Abstract:

In the process of super-resolution processing, the noise inherent in low resolution images will be amplified, resulting in distortion of super-resolution images. To this end, a super-resolution image noise recognition method based on neural networks has been proposed. The Activation function in the neural network is used to determine the peak signal to noise ratio. By combining the noise data set and hyperparameter coefficients, residual values are obtained, and combined with the noise information distribution density, super- resolution image noise recognition is achieved. The experimental results show that the proposed method has high clarity and good recognition performance in super-resolution images, with a maximum peak signal-to-noise ratio of 50 dB, indicating that the use of the proposed method can improve image quality.

Key words: neural network, super resolution images, noise identification, residual value, peak signal-to-noise ratio, hyper parameter coefficient, image clarity