{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:39:06Z","timestamp":1760146746205,"version":"build-2065373602"},"reference-count":39,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2024,12,3]],"date-time":"2024-12-03T00:00:00Z","timestamp":1733184000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Basic Public Welfare Research Program of Zhejiang Province","award":["LGF22F020017"],"award-info":[{"award-number":["LGF22F020017"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Deep learning-based image compressive sensing (CS) methods often suffer from high computational complexity and significant loss of image details in reconstructions. A non-local prior dense feature distillation network (NPDFD-Net) is proposed for image CS. First, the non-local priors of images are leveraged to enhance high-frequency information in the measurements. Second, a discrete wavelet decomposition learning module and an inverse discrete wavelet reconstruction module are designed to reduce information loss and significantly lower computational complexity. Third, a feature distillation mechanism is incorporated into residual dense blocks to improve feature transmission efficiency. Finally, a multi-scale enhanced spatial attention module is proposed to strengthen feature diversity. Experimental results indicate that compared to MRCS_GAN, OCTUF, and DPC-DUN, the proposed method achieves an average PSNR improvement of 1.52%, 2.35%, and 0.93%, respectively, on the Set5 dataset. The image reconstruction running time is enhanced by 93.93%, 71.76%, and 40.74%, respectively. Furthermore, the proposed method exhibits significant advantages in restoring fine texture details in the reconstructed images.<\/jats:p>","DOI":"10.3390\/info15120773","type":"journal-article","created":{"date-parts":[[2024,12,4]],"date-time":"2024-12-04T06:38:25Z","timestamp":1733294305000},"page":"773","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Non-Local Prior Dense Feature Distillation Network for Image Compressive Sensing"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6716-8949","authenticated-orcid":false,"given":"Mingkun","family":"Feng","sequence":"first","affiliation":[{"name":"School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China"}]},{"given":"Xiaole","family":"Han","sequence":"additional","affiliation":[{"name":"School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China"}]},{"given":"Kai","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,12,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1289","DOI":"10.1109\/TIT.2006.871582","article-title":"Compressive sensing","volume":"52","author":"Donoho","year":"2006","journal-title":"IEEE Trans. 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