In this paper, we propose a regularization technique for noisy-image super-resolution and image denoising. Total variation (TV) is adopted in many processing applications to preserve the local smoothness. However, TV prior prone oversmoothness, staircasing effect, contrast losses. Nonlocal (NLTV) mitigates losses by adaptively weighting smoothness based on similarity measure of patches. Althoug...

periodic noises are unwished and spurious signals that create repetitive pattern on images and decreased the visual quality. firstly, this paper investigates various methods for reducing the effects of the periodic noise in digital images. then an adaptive optimum notch filter is proposed. in the proposed method, the regions of noise frequencies are determined by analyzing the spectral of noisy...

Exploration into quantum machine learning has grown tremendously in recent years due to the ability of computers speed up classical programs. However, these ef- forts have yet solve unsupervised similarity detection tasks challenge porting them run on com- puters. To overcome this challenge, we propose SLIQ, first open-sourced work for resource-efficient sim- ilarity networks, built with practi...

Recently, deep learning techniques are soaring and have shown dramatic improvements in real-world noisy image denoising. However, the statistics of real noise generally vary with different camera sensors in-camera signal processing pipelines. This will induce problems most denoisers for overfitting or degrading performance due to discrepancy between training test sets. To remedy this issue, we ...

Removing spatially variant motion blur from a blurry image is challenging problem as can be complicated and difficult to model accurately. Recent progress in deep neural networks suggests that kernel-free single deblurring achieved, but questions about performance persist. To improve performance, we proposed convolutional network restore sharp noisy/blurry pair captured quick succession. Two st...