{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T10:08:55Z","timestamp":1779271735515,"version":"3.51.4"},"reference-count":44,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T00:00:00Z","timestamp":1779235200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Changzhou Municipal Science and Technology Bureau, China","award":["CJ20240047"],"award-info":[{"award-number":["CJ20240047"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>Single image super-resolution (SR) is an important part of image processing, which aims to improve the spatial resolution of images. This is a typical ill-posed inverse problem. The main difficulty is that a low-resolution image block usually corresponds to multiple high-resolution image blocks. The existing methods cannot provide enough correlation to determine the unique high-resolution image block, which leads to artifacts and image distortion in the reconstructed image. To address this problem, a method (EHNet) is proposed to achieve super-resolution by using a Hybrid-Channel Fusion Block (HCFB) and an Enhanced Dual-Convolution Block (EDCB). The EDCB effectively enhances the network\u2019s ability to capture image details and textures by combining local and global feature processing. The HCFB strengthens the information interaction between channels by combining channel segmentation with large-kernel convolution, fully explores feature dependencies, and thus optimizes the feature extraction effect. Experimental results show that the super-resolution reconstructed image of EHNet achieves 32.59 dB PSNR and 0.9006 SSIM on the Set5 \u00d74 SR benchmark, outperforming several state-of-the-art SR methods. In addition, the model exhibits notable improvements in artifact suppression, and the reconstructed image\u2019s subjective visual impact surpasses that of other current techniques.<\/jats:p>","DOI":"10.3390\/computers15050323","type":"journal-article","created":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T09:12:41Z","timestamp":1779268361000},"page":"323","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["EHNet: Super-Resolution via Enhanced Dual Convolution and Hybrid-Channel Fusion"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4081-1337","authenticated-orcid":false,"given":"Shen","family":"Shi","sequence":"first","affiliation":[{"name":"School of Computer Science and Artificial Intelligence, Changzhou University, Yanzheng West Avenue, Changzhou 213164, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weiji","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Artificial Intelligence, Changzhou University, Yanzheng West Avenue, Changzhou 213164, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruifeng","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Artificial Intelligence, Changzhou University, Yanzheng West Avenue, Changzhou 213164, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-2146-6044","authenticated-orcid":false,"given":"Yizhuo","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Artificial Intelligence, Changzhou University, Yanzheng West Avenue, Changzhou 213164, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,5,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1016\/j.inffus.2022.10.007","article-title":"Image super-resolution: A comprehensive review, recent trends, challenges and applications","volume":"91","author":"Lepcha","year":"2023","journal-title":"Inf. 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