolution(SR)network is then applied to enhance focused images,where a dimensionality stretching strategy is employed to integrate low-resolution FS with degradation maps corresponding to deterioration parameters as input to convolutional neural networks.Experiments were conducted on both public and self-built light field datasets.The results show that the proposed method has an average increase of 0.21%in the image information entropy for the foreground and 0.22%for the background compared to the LF-IINet method,and an average increase of 0.21%for the foreground and 0.28%for the background compared to the Distg-SSR method.In terms of the high-frequency energy of the image,the proposed method has an average increase of 10.78%for the foreground and 10.56%for the background compared to the LF-IINet method,and an average increase of 11.33%for the foreground and 11.02%for the background compared to the Distg-SSR method.Through depth-guided focused enhancement and defocused blurring strategies,this approach achieves a balance between detail enhancement and noise suppression.The framework provides an efficient and reliable solution for advancing LF imaging applications in computational photography.关键词
光场/多聚焦特性/焦点堆栈/深度图/超分辨率Key words
light field(LF)/multi-focus characteristics/focal stack/depth maps/super-resolution分类
信息技术与安全科学引用本文复制引用
牛晶慧,张峻彬,袁仲云,程永强,赵纯..一种高效的光场焦点堆栈的超分辨率网络[J].测试技术学报,2026,40(3):335-343,9.基金项目
国家自然科学基金资助项目(52275568) （52275568）
山西省重点研发计划资助项目(202102150401011) （202102150401011）
