For flexible non-blind image denoising, existing deep networks usually concatenate noisy
image and noise level map as the input for handling various noise levels with a single …

Traditional shrimp recognition algorithms, based on machine vision, commonly utilize
human-designed features, which are heavily dependent on human experience and can be …

The adaptive block size processing method in different image areas makes block-matching
and 3D-filtering (BM3D) have a very good image denoising effect. Based on these …

We propose an adaptive learning procedure to learn patch-based image priors for image
denoising. The new algorithm, called the expectation-maximization (EM) adaptation, takes a …

In this paper, we propose an advanced framework for image denoising using bilateral
filtering based non-subsampled shearlet transform (NSST). Initially, we apply the NSST to …

Digital images are more important in numerous contemporary applications, and the need for
images in the technical field is also increasing drastically. It is used to recognize signatures …

The key problem of image denoising methods is to smooth noise while retaining the details
of original image. The human vision system is more sensitive to the details (or the high …

This paper deals with the parameter choice for the nonlocal means (NLM) algorithm. After
basic computations on toy models highlighting the bias of the NLM, we study the bias …

Clustering is one of the most vital image segmentation techniques. However, proper image
clustering has always been a challenging task due to blurred and vague areas near to …

In this paper, a cascade of stages is used to denoise images corrupted with Gaussian noise,
impulse noise or a mixture of the two. The proposed method is based on removing the …