Generative Model Training Across Varying Observation Conditions

The patent applies preliminary action by pre-processing training images to include multiple observation conditions during the training phase. The generator is trained on images that have been transformed to represent different observation conditions (e.g., different window levels and widths in medical imaging), enabling the model to learn and generate natural images across varying conditions without requiring post-processing adjustments.

The patent applies parameter changes by transforming images to embed observation condition information directly into the image data itself, rather than requiring separate categorical inputs. By converting observation conditions (such as window level and width parameters in medical imaging) into actual pixel value transformations, the generator learns to handle varying conditions through image parameter changes alone, eliminating the need for additional categorical input channels or complex conditional processing modules.

The patent applies merging by combining multiple observation conditions into a unified training dataset. Instead of training separate models for each observation condition or using complex multi-stage training procedures, the method merges images under different observation conditions into a single training set, allowing the generator to learn all conditions simultaneously through a unified training process that improves image quality consistency across conditions.