Use of wavelet for image processing in smart cameras with low hardware resources
Images from embedded sensors need digital processing to recover high-quality images and to extract features of a scene. Depending on the properties of the sensor and on the application, the designer fits together different algorithms to process images. In the context of embedded devices, the hardware supporting those applications is very constrained in terms of power consumption and silicon area. Thus, the algorithms have to be compliant with the embedded specifications i.e. reduced computational complexity and low memory requirements. We investigate the opportunity to use the wavelet representation to perform good quality image processing algorithms at a lower computational complexity than using the spatial representation. To reproduce such conditions, demosaicing, denoising, contrast correction and classification algorithms are executed over several well known embedded cores (Leon3, Cortex A9 and DSP C6x). Wavelet-based image reconstruction shows higher image quality and lower computational complexity (3x) than usual spatial reconstruction. The use of wavelet decomposition also permits to increase the recognition rate of faces while decreasing computational complexity by a factor 25.
wavelet demosaicing denoising recognition dwt embedded systems