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They are achieving outstanding performance. However, there are still problems in the state-of-the-art works, especially from two perspectives: 1. failed at exploiting the hierarchical characteristics from the input, resulting in loss of information and artifacts in the final high resolution (HR) image; 2. failed to handle arbitrary-sized images; the existing research works are focused on fixed size input images. To address these challenges, this paper proposed a residual dense network (RDN) and multi-scale sub-pixel convolution network (MSSPCN) which are integrated into a Collapsible Linear Block Super Efficient Super-Resolution (SESR) network. The RDNs aims to tackle the first challenge, carrying the hierarchical features from end-to-end. An adaptive cropping strategy (ACS) technique is introduced before feature extraction targeting at the image size challenge. The novelty of this work is extracting the hierarchical features and integrating RDNs with MSSPCNs. The proposed network can upscale any arbitrary-sized image (1080p) to \u00d72 (4K) and \u00d74 (8K). To secure ground truth for evaluation, this paper follows the opposite flow, generating the input LR images by down-sampling the given HR images (ground truth). To evaluate the performance, the proposed algorithm is compared with eight state-of-the-art algorithms, both quantitatively and qualitatively. The results are verified on six benchmark datasets. The extensive experiments justify that the proposed architecture performs better than other methods and upscales the images satisfactorily.<\/jats:p>","DOI":"10.1007\/s11042-024-19292-8","type":"journal-article","created":{"date-parts":[[2024,5,21]],"date-time":"2024-05-21T07:03:35Z","timestamp":1716275015000},"page":"11409-11426","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Enhanced collapsible linear blocks for arbitrary sized image super-resolution"],"prefix":"10.1007","volume":"84","author":[{"given":"Prathap","family":"Soma","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaosong","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jian","family":"Chang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jian Jun","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,5,21]]},"reference":[{"doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. 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