Progressive CNN-transformer alternating reconstruction network for hyperspectral image reconstruction—A case study in red tide detection

• Propose a progressive CNN-transformer alternating reconstruction network. • Minimize exposure and noise through reconstruction to enhance detection robustness. • Pioneer the application of spectral reconstruction for red tide detection. Spectral reconstruction technology extracts rich detail information from limited spectral bands, thereby enhancing both of the image quality and the resolution capabilities. It finds application in non-destructive testing, elevating the precision and robustness of detection. Current studies primarily focus on improving the local information perception of convolutional neural networks or modeling long-distance dependencies with Transformer. However, such approaches fail to effectively integrate global–local modeling information, resulting in poor accuracy in image reconstruction. This paper introduces a Progressive CNN-Transformer Alternating Reconstruction Network (PCTARN) to alternately utilize robust convolutional attention and transpose Transformer self-attention. A Dual-Path CNN-Transformer Alternating Reconstruction Module (DPCTARM) is proposed to dynamically introduce global–local dynamic priors at various levels to facilitate extracting high- and low-frequency features. This enhancement effectively strengthens PCTARN’s capability to discern valuable signals. To verify the proposed method, a spectral dataset based on seven selected red tide algae is collected. And a peak signal-to-noise ratio (PSNR) metric of 34.58 dB is achieved, which is at least 0.44 dB higher than the methods such as MAUN and MST++. While the Params and FLOPS are reduced by over 41.9 % and 38.4 %, respectively. Since the performance of the proposed PCTARN depends not only on image quality but also on spectral fidelity, an application of spectral detection on red tide are conducted for this purpose. Four feature bands are selected from multispectral images and reconstructed into 20-band hyperspectral images by using PCTARN. Species identification and cell concentration detection are conducted based on the reconstructed images. The results demonstrate that PCTARN can enhance the spatial signal and spectral peak differences of red tide samples, achieving an identification accuracy of 94.21 % and a coefficient of determination ( R2 ) of 0.9660 in species identification and cell concentration detection, which are respectively improved by 11.55 % and 11.59 % compared to those of 4-band multispectral detection.