Faster,Lighter,Stronger: Image Rectangling Using Multi-Teacher Instance-Level Distillation

Image rectangling research aims to solve the problem of irregular boundaries in stitched images, which helps to enhance the visual satisfaction of electronic consumers with the imaging of electronic products. To do so, it learns the mesh warping rules of the image, allowing for the generation of a rectangular image with content fidelity. Existing image rectangling solutions either require two stages of warping processing, or one stage but multiple levels of warping processing to achieve the rectangling effect, both of which are inefficient at rectangling and are prone to cumulative errors and distortions of images. Based on an optimization exploration of a single-stage and multi-level warping approach, we construct an image rectangling knowledge distillation task. An image rectangling network with single-stage and single-level warping is proposed after analyzing the distillation task construction impact factors. The proposed method, combined with our multi-teacher guided instance-level knowledge distillation strategy, utilizes only 2.53% of the parameters employed by the state-of-the-art method. It achieves improvements of 4.68% in SSIM, 1.94% in PSNR, and 1.01% in NIQE metrics, while also achieving a speedup of 81.38%. The codes and models will be available at https://github.com/MmelodYy/Distill_Rectangling.