An Improved Fourier-Mellin Transform-Based Registration Used in TDI-CMOS

Time-delayed integration (TDI) is a common imaging mode used in airborne cameras to compensate for image motion. The paper presents an improved Fourier-Mellin transform (FMT)-based registration method, which can be used to realize the registration-based TDI. It first analyzes the noise influences on the FMT spectrum by comparing the curve changes in the standard-deviation versus column plots under the various intensity of noise, then designs a special filter called WCSDF according to the amount of variation of each column in the FMT spectrum, which can reduce the noise affection on registration process to the larger extent. And an improved FMT-based registration method is finally proposed to form a framework of registration-based TDI. In computer simulations, the proposed method shows a significant improvement in robustness to noise (noise level up to 40). Compared with the existing method, its low computational complexity makes the method easy to be implemented in hardware and can estimate the larger relative shifts among dim noisy images, moreover, the TDI images generated by the proposed framework have higher quality in the index of information entropy, average gradient, and spatial frequency response.

[1]  C. Hsieh,et al.  A 32-Stage 15-b Digital Time-Delay Integration Linear CMOS Image Sensor With Data Prediction Switching Technique , 2017, IEEE Transactions on Electron Devices.

[2]  Agnes C. Kleimann,et al.  Frame transfer CCDs for digital still cameras: Concept, design, and evaluation , 2002 .

[3]  Yabo Fu,et al.  Deep Learning in Medical Image Registration: A Review , 2020, Physics in medicine and biology.

[4]  Gaylord G. Olson Image motion compensation with frame transfer CCDs , 2002, Optics East.

[5]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[6]  Levent Malgaca,et al.  Analysis of active vibration control in smart structures by ANSYS , 2004 .

[7]  M. Petrou Image Registration: An Overview , 2004 .

[8]  Guojin He,et al.  An Extension of Phase Correlation-Based Image Registration to Estimate Similarity Transform Using Multiple Polar Fourier Transform , 2018, Remote. Sens..

[9]  Peter D. Burns,et al.  Refined Slanted-Edge Measurement from Practical Camera and Scanner Testing , 2002, PICS.

[10]  Masaaki Ikehara,et al.  High-Accuracy Image Rotation and Scale Estimation Using Radon Transform and Sub-Pixel Shift Estimation , 2019, IEEE Access.

[11]  Xuemei Hu,et al.  Time-Delay-Integration Imaging Implemented With Single-Photon-Avalanche-Diode Linear Array , 2021, IEEE Sensors Journal.

[12]  Bo Tao,et al.  Analysis of image registration noise due to rotationally dependent aliasing , 2003, J. Vis. Commun. Image Represent..

[13]  Wei Xu,et al.  Realize the Image Motion Self-Registration Based on TDI in Digital Domain , 2019, IEEE Sensors Journal.

[14]  Cordelia Schmid,et al.  A performance evaluation of local descriptors , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  Amir Averbuch,et al.  Pseudopolar-based estimation of large translations, rotations, and scalings in images , 2005, IEEE Transactions on Image Processing.

[16]  Jan Flusser,et al.  Image registration methods: a survey , 2003, Image Vis. Comput..

[17]  C. Morandi,et al.  Registration of Translated and Rotated Images Using Finite Fourier Transforms , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[18]  Luc Van Gool,et al.  Speeded-Up Robust Features (SURF) , 2008, Comput. Vis. Image Underst..

[19]  Hassan Foroosh,et al.  An Exact and Fast Computation of Discrete Fourier Transform for Polar and Spherical Grid , 2017, IEEE Transactions on Signal Processing.

[20]  B. N. Chatterji,et al.  An FFT-based technique for translation, rotation, and scale-invariant image registration , 1996, IEEE Trans. Image Process..

[21]  Cristian Secchi,et al.  A Passivity-Based Approach for Simulating Satellite Dynamics With Robots: Discrete-Time Integration and Time-Delay Compensation , 2020, IEEE Transactions on Robotics.

[22]  Jiangtao Xu,et al.  A 128-Stage CMOS TDI Image Sensor With On-Chip Digital Accumulator , 2016, IEEE Sensors Journal.

[23]  Qian Du,et al.  Image Registration With Fourier-Based Image Correlation: A Comprehensive Review of Developments and Applications , 2019, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[24]  Jiangtao Xu,et al.  A 128-Stage Analog Accumulator for CMOS TDI Image Sensor , 2014, IEEE Transactions on Circuits and Systems I: Regular Papers.

[25]  Limin Zhang,et al.  SPAD Sensors with $256\times 2$ Linear Array for Time Delay Integration Demonstration , 2018, 2018 IEEE SENSORS.

[26]  Chao Xu,et al.  Attitude Maneuver Planning of Agile Satellites for Time Delay Integration Imaging , 2020 .

[27]  Da-Chiang Chang,et al.  Dynamic image acquisition and verification for a 32-stages time delay and integration CMOS image sensor , 2018, Remote Sensing.

[28]  Jing Gao,et al.  Digital domain dynamic path accumulation method to compensate for image vibration distortion for CMOS-time-delay-integration image sensor , 2020 .