Image Sensor Bad Pixel Correction Using Position-Aware Neural Processing

The patent segments the bad pixel correction task into two distinct phases: an offline training phase where a neural network model is trained using labeled bad pixel data, and an online inference phase where the trained model is deployed for real-time correction. This segmentation allows computationally intensive training to be performed separately from time-critical real-time processing, resolving the contradiction between accuracy and speed.

The patent performs preliminary training of the neural network model offline before deployment. During this preliminary action, the model learns to identify and correct bad pixels using extensive arithmetic operations that would be too slow for real-time processing. Once trained, the model is saved and reused during real-time operation, eliminating the need to repeat expensive training computations during actual image processing.

The patent creates a digital copy or model of the pixel array's expected behavior through the neural network. Instead of physically replacing or repairing defective pixels, the system learns the normal response patterns of pixels and uses this knowledge to generate corrected values for bad pixels by comparing actual readings against the learned model, effectively copying the expected correct signal.

The patent introduces a neural network model as an intermediary between the raw pixel data and the final image output. This intermediary processes the pixel signals, identifying and correcting bad pixel values based on learned patterns from training data, thereby mediating the effect of miniaturization-induced defects on the final image quality.

The patent performs the complex neural network training operation in advance during an offline phase, storing only the trained model parameters (weights and biases) in the device. This preliminary action transfers the computational burden from the deployed device to the training phase, allowing the device to use a relatively simple inference process that requires minimal computational resources while maintaining high correction capability.

The patent extracts only the essential correction functionality from the full neural network training process. Instead of implementing the entire training pipeline in the device, the system extracts the trained model parameters and stores them, separating the complex training operation (performed externally) from the simple inference operation (performed in the device). This extraction reduces device complexity while preserving correction capability.