Abstract: For the problem that using Mean Square Error (MSE) as the target loss function leads to the difficulty of obtaining both distortion and perceptual quality of the reconstructed super-resolution image, a Super-resolution Bidirectional Recurrent Neural Network (SRBRNN) is proposed to improve the reconstruction effect. Firstly, considering the remarkable visual function of chameleon that can gaze at two different directions at the same time, SRBRNN model uses the idea of bidirectional recurrent neural network combined with the forward and reverse of sequence evolution to provide time information in different directions to the output to achieve both distortion and perceptual quality in the reconstruction process. Secondly, the SRBRNN model defines the feature evolution and degradation sequences and designs the low-resolution image to high-resolution image evolution and the high-resolution image to low-resolution image degradation network. The evolution network and degradation network are applied as the forward cycle network and reverse cycle network of the original bidirectional cycle network. Finally, the bidirectional cycling mechanism is used to reconstruct the super-resolution features. The SRBRNN algorithm is tested on Set5, Set14 and BSD100 benchmark test sets. The experimental results show that the SRBRNN algorithm outperforms other mainstream algorithms in the evaluation index of Peak Signal to Noise Ratio (PSNR) and Structural Similarity (SSIM) and subjective quality score.