Neural edge enhancer for supervised edge enhancement from noisy images

Abstract

We propose a new edge enhancer based on a modified multilayer neural network, which is called a neural edge enhancer (NEE), for enhancing the desired edges clearly from noisy images. The NEE is a supervised edge enhancer: Through training with a set of input noisy images and teaching edges, the NEE acquires the function of a desired edge enhancer. The input images are synthesized from noiseless images by addition of noise. The teaching edges are made from the noiseless images by performing the desired edge enhancer. To investigate the performance, we carried out experiments to enhance edges from noisy artificial and natural images. By comparison with conventional edge enhancers, the following was demonstrated: The NEE was robust against noise, was able to enhance continuous edges from noisy images, and was superior to the conventional edge enhancers in similarity to the desired edges. To gain insight into the nonlinear kernel of the NEE, we performed analyses on the trained NEE. The results suggested that the trained NEE acquired directional gradient operators with smoothing. Furthermore, we propose a method for edge localization for the NEE. We compared the NEE, together with the proposed edge localization method, with a leading edge detector. The NEE was proven to be useful for enhancing edges from noisy images.

Hybrid Filter Based on Neural Networks for Removing Quantum Noise in Low-Dose Medical X-ray CT Images

“…A NEE for clearly enhancing the desired edges from noisy images was proposed by Suzuki [17]. The NEE consists of a modified multilayer NN (The proposed NF has the same structure of NN as NEE shown in Figure 2.…”

“…The architecture of the NF is shown in Figure 2, which is the same as the NEE [17]. The number of units in the input is equal to 12(or 14), which is the sum of the number of filters (5 conventional BFs and 5 median version BFs), the NEE(1 or 3), and one direct noisy input.…”

Pixel-Based Artificial Neural Networks in Computer-Aided Diagnosis

“…Edge enhancement from noisy images [22]. Enhancement of subjective edges traced by a physician [23].…”

Adding explicit content classification to nonlinear filters

“…The identity function is often employed in the Fig. 1 The two-layer neural network model with several hidden units at the hidden layer output layer because the characteristics of a neural network are improved significantly with an identity function when applied to function approximation issues in image processing [22]. When all the transfer functions are identity functions, the neural filter becomes a linear filter.…”