Generally, most existing super-resolution (SR) methods do not consider noise, which treats SR reconstruction and denoising as two separate problems performs separately. However, noise is inevitably introduced in the imaging process. Based on analysis of degraded model, this paper, interpolation are modeled to estimate noiseless missing images under same framework. By applying local fractal dime...

Despite the success of deep learning methods in medical image segmentation tasks, human-level performance relies on massive training data with high-quality annotations, which are expensive and time-consuming to collect. The fact is that there exist low-quality annotations label noise, leads suboptimal learned models. Two prominent directions for noisy labels include pixel-wise noise robust imag...

Image segmentation is one of the most difficult, dynamic and challenging problems in the image processing domain. It denotes a process by which a raw input image is partitioned into non-overlapping regions such that each region is homogeneous and the union of two adjacent regions is heterogeneous. This paper proposes a novel level set based method for image segmentation, which is able to deal w...

This paper presents a preliminary research on extracting the recurring patterns from an image. In practice, the recurring patterns have high correlation with the objects that appear recurrently in the image. We assume that the patterns only undergo shift transformation in the image. The main idea of our method is to use the local context information for detecting the 2D distinguishable sub -pat...

Noisy image edge detection is one of the major areas of work in image processing. Edge detection of the noise-free images is much easier than finding the edge of noisy images. But in practice, many of the images are imbued with noise. This paper will present a new algorithm based on morphology operation to eliminate noise and perform correct edge detection simultaneously. The algorithm is appli...

This paper presents a segmentation algorithm for gray-level images and addresses issues related to its performance on noisy images. It formulates an image segmentation problem as a partition of an image into (arbitrarily-shaped) connected regions to minimize the sum of graylevel variations over all partitioned regions, under the constraints that (1) each partitioned region has at least a specif...

This paper uses the noisy chaotic neural network (NCNN) that we proposed earlier for image denoising as a constrained optimization problem. The experimental results show that the NCNN is able to offer good quality solutions.

A novel segmentation scheme for noisy image is proposed. According to the analysis of wavelet denoising method and multiscale geometric analysis techniques, an improved wavelet denoising algorithm combined with multiscale geometric analysis is presented in this paper first. Due to the isotropic nature of wavelet transform, 2D image details are not well represented in wavelet transform, which re...