Images were divided into four equal-sized blocks by adaptive compress ive imaging.The four blocks were classified into two types after getting the number of important discrete wa velet transform (DWT) coefficients,which are the important blocks and the unimportant blocks.The important blocks were all sampl ed with higher resolution, leading to high sampling rate.To solve these problems,an imaging method is de signed based on fine precision classification of image blocks.Coarse image is reconstruted and divided into f our equal-size blocks first .Then the four image blocks are classified into three types according to the number a nd distribution of their important DWT coefficients,which are the unimportant bl ocks,the important blocks and the partly important blocks.The unimportant blocks are ignored.The important blocks are all sampl ed with higher resolution.The partly important blocks are divided repeatedly,and only the important parts are sampled.Image with higher resolution is reconstructed through inverse wavelet transform.Steps above are repeated until the reconstructed image gets required resolution.Experimental re sults show that compared with other three algorithms, the peak signal-to-noise ratio (PSNR) of reconstructed images is improved by at least 2dB with similar sampling rate for biology imag es.