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However, the cost of HS cameras that can perform the desired detection tasks is prohibitive-typically fifty thousand to hundreds of thousands of dollars. In a previous study at the Agricultural Research Organization\u2019s Volcani Institute (Israel), a low-cost, high-performing HS system was developed which included a point spectrometer and optical components. Its main disadvantage was long shooting time for each image. Shooting time strongly depends on the predetermined integration time of the point spectrometer. While essential for performing monitoring tasks in a reasonable time, shortening integration time from a typical value in the range of 200\u00a0ms to the 10\u00a0ms range results in deterioration of the dynamic range of the captured scene. In this work, we suggest correcting this by learning the transformation from data measured with short integration time to that measured with long integration time. Reduction of the dynamic range and consequent low SNR were successfully overcome using three developed deep neural networks models based on a denoising auto-encoder, DnCNN and LambdaNetworks architectures as a backbone. The best model was based on DnCNN using a combined loss function of <jats:inline-formula><jats:alternatives><jats:tex-math>$$\\ell _{2}$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:msub>\n                    <mml:mi>\u2113<\/mml:mi>\n                    <mml:mn>2<\/mml:mn>\n                  <\/mml:msub>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula> and Kullback\u2013Leibler divergence on images with 20 consecutive channels. The full spectrum of the model achieved a mean PSNR of 30.61 and mean SSIM of 0.9, showing total improvement relatively to the 10 ms measurements\u2019 mean PSNR and mean SSIM values by 60.43% and 94.51%, respectively.<\/jats:p>","DOI":"10.1007\/s00138-024-01523-5","type":"journal-article","created":{"date-parts":[[2024,3,22]],"date-time":"2024-03-22T19:02:27Z","timestamp":1711134147000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Hyperspectral image dynamic range reconstruction using deep neural network-based denoising methods"],"prefix":"10.1007","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9025-6848","authenticated-orcid":false,"given":"Loran","family":"Cheplanov","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shai","family":"Avidan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"David J.","family":"Bonfil","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Iftach","family":"Klapp","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,3,22]]},"reference":[{"key":"1523_CR1","doi-asserted-by":"publisher","first-page":"1632","DOI":"10.3390\/su5041632","volume":"5","author":"H Spiertz","year":"2013","unstructured":"Spiertz, H.: Challenges for crop production research in improving land use, productivity and sustainability. 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