Fast Projection Defocus Correction for Multiple Projection Surface Types

A major obstacle in digital projector technology is that images projected onto nonideal surfaces with large depth variances can easily become blurred. In this article, present a method to overcome projection defocus for projection surfaces that inevitably have complex shapes and large depth variances. The proposed method has two main advantages over traditional methods. First, an edge-intensification-based defocus compensation algorithm is proposed to manipulate the input image to compensate for its projection defocus before blurring occurs. Unlike previous time-consuming compensation algorithms, the proposed algorithm has very high efficiency, as it is noniterative and open loop. Second, a sinusoidal-projection-based estimation method is proposed to reduce kernel estimation errors on complex surface types. Unlike previous methods limited to specific surface types, the proposed method can provide consistently good kernel estimation results even for discontinuous and textured (nonpure white) projection surfaces. Hence, the proposed method can be applied to a wider range of applicable surfaces. These two contributions are demonstrated through extensive experiments and compared with the state-of-the-art methods. (1) (1) The MATALB code and experimental data for this article are available at https://github.com/lpl-code/FastProjDefocusComp.

Automated summary

The article presents a method to overcome projection defocus on nonideal surfaces, using an efficient defocus compensation algorithm and estimation method for accurate kernel estimation on complex projection surfaces.