  Region modification-based data augmentation techniques have shown to improve
performance for high level vision tasks (object detection, semantic
segmentation, image classification, etc.) by encouraging underlying algorithms
to focus on multiple discriminative features. However, as these techniques
destroy spatial relationship with neighboring regions, performance can be
deteriorated when using them to train algorithms designed for low level vision
tasks (low light image enhancement, image dehazing, deblurring, etc.) where
textural consistency between recovered and its neighboring regions is important
to ensure effective performance. In this paper, we examine the efficacy of a
simple copy-blend data augmentation technique that copies patches from noisy
images and blends onto a clean image and vice versa to ensure that an
underlying algorithm localizes and recovers affected regions resulting in
increased perceptual quality of a recovered image. To assess performance
improvement, we perform extensive experiments alongside different region
modification-based augmentation techniques and report observations such as
improved performance, reduced requirement for training dataset, and early
convergence across tasks such as low light image enhancement, image dehazing
and image deblurring without any modification to baseline algorithm.
