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As a noninvasive, nonradiative and high-speed imaging modality, fluorescence imaging in the second near-infrared window (NIR-II, 1,000-1,700 nm) has demonstrated great potential for biomedical research and clinical study. The NIR-II window can be further divided into two spectral regions: NIR-IIa (1,000-1,300 nm) and NIR-IIb (1,500-1,700 nm). Compared to NIR-IIa, imaging in NIR-IIb region affords high-resolution imaging at subcentimeter tissue depths due to suppressed photon scattering and diminished tissue autofluorescence at long wavelengths, but relies on probes with high toxicity. To address the problem, researchers employ deep learning networks to attain NIR-IIb images from NIR-IIa images. However, current methods require numerous paired or unpaired images (more than 2800 images) as training sets, which can hardly acquire. In this work, an innovative convolutional neural network (BRCycle-GAN) is trained based on a small training set (merely 63 images) to transform NIR-IIa images into images with NIR-IIb imaging qualities. The NIR-IIb images generated by BRCycle-GAN outperform previous network models in terms of peak signal-to-noise ratio, cosine similarity and other image evaluation indices.