Abstract: Reflection in the image not only significantly reduces the image quality, but also seriously affects the subsequent computer vision tasks. So proposed a single image reflection removal cascaded algorithm using non local correlation and contrast constraint. This algorithm utilizes a dual-branch approach for LSTM-based information propagation across cascades. It employs reflection and background features to complement each other and iteratively refine prediction accuracy, ensuring mutual enhancement of the two branches' prediction results. To facilitate training for multiple cascade steps, a positive-negative contrastive regularization loss is introduced. This loss treats background images and original images features as positive and negative samples, respectively. This ensures that the target image is brought closer to the background image while moving away from the original image in the representation space, narrowing the prediction range and effectively alleviating the ill-posed problem. Additionally, an efficient, low-computational-cost non-local correlation prediction module is proposed, capable of capturing contextual information for all pixels along cross paths. Through further cascade operations, each pixel captures long-distance dependencies across the entire image, enabling the use of surrounding point information to predict background information obscured by strong reflections. Experimental results demonstrate that, compared to current algorithms, the proposed algorithm achieves superior results and exhibits robust performance.

Key words: image reflection, cascaded, regularization, contextual information, long-range dependence