Abstract
CMOS image sensor cameras are integral part of modern hand held devices. Traditionally, CMOS image sensors are affected by many types of noises which reduce the quality of image generated. These spatially and temporally varying noises alter the pixel intensities, leading to corrupted pixels, also known as “bad” pixels. The proposed method uses a simple neural network approach to detect such bad pixels on a Bayer sensor image so that it can be corrected and overall image quality can be improved. The results show that we are able to achieve a defect miss rate of less than 0.045% with the proposed method.
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Kalyanasundaram, G., Pandey, P., Hota, M. (2021). A Pre-processing Assisted Neural Network for Dynamic Bad Pixel Detection in Bayer Images. In: Singh, S.K., Roy, P., Raman, B., Nagabhushan, P. (eds) Computer Vision and Image Processing. CVIP 2020. Communications in Computer and Information Science, vol 1377. Springer, Singapore. https://doi.org/10.1007/978-981-16-1092-9_43
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DOI: https://doi.org/10.1007/978-981-16-1092-9_43
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