Patchdrivenet

Patch-Driven-Net is a deep learning-based image processing approach that leverages the power of CNNs to process images in a patch-wise manner. The core idea behind Patch-Driven-Net is to divide an input image into small patches, process each patch independently using a CNN, and then aggregate the results to form the final output. This patch-wise processing approach allows Patch-Driven-Net to effectively capture local patterns and textures in images, leading to improved performance in various image processing tasks.

: By processing the image in patches, the system can identify which parts of its view are being tampered with or are "noisy." patchdrivenet

: The patch-driven approach makes the model more resilient to occlusions or image corruption, as the network can still identify objects based on the remaining visible patches. Scalability : By processing the image in patches, the