GUNet++: guided-U-Net-based compact image representation with an improved reconstruction mechanism

The invention of microscopy- and nanoscopy-based imaging technology opened up different research directions in life science. However, these technologies create the need for larger storage space, which has negative impacts on the environment. This scenario creates the need for storing such images in a memory-efficient way. Compact image representation (CIR) can solve the issue as it targets storing images in a memory-efficient way. Thus, in this work, we have designed a deep-learning-based CIR technique that selects key pixels using the guided U-Net (GU-Net) architecture [Asian Conference on Pattern Recognition, p. 317 (2023)], and then near-original images are constructed using a conditional generative adversarial network (GAN)-based architecture. The technique was evaluated on two microscopy- and two scanner-captured-image datasets and obtained good performance in terms of storage requirements and quality of the reconstructed images.