Hardware/software solution for high precision defect correction in digital image sensors

High resolution image sensors are now standard in imaging devices such as mobile phones with camera functionality. Resolution improvement in very small sensors is obtained by decreasing the pixel size but, in CMOS sensors, the likelihood of defective pixels also augments. Hence, sophisticated processing is necessary for achieving high quality images despite of noise and defects. This paper presents a hardware/software solution for high precision correction of defective pixels in an image sensor. The method maintains an always up-to-date map of the defective pixels and also allows detection of new defects as they show up during the lifetime of the sensor. The reliability of the map is assured by tracking the history of pixels defectiveness. The map is updated automatically and in real time without user intervention.

[1]  Aishy Amer,et al.  Fast and reliable structure-oriented video noise estimation , 2005 .

[2]  Charles K. Chui,et al.  A universal noise removal algorithm with an impulse detector , 2005, IEEE Transactions on Image Processing.

[3]  A. Bruna,et al.  Spatio-temporal filter with adaptive multiple outliers rejector , 2006, 2006 Digest of Technical Papers International Conference on Consumer Electronics.

[4]  Andrzej Chydzinski,et al.  Fast detection and impulsive noise removal in color images , 2005, Real Time Imaging.

[5]  Amar Mitiche,et al.  Reliable and fast structure-oriented video noise estimation , 2002, Proceedings. International Conference on Image Processing.

[6]  Stewart Smith,et al.  Fast Noise Level Estimation using a Convergent Multiframe Approach , 2006, 2006 International Conference on Image Processing.

[7]  Etienne E. Kerre,et al.  A New Fuzzy Color Correlated Impulse Noise Reduction Method , 2007, IEEE Transactions on Image Processing.

[8]  Jaeheon Lee,et al.  Image feature and noise detection based on statistical hypothesis tests and their applications in noise reduction , 2005, IEEE Trans. Consumer Electron..

[9]  Ioannis Andreadis,et al.  Real-time adaptive image impulse noise suppression , 2004, IEEE Transactions on Instrumentation and Measurement.