In general, the overall results of histogram adjustment [19] exhibit more details but less contrast.
Optimization tone mapping [22] produces a sharper image but preserve fewer details than our approach. Our tone mapping function is similar to photographic tone reproduction [30], so our results are similar to those of photographic tone reproduction in most parts of the tested images;
however, our approach can preserve details better and maintain higher contrast in most tested images. A limitation of our approach is that our tone mapping function has a weakness for dealing with whitely cloudy sky.
5.4 Discussion and limitation 32
(a) Histogram adjustment (b) Optimization tone mapping
(c) Photographic tone reproduction (d) Our result
Figure 5.4: Memorial Church in Stanford University. Randiance map courtesy of Paul Debevec.
5.4 Discussion and limitation 33
(a) Histogram adjustment (b) Optimization tone mapping
(c) Photographic tone reproduction (d) Our result Figure 5.5: Bathroom. Randiance map courtesy of Paul Debevec.
5.4 Discussion and limitation 34
(a)
(b)
(c)
(d)
Figure 5.6: MtTamWest.hdr Randiance map courtesy of ILM (a) Histogram adjustment (b) Optimization tone mapping (c) Photographic tone reproduction (d) Our result
5.4 Discussion and limitation 35
(a)
(b)
(c)
(d)
Figure 5.7: dani belgium.hdr Randiance map courtesy of Karol Myszkowski (a) Histogram adjustment (b) Optimization tone mapping (c) Photographic tone reproduction (d) Our result
5.4 Discussion and limitation 36
Figure 5.8: The scene is captured by Grzegorz Krawczyk. (a)-(c) and (g)-(i) are produced by our algorithm. (d)-(f) and (j)-(l) are produced by photographic tone production.
C H A P T E R 6
Conclusion and Future Work
We present a tone mapping method that considers the attention and adaptation effects in Human Visual System. We implicitly model the effect of sustained attention in adaptation and propose two models for transient attention and adaptation based on studies in psychophysics and neuro-science. We adopt the HDR saliency map [15][16] based on Itti et al. [3] to model the bottom-up process of attention. We also demonstrate that our results can preserve the contrast and details better than those produced by three state-of-the-art tone mapping methods: histogram adjust-ment [19], photography tone mapping operator[30], and optimization tone mapping operator [22].
In the future, when researchers learn more about the brain functions, we will replace the saliency map with a more sophisticated computational model of attention. We will also conduct some experiments to measure the visual quality of our results against the real scene in the world [47]. We would also like to apply our approach to HDR compression [7][8][9].
37
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