**5. Conclusions**

*Biomimetics*

**4. Discussion**

(see **Figure 2C**).

deviation was described by Donoho [26].

the pixel-wise operations employed.

patchy regions of homogeneous color, when the same noisy image was used as input. Furthermore, GSM wavelet denoising of this image resulted in image blur.

The first two image-processing steps of this novel bionic NV algorithm were inspired by the transducer gain of photoreceptor cells of nocturnal insects and the spatial integration of image information in lamina neurons of *M. genalis* [2]. The combination of them allowed the strong enhancement of the contrast of dim images and effectively removed noise that would have been amplified after performing histogram stretching (see **Figures 2** and **3**). A drawback of many denoising algorithms is obvious by the loss of fine image details and sharp object contours, which leads to image blur and staircase effects. In contrast, spatial domain-filtering by means of 'adaptive spatial averaging' showed an unexpected denoising performance, which removes noise without affecting fine image details and object contours. The denoising performance of the NV algorithm is comparable to the performance of well-known denoising techniques such as wavelet, bilateral, TV, ATVM, BM3D filtering as long as the noise level of the input image remains below a critical value. If noise dominates the input image, the output image may contain small artifacts in the form of grain noise in homogeneous image regions

Automatic parameter estimation as described here was sufficient to improve the quality of 12 pictures having different average brightness and noise levels. The method used here for noise estimation is similar to that described by Förstner [24] who estimated the noise level from the gradient of smooth or fine-textured regions, whereby these authors estimated signal-dependent noise level for each intensity interval. Similarly, Stefano et al. [25] described three methods to estimate the noise levels of natural images, and a noise estimation method based on mean absolute

It will be possible to modify the bionic NV algorithm to improve the contrast of image sequences in dim films such as those generated by surveillance cameras operating at low light levels or digital night vision goggles. To reduce the noise of input images, it may be helpful to insert an additional processing step that performs a temporal summation of grey values by averaging across subsequent frames. A bionic method that operates in the spatial and temporal domains was described by Warrant et al. [5]. The night vision method described there is based on a smoothing kernel that is constructed for each pixel in three dimensions (two in space and one in time). In contrast to this complex algorithm, the NV algorithm described in this study is computationally less demanding and even runs on FPGA hardware due to

FPGA hardware can process images almost in real time due to its parallel architecture [19, 20]. To compute histogram statistics and equalization in parallel on a FPGA chip [27], non-conventional schemes for real-time histogram equalization have been discussed and implemented by Alsuwailem and Alshebeili [28]. Furthermore, several studies have investigated the implementation of the brightness control, contrast stretching, and histogram equalization algorithm on FPGA [29, 30], which has become a competitive alternative for high-performance digital signal processing (DSP) applications. Bittibssi et al. [31] addressed the hardware implementation of five image-enhancement algorithms in the spatial domain using FPGA: median filter, contrast stretching, histogram equalization, negative image transformation, and power law transformation. Recently, this NV algorithm was successfully implemented on a Trenz Electronic FPGA hardware

**44**

This bee-eyes inspired NV algorithm is based on rather simple calculations and operates in the spatial domain to suppress sensor noise in static images. It is applicable to a great variety of dim images differing in their brightness and degree of noise. Such a simple algorithm is suitable for FPGA technology, which allows image processing steps to be executed in parallel at the level of pixels. Since all parameters can be derived from the statistics of the input images, its use in the form of digital night vision goggles, medial applications and real-time fluorescence imaging systems is possible. In the future, this method will be adapted for night vision cameras that almost operate in the dark. For this task, subsequent video frames will be averaged to improve visual information. Another project is dedicated to the enhancement of dim X-ray images. This approach is based on the assumption that X-ray exposure during breast cancer screenings can be reduced by the amplification and denoising of slightly underexposed diagnostic raw data images. This is a challenging task because the resolution of such images as well as the range of grey values (16 Bit) is high and the procedure used for the noise estimation as well as the calculation of the threshold for "adaptive spatial averaging" needs to be modified.
