Non-local means (NLM) is a popular image denoising scheme for reducing additive Gaussian noise. It uses a patch-based approach to find similar regions within a search neighborhood and estimates the denoised pixel based on the weighted average of all pixels in the neighborhood. All weights are considered for averaging, irrespective of the value of the weights. This paper proposes an improved var...

In this thesis, we propose a novel semi-supervised clean-noisy datasets adaptation algorithm. We transfer the knowledge learned on clean images to unlabeled noise-distorted ones. This modification on standard deep networks produce stable classification performance on all distortion levels, which brings benefit to real-world cases. Specifically, we propose a strategy to jointly learn a shared fe...

This paper studies the problem of full reference visual quality assessment of denoised images with a special emphasis on images with low contrast and noise-like texture. Denoising of such images together with noise removal often results in image details loss or smoothing. A new test image database, FLT, containing 75 noise-free ‘reference’ images and 300 filtered (‘distorted’) images is develop...

Retinal diseases are significant cause of visual impairment globally. In the worst case they may lead to severe vision loss or blindness. Accurate diagnosis is a key factor in right treatment planning that can stop slow disease. The examination aid Optical Coherence Tomography (OCT). OCT scans susceptible various noise effects which deteriorate their quality and as result impede analysis conten...

Convolutional layers treat the Channel features equally with no prioritization. When Neural Networks (CNNs) are used for image denoising in real-world applications unknown noise distributions, particularly structured learnable patterns, modeling informative can substantially boost performance. attentions tasks exploit dependencies between feature channels; therefore, they be viewed as a frequen...

In this paper we propose a generic recursive algorithm for improving image denoising methods. Given the initial denoised image, we suggest repeating the following “SOS” procedure: (i) Strengthen the signal by adding the previous denoised image to the degraded input image, (ii) Operate the denoising method on the strengthened image, and (iii) Subtract the previous denoised image from the restore...

Due to the fast inference and good performance, discriminative learning methods have been widely studied in image denoising. However, these methods mostly learn a specific model for each noise level, and require multiple models for denoising images with different noise levels. They also lack flexibility to deal with spatially variant noise, limiting their applications in practical denoising. To...

We propose an image representation scheme combining the local and nonlocal characterization of patches in an image. Our representation scheme can be shown to be equivalent to a tight frame constructed from convolving local bases (e.g., wavelet frames, discrete cosine transforms, etc.) with nonlocal bases (e.g., spectral basis induced by nonlinear dimension reduction on patches), and we call the...

Phase-shifting interferometry is a coherent optical method that combines high accuracy with high measurement speeds. This technique is therefore desirable in many applications such as the efficient industrial quality inspection process. However, despite its advantageous properties, the inference of the object amplitude and the phase, herein termed wavefront reconstruction, is not a trivial task...

Natural images are often affected by random noise and image denoising has long been a central topic in Computer Vision. Many algorithms have been introduced to remove the noise from the natural images, such as Gaussian, Wiener filtering and wavelet thresholding. However, many of these algorithms remove the fine edges and make them blur. Recently, many promising denoising algorithms have been in...