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The aim is to further balance the high and low-frequency information of an image, enrich the detailed texture to improve perceptual quality, and improve the adaptive optimization and generalization of the model in the process of super-resolution reconstruction. First, an effective multi-layer fusion super-resolution (MFSR) basic model is constructed by the design of edge enhancement, refine layering, enhanced super-resolution generative adversarial network and other sub-models, and effective multi-layer fusion. This further enriches the image representation of features of different scales and depths and improves the feature representation of high and low-frequency information in a balanced way. Next, a total loss function of the generator is constructed with adaptive parameter tuning performance. The overall adaptability of the model is improved through adaptive weight distribution and fusion of content loss, perceptual loss, and adversarial loss, and improving the error while reducing the edge enhancement model. Finally, a fitness function with the evaluation perceptual function as the optimization strategy is constructed, and the model compression and adaptive tuning of MFSR are carried out based on the multi-mechanism fusion strategy. Consequently, the construction of the adaptive MFSR model is realized. Adaptive MFSR can maintain high peak signal to noise ratio and structural similarity on the test sets Set5, Set14, and BSD100, and achieve high-quality reconstructed images with low learned perceptual image patch similarity and perceptual index, while having good generalization capabilities.<\/jats:p>","DOI":"10.1007\/s11063-024-11660-7","type":"journal-article","created":{"date-parts":[[2024,6,19]],"date-time":"2024-06-19T16:03:50Z","timestamp":1718813030000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Single Image High-Perception Super-Resolution Reconstruction Method Based on Multi-layer Feature Fusion Model with Adaptive Compression and Parameter Tuning"],"prefix":"10.1007","volume":"56","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7767-7413","authenticated-orcid":false,"given":"Rui","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenyu","family":"Ren","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lihu","family":"Pan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaolu","family":"Bai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ji","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,6,19]]},"reference":[{"issue":"5","key":"11660_CR1","doi-asserted-by":"publisher","first-page":"2530","DOI":"10.1109\/TIP.2018.2887017","volume":"28","author":"Z Wang","year":"2019","unstructured":"Wang Z, Yi P, Jiang K et al (2019) Multi-memory convolutional neural network for video super-resolution. 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