Univ. Grenoble Alpes, Grenoble, France, CEA-LETI, MINATEC Campus, Grenoble, France

This paper discusses the impact of Fixed Pattern Noise (FPN) on image compression embedded in CMOS sensors. This FPN is mainly due to technology dispersions as well as a non uniform topology of the layout. FPN is generally modelled as a non uniform affine mapping of the pixels. The Dark Signal Non-Uniformity (DSNU) represents the level of offset variations while Photo Response Non-Uniformity (PRNU) is the level of pixel gain variations. Once those two parameters are properly calibrated for each pixel, the image can be corrected by linear interpolations (i.e. affine correction). This operation requires specific processing and large memory resources to be implemented directly in the focal plane. If this 2-points correction is not performed inside the sensor, some problems arise in the context of embedded compression because the hypothesis on image sparsity is no longer satisfied. By considering this context, this paper presents a comparative study between a 2D Haar wavelet compression, separable Compressive Sensing (CS) and a novel approach combining wavelet based compression and row based CS. In the case of a high non uniformity of the focal plane (e.g. considerable technological dispersion), it appears that a proper combination of CS and Haar takes advantage of both enabling FPN correction at the reconstruction stage and outperforming the alternative techniques. © 2017 IEEE.

advanced CMOS image sensor, Compressive sensing, Fixed Pattern Noise, Wavelet image compression

CMOS integrated circuits, Compressed sensing, Focusing, Pixels, Advanced CMOS, Compressive sensing, Dark signal non uniformities, Embedded compression, Fixed pattern noise, Linear Interpolation, Photo response non uniformities (PRNU), Wavelet-based compressions, Image compression