On the use of deep learning for computational imaging
Since their inception in the 1930–1960s, the research disciplines of computational imaging
and machine learning have followed parallel tracks and, during the last two decades …
and machine learning have followed parallel tracks and, during the last two decades …
Dictionaries for sparse representation modeling
Sparse and redundant representation modeling of data assumes an ability to describe
signals as linear combinations of a few atoms from a pre-specified dictionary. As such, the …
signals as linear combinations of a few atoms from a pre-specified dictionary. As such, the …
Making convolutional networks shift-invariant again
R Zhang - International conference on machine learning, 2019 - proceedings.mlr.press
Modern convolutional networks are not shift-invariant, as small input shifts or translations
can cause drastic changes in the output. Commonly used downsampling methods, such as …
can cause drastic changes in the output. Commonly used downsampling methods, such as …
Why do deep convolutional networks generalize so poorly to small image transformations?
Abstract Convolutional Neural Networks (CNNs) are commonly assumed to be invariant to
small image transformations: either because of the convolutional architecture or because …
small image transformations: either because of the convolutional architecture or because …
End-to-end optimized image compression
We describe an image compression method, consisting of a nonlinear analysis
transformation, a uniform quantizer, and a nonlinear synthesis transformation. The …
transformation, a uniform quantizer, and a nonlinear synthesis transformation. The …
Deep generative image models using a laplacian pyramid of adversarial networks
In this paper we introduce a generative model capable of producing high quality samples of
natural images. Our approach uses a cascade of convolutional networks (convnets) within a …
natural images. Our approach uses a cascade of convolutional networks (convnets) within a …
Learning a no-reference quality metric for single-image super-resolution
Numerous single-image super-resolution algorithms have been proposed in the literature,
but few studies address the problem of performance evaluation based on visual perception …
but few studies address the problem of performance evaluation based on visual perception …
A survey of sparse representation: algorithms and applications
Sparse representation has attracted much attention from researchers in fields of signal
processing, image processing, computer vision, and pattern recognition. Sparse …
processing, image processing, computer vision, and pattern recognition. Sparse …
dipIQ: Blind image quality assessment by learning-to-rank discriminable image pairs
Objective assessment of image quality is fundamentally important in many image processing
tasks. In this paper, we focus on learning blind image quality assessment (BIQA) models …
tasks. In this paper, we focus on learning blind image quality assessment (BIQA) models …