Optimal Reconstruction of Natural Images by Small Sets of Gabor Filters

1998 ◽  
pp. 127-131
Author(s):  
J. H. van Deemter ◽  
G. Cristóbal
Keyword(s):  
Gabor Filters ◽  
Natural Images ◽  
Optimal Reconstruction

scholarly journals Double-Gabor Filters Are Independent Components of Small Translation-Invariant Image Patches

2013 ◽  
Vol 25 (4) ◽  
pp. 922-939 ◽  
Author(s):  
Saeed Saremi ◽  
Terrence J. Sejnowski ◽  
Tatyana O. Sharpee
Keyword(s):  
Visual Cortex ◽  
Independent Component Analysis ◽  
Primary Visual Cortex ◽  
Receptive Fields ◽  
Gabor Filters ◽  
Natural Images ◽  
Invariant Representation ◽  
Translation Invariant ◽  
Independent Components ◽  
Image Patches

The analysis of natural images with independent component analysis (ICA) yields localized bandpass Gabor-type filters similar to receptive fields of simple cells in visual cortex. We applied ICA on a subset of patches called position-centered patches, selected for forming a translation-invariant representation of small patches. The resulting filters were qualitatively different in two respects. One novel feature was the emergence of filters we call double-Gabor filters. In contrast to Gabor functions that are modulated in one direction, double-Gabor filters are sinusoidally modulated in two orthogonal directions. In addition the filters were more extended in space and frequency compared to standard ICA filters and better matched the distribution in experimental recordings from neurons in primary visual cortex. We further found a dual role for double-Gabor filters as edge and texture detectors, which could have engineering applications.

Prediction of the Helmholtz-Kohlrausch Effect for Natural Images Using a Correction Function

2019 ◽  
Vol E102.A (9) ◽  
pp. 1217-1224
Author(s):  
Yuki HAYAMI ◽  
Daiki TAKASU ◽  
Hisakazu AOYANAGI ◽  
Hiroaki TAKAMATSU ◽  
Yoshifumi SHIMODAIRA ◽  
...  
Keyword(s):  
Natural Images ◽  
Correction Function

Learning About the World by Learning About Images

2021 ◽  
pp. 096372142199033
Author(s):  
Katherine R. Storrs ◽  
Roland W. Fleming
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Visual Processing ◽  
Visual Experience ◽  
Natural Images ◽  
Sensory Coding ◽  
The World ◽  
Unsupervised Deep Learning ◽  
Statistical Regularities ◽  
Sensory Encoding

One of the deepest insights in neuroscience is that sensory encoding should take advantage of statistical regularities. Humans’ visual experience contains many redundancies: Scenes mostly stay the same from moment to moment, and nearby image locations usually have similar colors. A visual system that knows which regularities shape natural images can exploit them to encode scenes compactly or guess what will happen next. Although these principles have been appreciated for more than 60 years, until recently it has been possible to convert them into explicit models only for the earliest stages of visual processing. But recent advances in unsupervised deep learning have changed that. Neural networks can be taught to compress images or make predictions in space or time. In the process, they learn the statistical regularities that structure images, which in turn often reflect physical objects and processes in the outside world. The astonishing accomplishments of unsupervised deep learning reaffirm the importance of learning statistical regularities for sensory coding and provide a coherent framework for how knowledge of the outside world gets into visual cortex.

A Hybrid Inpainting Model Combining Diffusion and Enhanced Exemplar Methods

2021 ◽  
Vol 13 (3) ◽  
pp. 1-19
Author(s):  
Sreelakshmy I. J. ◽  
Binsu C. Kovoor
Keyword(s):  
Patch Size ◽  
Image Inpainting ◽  
Qualitative Evaluation ◽  
Natural Images ◽  
External Information ◽  
Image Editing ◽  
Model Combining ◽  
Proposed Model ◽  
The World

Image inpainting is a technique in the world of image editing where missing portions of the image are estimated and filled with the help of available or external information. In the proposed model, a novel hybrid inpainting algorithm is implemented, which adds the benefits of a diffusion-based inpainting method to an enhanced exemplar algorithm. The structure part of the image is dealt with a diffusion-based method, followed by applying an adaptive patch size–based exemplar inpainting. Due to its hybrid nature, the proposed model exceeds the quality of output obtained by applying conventional methods individually. A new term, coefficient of smoothness, is introduced in the model, which is used in the computation of adaptive patch size for the enhanced exemplar method. An automatic mask generation module relieves the user from the burden of creating additional mask input. Quantitative and qualitative evaluation is performed on images from various datasets. The results provide a testimonial to the fact that the proposed model is faster in the case of smooth images. Moreover, the proposed model provides good quality results while inpainting natural images with both texture and structure regions.

scholarly journals HDR Imaging With Quanta Image Sensors: Theoretical Limits and Optimal Reconstruction

2020 ◽  
Vol 6 ◽  
pp. 1571-1585
Author(s):  
Abhiram Gnanasambandam ◽  
Stanley H. Chan
Keyword(s):  
Image Sensors ◽  
Optimal Reconstruction
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