Threshold optimization method of single-bit quanta image sensor based on bit density

Abstract

To improve the quality of reconstructed images of single-bit quanta image sensors, a self-adaptive threshold optimization method is proposed. The method can adaptively adjust the quantization threshold according to different light intensities, making the quality of the reconstructed image improved. First, the bitstream data from the quantization of real images are obtained by sensor model. Second, the optimal threshold value of each row is determined by calculating the bit-density, followed by the threshold value updated according to the deviation value between the bit-density of a single-pixel and 0.5. Finally, the optimal threshold matrix obtained by this method is used to reconstruct the image to improve imaging quality. Four identical test images were processed by the proposed method, the traditional method, and Elgendy’s variable threshold method, respectively. The results show that the proposed method can improve the peak signal-to-noise ratio of the reconstructed image in comparison with the traditional uniform threshold method. Compared with Elgendy’s method, the proposed method can effectively reduce the number of iterations under the same parameter conditions. These experiments prove that the proposed method can effectively improve the quality of reconstructed images with fewer iterations.