To improve denoising effect and execution efficiency of the existing switching random-valued impulse noise (RVIN) removal algorithms

we propose a convolutional neural network (CNN)-based fast non-switching RVIN denoising algorithm (FNRDA)

which consists of two serial CNN-based modules

i.e.

noise detector and denoiser. Specifically

we first use the noise detector to detect some randomly selected pixels of a given noisy image. Then we divide the number of the detected noisy pixels by the total number of detected pixels to convert it into noise ratio

which can be treated as a measure of the distortion level for the given noisy image. Finally

according to the estimated noise ratio

we exploit the corresponding pre-trained non-switching CNN-based denoising model to remove RVIN efficiently with high quality. Experimental results show that

the proposed non-switching RVIN removal algorithm outperforms the classical switching ones in terms of denoising effect and execution efficiency across various noise ratios. This advantage makes it more attractive and practical in the real-time applications such as image restoration

signal detection

wireless communication

etc.