Content area
The image rectangling task aims to solve the problem of boundary irregularity in the stitched image without reducing the wide-field-of-view content information of the stitched image. Existing image rectangling methods are either limited by application scenarios, or have a little incomplete phenomenon in the rectangular boundary. To this end, we propose a stepwise deep rectangling model based on the idea of stepwise regression for the general image rectangling task. Considering the influence of factors such as luminosity differences in various regions of the image, we introduce a shallow feature encoder to eliminate the influence of such factors on mesh prediction. At the same time, we embed the mask information into the encoded image to constrain the network to learn a rectangular image with complete boundary. Subsequently, we perform mesh cumulative regression prediction based on the multi-level features extracted by the feature extractor. Experimental results show that the proposed method performs well in a variety of stitched image rectangling scenarios, exhibiting state-of-the-art performance in both qualitative and quantitative comparisons.