Hyperspectral image dynamic range reconstruction using deep neural network-based denoising methods

• Published in: Machine vision and applications Vol. 35; no. 3; p. 39
• Main Authors: Cheplanov, Loran, Avidan, Shai, Bonfil, David J., Klapp, Iftach
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.05.2024; Springer Nature B.V
• Subjects: Artificial neural networks; Communications Engineering; Computer Science; Dynamic range; Hyperspectral imaging; Image Processing and Computer Vision; Image reconstruction; Machine learning; Networks; Neural networks; Noise reduction; Optical components; Original Paper; Pattern Recognition; Time measurement; Deep neural network; Kullback–Leibler divergence loss; Dynamic-range reconstruction; Hyperspectral; Shortening shooting time; Image denoising
• ISSN: 0932-8092; 1432-1769
• DOI: 10.1007/s00138-024-01523-5

Hyperspectral (HS) measurement is among the most useful tools in agriculture for early disease detection. 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’s 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 ms to the 10 ms 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 ℓ 2 and Kullback–Leibler 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’ mean PSNR and mean SSIM values by 60.43% and 94.51%, respectively.