CASE STUDY 119

(a) 256 × 256 Bernoulli pattern (b) Experimental setup.

Figure 7.8: Compressive dual photography. (Extracted from [55].)

We have already enforced that one of the most significant advan-tages of CS is that it is nonadaptive. In this scenario, this implies that the procedure does not require real time processing during ac-quisition as in [57], where an estimation of the energy distribution has to be made prior to sensing. Since the patterns are all pre-computed, they can be displayed at an extremely fast framerate, without the need of any computational power for run-time processing.

Moreover, the illumination patterns are chosen regardless of the scene. This is true even in the cases when we must consider a different basis in which the signal is sparse, once the knowledge of the Ψ basis is only used for reconstruction and not for sensing² These simple binary patterns are easy to implement (compared e.g. to a basis of Daubechies Wavelets) and make good use of the limited dynamic range and quantization of the projector, thereby improving the SNR of the results.

Figure 7.9 shows a result obtained by [55]. We observe that the technique is able to capture global illumination effects such as diffuse-diffuse inter-reflections. However, in this more extreme case, they tend to fall off quicker than the ground truth image. The authors associated this difference with the limitations of the HDR capture configuration. Notice that, since the contrast between brightest and

2It is of course essential that L^T still meets the RIP when combined with Ψ.

dimmest entries in this matrix can be large, these limitations can lead to significant inaccuracies.

(a) Ground truth. (b) Rendered image.

Figure 7.9: Results extracted from [55].

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