Utilizing Multi-step Loss for Single Image Reflection Removal
This addresses image quality restoration for applications like object detection and image segmentation, though it appears incremental with a new training technique applied to an existing problem.
The paper tackles single image reflection removal by introducing a multi-step loss training technique and creating a synthetic dataset called RefGAN using Pix2Pix GAN. The approach outperforms state-of-the-art models on the SIR^2 benchmark and other real-world datasets.
Image reflection removal is crucial for restoring image quality. Distorted images can negatively impact tasks like object detection and image segmentation. In this paper, we present a novel approach for image reflection removal using a single image. Instead of focusing on model architecture, we introduce a new training technique that can be generalized to image-to-image problems, with input and output being similar in nature. This technique is embodied in our multi-step loss mechanism, which has proven effective in the reflection removal task. Additionally, we address the scarcity of reflection removal training data by synthesizing a high-quality, non-linear synthetic dataset called RefGAN using Pix2Pix GAN. This dataset significantly enhances the model's ability to learn better patterns for reflection removal. We also utilize a ranged depth map, extracted from the depth estimation of the ambient image, as an auxiliary feature, leveraging its property of lacking depth estimations for reflections. Our approach demonstrates superior performance on the SIR^2 benchmark and other real-world datasets, proving its effectiveness by outperforming other state-of-the-art models.