Non-linear locally-adaptive video contrast enhancement algorithm without artifacts

Tone-mapping functions and multiple-exposure techniques for high dynamic-range images

“…As can be observed, the SNR performance is poor when the multi-exposure technique is not incorporated. Since perception of details is also poor, we applied the local contrast-enhancing technique from our previous work [10]. Likewise, in Fig.…”

Efficient Operator for Local Energy Measurements to Enhance an Image Signal

“…One important aspect of the proposed detail enhancement scheme is the choice of the local signal energy measurement LD [5]. Let us now discuss some important possibilities and the related consequences for the implementation.…”

“…LSD is an energy measurement which is very expensive in terms of hardware/calculation, but it gives very good results without almost any disturbing "halo" artifacts [5]. If M = 2a k + 1 and N = 2b k + 1 are vertical and horizontal kernel sizes in the kth kernel, respectively, the square of the biased LSD is computed as:…”

“…Besides this, low-quality video footage originating from lowquality capturing devices poses significant challenges if an acceptable output video quality is required. In all these cases, locally-adaptive contrast enhancement will provide significant advantages for the perceived output image quality and the visibility of details [5]- [8], [11]- [16]. In our previous work [5], [6], we proposed an image enhancement method for processing an input image I(m, n) where the enhanced output image O(m, n) resulting from the multi-scale algorithm, is described by:…”

“…1(a)), and that the shape of the rejection band of the low-pass filters and the choice of LD function play a vital role in the output video quality. We also discussed ways for improving the noise performance of the algorithm and the minimization of the "halo" artifacts [5], [6]. The principal remaining problem related to image enhancement without artifacts is that the LD calculations are very expensive in terms of HW/SW computations, because they are often 2D-inseparable.…”