From Insect Vision to a Novel Bio-Inspired Algorithm for Image Denoising

Mapping Intimacies ◽

10.5772/intechopen.91911 ◽

2020 ◽

Author(s):

Manfred Hartbauer

Keyword(s):

Image Denoising ◽

Color Images ◽

Insect Vision ◽

Night Vision ◽

Primitive Type ◽

Noise Estimate ◽

Local Means ◽

Static Images ◽

Image Details ◽

Novel Algorithm

Night active insects inspired the development of image enhancement methods that uncover the information contained in dim images or movies. Here, I describe a novel bionic night vision (NV) algorithm that operates in the spatial domain to remove noise from static images. The parameters of this NV algorithm can be automatically derived from global image statistics and a primitive type of noise estimate. In a first step, luminance values were ln-transformed, and then adaptive local means’ calculations were executed to remove the remaining noise without degrading fine image details and object contours. Its performance is comparable with several popular denoising methods and can be applied to grey-scale and color images. This novel algorithm can be executed in parallel at the level of pixels on programmable hardware.

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Optimal graph edge weights driven nlms with multi-layer residual compensation

EURASIP Journal on Advances in Signal Processing ◽

10.1186/s13634-021-00800-z ◽

2021 ◽

Vol 2021 (1) ◽

Author(s):

Fang Yang ◽

Xin Chen ◽

Li Chai

Keyword(s):

Image Denoising ◽

Graph Laplacian ◽

Denoising Method ◽

Local Means ◽

Effective Performance ◽

Non Local ◽

Local Gradient ◽

Laplacian Regularization ◽

Image Details ◽

Optimal Graph

AbstractNon-local Means (NLMs) play essential roles in image denoising, restoration, inpainting, etc., due to its simple theory but effective performance. However, when the noise increases, the denoising accuracy of NLMs decreases significantly. This paper further develop the NLMs-based denoising method to remove noise with less loss of image details. It is realized by embedding an optimal graph edge weights driven NLMs kernel into a multi-layer residual compensation framework. Unlike the patch similarity-based weights in the traditional NLMs filters, the edge weights derived from the optimal graph Laplacian regularization consider (1) the distance between the target pixel and the candidate pixel, (2) the local gradient and (3) the patch similarity. After defining the weights, the graph-based NLMs kernel is then put into a multi-layer framework. The corresponding primal and residual terms at each layer are finally fused with learned weights to recover the image. Experimental results show that our method is effective and robust, especially for piecewise smooth images.

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Non Local Means Image Denoising for Color Images Using PCA

Advances in Computer Science and Information Technology - Communications in Computer and Information Science ◽

10.1007/978-3-642-17857-3_29 ◽

2011 ◽

pp. 288-297

Author(s):

P. A. Shyjila ◽

M. Wilscy

Keyword(s):

Image Denoising ◽

Color Images ◽

Local Means ◽

Non Local

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Optimal Graph Edge Weights Driven NLMs with Multi-layer Residual Compensation

10.21203/rs.3.rs-323646/v1 ◽

2021 ◽

Author(s):

Fang Yang ◽

Xin Chen ◽

Li Chai

Keyword(s):

Image Denoising ◽

Graph Laplacian ◽

Denoising Method ◽

Local Means ◽

Effective Performance ◽

Non Local ◽

Local Gradient ◽

Laplacian Regularization ◽

Image Details ◽

Optimal Graph

Abstract Non-Local Means (NLMs) play important roles in image denoising, restoration, inpainting etc. due to its simple theory but effective performance. In this paper, in order to better remove noise without loss of image details, we further develop the NLMs based denoising method. It is realized by introducing a graph Laplacian regularization based weighting model and a multi-layer residual compensation strategy. Unlike the patch similarity based weights in classic NLMs filters, the graph Laplacian regularization defines the weights by considering 1) the distance between target pixel and the candidate pixel, 2) the local gradient and 3) the patch similarity. The proposed NLMs filter performs in a multi-layer framework to better remove the noise and smooth the result. The corresponding residuals at each layer are finally combined with the smooth image with learned weights to recover the image details. Experimental results show that our method is effective and robust, especially for piecewise-smooth images.

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Insect photoreceptor adaptations to night vision

Philosophical Transactions of the Royal Society B Biological Sciences ◽

10.1098/rstb.2016.0077 ◽

2017 ◽

Vol 372 (1717) ◽

pp. 20160077 ◽

Cited By ~ 18

Author(s):

Anna Honkanen ◽

Esa-Ville Immonen ◽

Iikka Salmela ◽

Kyösti Heimonen ◽

Matti Weckström

Keyword(s):

Visual Information ◽

Insect Vision ◽

Night Vision ◽

Single Photons ◽

Visually Guided ◽

Neural Signals ◽

Light Responses ◽

Anatomical Structures ◽

Retinal Photoreceptors ◽

Physiological Adaptations

Night vision is ultimately about extracting information from a noisy visual input. Several species of nocturnal insects exhibit complex visually guided behaviour in conditions where most animals are practically blind. The compound eyes of nocturnal insects produce strong responses to single photons and process them into meaningful neural signals, which are amplified by specialized neuroanatomical structures. While a lot is known about the light responses and the anatomical structures that promote pooling of responses to increase sensitivity, there is still a dearth of knowledge on the physiology of night vision. Retinal photoreceptors form the first bottleneck for the transfer of visual information. In this review, we cover the basics of what is known about physiological adaptations of insect photoreceptors for low-light vision. We will also discuss major enigmas of some of the functional properties of nocturnal photoreceptors, and describe recent advances in methodologies that may help to solve them and broaden the field of insect vision research to new model animals. This article is part of the themed issue ‘Vision in dim light’.

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Automated Image Denoising Model: Contribution Towards Optimized Internal and External Basis

International Journal of Image and Graphics ◽

10.1142/s0219467821500510 ◽

2021 ◽

pp. 2150051

Author(s):

S. Elavaar Kuzhali ◽

D. S. Suresh

Keyword(s):

Image Denoising ◽

Image Patch ◽

The Past ◽

Local Means ◽

Hybrid Optimization Algorithm ◽

Proposed Model ◽

Processing Step ◽

Non Local ◽

Chicken Swarm Optimization ◽

Selection Of

For handling digital images for various applications, image denoising is considered as a fundamental pre-processing step. Diverse image denoising algorithms have been introduced in the past few decades. The main intent of this proposal is to develop an effective image denoising model on the basis of internal and external patches. This model adopts Non-local means (NLM) for performing the denoising, which uses redundant information of the image in pixel or spatial domain to reduce the noise. While performing the image denoising using NLM, “denoising an image patch using the other noisy patches within the noisy image is done for internal denoising and denoising a patch using the external clean natural patches is done for external denoising”. Here, the selection of optimal block from the entire datasets including internal noisy images and external clean natural images is decided by a new hybrid optimization algorithm. The two renowned optimization algorithms Chicken Swarm Optimization (CSO), and Dragon Fly Algorithm (DA) are merged, and the new hybrid algorithm Rooster-based Levy Updated DA (RLU-DA) is adopted. The experimental results in terms of some relevant performance measures show the promising results of the proposed model with remarkable stability and high accuracy.

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