Design and Implementation of Disparity Estimation Circuit with Light-Noise Resistance

Abstract

With the advancement of science and technology human needs for image applications have gradually transformed from traditional two-dimensional images to three-dimensional images In other words one more demand for "depth information" is required in the image data The depth information can be widely used in various forward-looking systems such as autonomous driving technology augmented reality (AR) virtual reality (VR) robot navigation judgment Therefore designing an effective depth prediction method to generate high-precision depth information is an important and urgent issue Among various depth prediction methods the simplest and most effective way is the dual-lens depth method Via this method the left-image and right-image are first taken by two parallel lenses The disparity estimation algorithm is designed to estimate the disparity values between the left- and right- image The final depth result is then generated with the known information such as the focal length and the distance between two lenses Thus the disparity estimation algorithm can be regarded as the vital step in the dual-lens depth method and its performance significantly impacts the final depth information results Many studies have also implemented the disparity estimation algorithm into the digital circuit to improve performance in recent years However there are still several problems in practical applications that need to be solved such as colossal hardware resource usage excessive prediction error rate low execution frequency and interference from external light noise Therefore a practical disparity estimation algorithm must be designed to satisfy the requirements for low error rate high operation frequency and resistance to light noise under the application of limited hardware resources In this dissertation a guided image filter (GIF) -based disparity estimation algorithm is proposed which aims at (1) reducing hardware resources and increasing the operation frequency (2) reducing the prediction error rate and (3) reducing the impact of light noise Regarding the first target based on the concept of mutual confirmation we adopted the left-right decision tree check method in our algorithm so that the left and right images only check the even and odd disparity values respectively thereby halving resource costs In terms of the second target our algorithm introduces the continuous plane segmentation method to correct the inconsistent points during the same plane to enhance the overall accuracy Finally to achieve the last target the Hamming distance method is adopted in our algorithm It can still search for similar points between the left and right images under light noise and generate the correct disparity values The proposed algorithm was implemented with the Xilinx Vertex-7 and Kintex-7 field-programmable gate array (FPGA) and its performance was tested using the Middlebury -version 2 and -version 3 data sets The version 3 data set is used primarily for testing the accuracy of disparity values without light noise The version 3 data set adds the light noise factor and the complexity of the images in this data set also increases According to the experiment results the proposed algorithm provides an operation speed of 133 fps with an error rate of only 6 36% in the version 2 data set Our algorithm can achieve the highest operation frequency and lowest testing error rate compared with other GIF-based disparity estimation algorithms When testing by the version 3 data set the average error rate is 19 65% which can also perform the lowest test error rate compared with algorithms that can also be applied to light-noise environments which is caused by lighting noise Besides this design is also the first GIF-based algorithm which can be applied in the light-noise environment In the end we use the proposed algorithm to estimate the disparity values of the images taken in the actual environment and get excellent estimation results which proves that the proposed algorithm is quite suitable to be applied in practical cases

Date of Award	2021
Original language	English
Supervisor	Pei-Yin Chen (Supervisor)