As one of the key technologies in compression sensing

a reconstruction algorithm plays a key role in scientific research.Commonly used reconstruction algorithms include the non-convex optimization algorithm based on L norm and the convex optimization algorithm based on L norm. However

these algorithms exhibit shortcomings

such as low precision reconstruction and extremely long operation time. To overcome these limitations and improve the precision and efficiency of the existing compressed sensing image reconstruction algorithm based on L norm reconstruction

an improved algorithm is presented in this study. Sequential quadratic programming is one of the most effective methods for solving constrained nonlinear programming problems in recent years. Compared with other algorithms

it demonstrates the most apparent advantages of good convergence

high computational efficiency

and edge search capability. The use of the Lagrange function series quadratic programming (SQP) method for the heather (Hesse) does not provide a positive definite matrix calculation for many problems; therefore

a value function should be introduced

the Hesse matrix sequence quadratic programming method should be corrected

and an image block compressed sensing technology should be integrated. This study proposes an image reconstruction algorithm based on L norm compression awareness. Under the same sampling rate of 40%

the signal-to-noise ratio (SNR) of the proposed algorithm is 34.28 dB

which is better than that of the block orthogonal matching pursuit (BOMP) algorithmThe BOMP algorithm's SNR is 33.76dB) and the algorithm presented when the penalty function is used as the correction method (30.23 dB). The time of the proposed algorithm is 190.55 s

which is faster than that of the BOMP algorithm (302.14 s) and that when the penalty function is used as the correction method (586.15 s). When the sampling rate is 50%

the SNR of the proposed algorithm is 35.42 dB

which is better than that of the BOMP algorithm (34.56 dB) and that when the penalty function is used as the correction method (31.38 dB). The computation time of the proposed algorithm is 196.67 s

which is faster than that of the BOMP algorithm (617.62 s) and that when the penalty function is used as the correction method (1 071.15 s). When the sampling rate is 60%

the SNR of the proposed algorithm is 36.33 dB

which is better than that of the BOMP algorithm (35.18 dB) and that when the penalty function is used as the correction method (33.57 dB). The computation time of the proposed algorithm is 201.72 s

which is faster than that of the BOMP algorithm (1 136.29 s) and that when the penalty function is used as the correction method (1 505.35 s). At a sampling rate of 70%

the SNR of the proposed algorithm is 38.62 dB

which is better than that of the BOMP algorithm (37.65 dB) and that when the penalty function is used as the correction method (35.17 dB). The calculation time of the proposed algorithm is 214.68 s

which is faster than that of the BOMP algorithm (1 802.42 s) and that when the penalty function is used as the correction method (2 415.81 s). Experimental results show that the improved algorithm is superior to the BOMP algorithm and algorithm when the penalty function is used as the correction method in terms of reconstruction precision and calculation time under the same sampling rate. When the sampling rate is higher

the precision of the reconstructed image will be higher and reconstruction time will be shorter. The convex optimization algorithm for image reconstruction based on compression perception theory is superior to the non-convex optimization algorithm for image reconstruction.However

reconstruction precision emains low and reconstruction rate emains slow

and thus

the problem remains serious. This study proposes an image reconstruction algorithm based on L norm compression awareness. Experiment results indicate that the proposed algorithm generally improves image reconstruction precision and computation time. Further study should be conducted to establish an algorithm with higher precision and faster image reconstruction.