Corresponding paper is available on IEEE Transactions on Industrial Informatics. [Download here]
0. Before running the demo file "computeCompensationImage_demo.m", "real_scene_demo.m", "binary_decoding_evaluation.m", "metrics_for_vgroove.m", "metrics_for_textured.m", and "noise_level_est.m", make sure the path to the current folder be set to ".\FastProjBlurComp-master".
1. To reproduce the "Efficiency Evaluation" (shown in "Section Ⅳ-A" of the manuscript), please run "computeCompensationImage_demo.m".
Note: The original image can be replaced by modifying the variable value "image_no" in the 7th line of "computeCompensationImage_demo.m", the allowable value of 'image_no' is from 1 to 15.
Note: "image_no = 1, 2, 3" stands for "Lena", "Baby" and "House" respectively, as shown in Table I.
2. To reproduce an example of the proposed method used "in Real Scenes with a Tilted Surface" (shown in "Section Ⅳ-B" of the manuscript), please run "real_scene_demo.m".
Note: "real_scene_demo.m" performs following actions in order, 1) solving the "Transfer" from the camera to the projector's coordinate frame, 2) estimating kernel αf and Γ, and 3) generating the compensation image.
Note: The original image can be replaced by modifying the variable value "image_no" in the 6th line of "real_scene_demo.m", the allowable value of "image_no" is from 1 to 7.
3. To reproduce the metrics calculation results listed in TABLE Ⅲ on both v-groove (see Fig. 11) and textured surface (see Fig. 12), please run "metrics_for_vgroove.m" and "metrics_for_textured.m", respectively.
4. To reproduce the experiment of binary-decoding-based evaluation method on a tilted surface (see Fig. 13), please run "binary_decoding_evaluation.m".
5. To reproduce the estimation results of the camera error level of the raw captured images, as listed in TABLE Ⅵ, please run "noise_level_est.m".
Note: We use the code provided by Wenzhao Zhao, which is described in the following article:
[26] W. Zhao, Q. Liu, Y. Lv, and B. Qin, "Texture variation adaptive image denoising with nonlocal PCA," IEEE Transactions on Image Processing.
| Name | Function |
|---|---|
| optimization_method.m | Generating the compensation image by using "Previous Iterative Optimization Method". Note: Previous method consumes prohibitive time that generally more than 10 minutes. |
| proposed_method.m | Generating the compensation image by using the proposed "Edge Intensification-based Algorithm", as discussed in Section Ⅱ-B. |
| Name | Function |
|---|---|
| pist_main.m | Solving the "Transfer" from the camera to the projector's coordinate frame, as discussed in "Section Ⅲ-B". |
| kernelEstimation.m | Estimating the "Defocus Kernel (PSF)" (kernel αf and Γ), as discussed in "Section Ⅲ-A". |
| compensationImage.m | Generating the "Compensation Image" in real scence by using the proposed using the proposed "Edge Intensification-based Algorithm". |
| Name | Function |
|---|---|
| decode_conGraycode_version2.m | Decoding from the captured binary-encoded images, which is generally used in the context of structured-light 3D measurement, as discussed in "Section Ⅳ-C". |
| Name | Function |
|---|---|
| noise_evaluation_acva.m | Estimating the camera errors based on Poisson-Gaussian noise model, by using the code provided in [26]. |
| Name | Function |
|---|---|
| subfolder "DOT" | Captured images of the "Dot Patterns", as discussed in "Section Ⅲ-A" and "Figure 5" |
| subfolder "RE1" | Caputred images of the "Horizontal Sinusoidal Projections", as discussed in "Section Ⅲ-B" and "Figure 5" |
| subfolder "RE6" | Caputred images of the "Vertical Sinusoidal Projections", as discussed in "Section Ⅲ-B" and "Figure 5" |
| folder "vgroove1-lite" | Captured images from the complex surface, v-groove, as discussed in "Section Ⅳ-B" and "Figure 11" |
| folder "textured_lite" | Captured images from the complex surface, textured surface, as discussed in "Section Ⅳ-B" and "Figure 12" |
| folder "binary-30-lite" | Captured images of the "gray-code binary-encoded patterns", as discussed in "Section Ⅳ-C" and "Figure 13" |