Edge-preserving denoising using gradient-based estimation and iterative noise-aided processing
In this work, the concept of stochastic resonance has been applied to denoise the noisy input image. In stochastic resonance, a small value of noise is added to the noisy input image iteratively to enhance the edges of the input image based on which the weight coefficient is determined. It is observed the quality of the output image improves as the number of iterations is increased up to a certain number of iteration after which the quality of the image degrades or remains constant depending on the value of noise in the input image. In order to compare the quality of the denoised output image wrt the number of iterations state of the art no-reference quality metric such as Blind Universal Quality Indices (BIQI), spatial-spectral entropy-based quality (SSEQ) have been used. When compared with conventional denoising techniques such as Anisotropic diffusion (AD), Bilateral filtering (BLT), adaptive smoothing, the proposed algorithm yields marginally better results.

Noise-aided edge-preserving image denoising using non-local means with stochastic resonance

In this paper, we extend the application of stochastic resonance to improve the performance of the conventional non-local means (NLM) filtering for edge-preserving image denoising. The NLM algorithm typically involves computation of weights denoting similarity of a pixel with all other pixels in the image. In the proposed algorithm, these similarity weights are iteratively processed using the concept of dynamic stochastic resonance. The results indicate a significant improvement in sharpness of edges in the denoised images in comparison with the conventional NLM approach both visually and quantitatively in terms of full-reference and no-reference image quality metrics.

Enhanced edge detection using SR-guided threshold maneuvering and window mapping

Preserving edges in a noisy environment is a challenging task as even some of the latest end-to-end deep learning (DL) algorithms continue to struggle in achieving high pixel-level accuracy. As the Canny Edge Detector (CED) continues to be one of the most popular edge detection operators, this paper presents an enhanced CED using Stochastic Resonance (SR) guided threshold maneuvering and window mapping, which takes the same input parameter set as that of the conventional Canny but produces the edge map with better-connected edges and reduced noise. The SR-based analysis informs the steps that should be followed to enhance the performance of the classical CED. We also propose a new measure for efficient edge detection; a unique, efficient way of edge content extraction and its combination for various channels; and a framework to handle repercussions of the randomness of the noise. Since the proposed solution comes in the form of a modular patch-based framework, it can be easily incorporated into other algorithm developments. Qualitative and quantitative results are presented along with the BSDS500 & BIPED benchmarking to showcase the proposed algorithm’s effectiveness. On BIPED benchmarking, our algorithm gives the human-level performance (F1 score .79), which is appreciable considering that it is a non-DL–based algorithm.