Image Inpainting with GANs on LOKI Dataset

Inpainting images becomes crucial, especially when dealing with the challenging LOKI zooplankton dataset. This research presents a novel framework for image inpainting, designed to optimize results through a systematic approach. Our proposed framework effectively addresses these challenges, enhancing both the qualitative and quantitative aspects of inpainting quality. Through evaluation on the LOKI dataset, we achieved an overall Dice score of 0.7604, indicating a high level of accuracy. Additionally, the inclusion of squared masks proved advantageous in predicting missing components across images. Our innovative framework for inpainting plankton images in the LOKI dataset overcomes limitations by enabling inpainting within squareshaped regions, without requiring prior pixel location knowledge. This approach leverages a MultiRes-UNet generator, two discriminator networks with EfficientNet architecture, and a pretrained feature extractor network (VGG19), resulting in efficient performance in terms of our task.