Image synthesis based on a model of human vision

Modern computer graphics systems are able to construct renderings of such high quality that viewers are deceived into regarding the images as coming from a photographic source. Large amounts of computing resources are expended in this rendering process, using complex mathematical models of lighting and shading. However, psychophysical experiments have revealed that viewers only regard certain informative regions within a presented image. Furthermore, it has been shown that these visually important regions contain low-level visual feature differences that attract the attention of the viewer. This thesis will present a new approach to image synthesis that exploits these experimental findings by modulating the spatial quality of image regions by their visual importance. Efficiency gains are therefore reaped, without sacrificing much of the perceived quality of the image. Two tasks must be undertaken to achieve this goal. Firstly, the design of an appropriate region-based model of visual importance, and secondly, the modification of progressive rendering techniques to effect an importance-based rendering approach. A rule-based fuzzy logic model is presented that computes, using spatial feature differences, the relative visual importance of regions in an image. This model improves upon previous work by incorporating threshold effects induced by global feature difference distributions and by using texture concentration measures. A modified approach to progressive ray-tracing is also presented. This new approach uses the visual importance model to guide the progressive refinement of an image. In addition, this concept of visual importance has been incorporated into supersampling, texture mapping and computer animation techniques. Experimental results are presented, illustrating the efficiency gains reaped from using this method of progressive rendering. This visual importance-based rendering approach is expected to have applications in the entertainment industry, where image fidelity may be sacrificed for efficiency purposes, as long as the overall visual impression of the scene is maintained. Different aspects of the approach should find many other applications in image compression, image retrieval, progressive data transmission and active robotic vision

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Fast Deep Multi-patch Hierarchical Network for Nonhomogeneous Image Dehazing

Recently, CNN based end-to-end deep learning methods achieve superiority in Image Dehazing but they tend to fail drastically in Non-homogeneous dehazing. Apart from that, existing popular Multi-scale approaches are runtime intensive and memory inefficient. In this context, we proposed a fast Deep Multi-patch Hierarchical Network to restore Non-homogeneous hazed images by aggregating features from multiple image patches from different spatial sections of the hazed image with fewer number of network parameters. Our proposed method is quite robust for different environments with various density of the haze or fog in the scene and very lightweight as the total size of the model is around 21.7 MB. It also provides faster runtime compared to current multi-scale methods with an average runtime of 0.0145s to process 1200x1600 HD quality image. Finally, we show the superiority of this network on Dense Haze Removal to other state-of-the-art models.Comment: CVPR Workshops Proceedings 202

Coding of details in very low bit-rate video systems

In this paper, the importance of including small image features at the initial levels of a progressive second generation video coding scheme is presented. It is shown that a number of meaningful small features called details should be coded, even at very low data bit-rates, in order to match their perceptual significance to the human visual system. We propose a method for extracting, perceptually selecting and coding of visual details in a video sequence using morphological techniques. Its application in the framework of a multiresolution segmentation-based coding algorithm yields better results than pure segmentation techniques at higher compression ratios, if the selection step fits some main subjective requirements. Details are extracted and coded separately from the region structure and included in the reconstructed images in a later stage. The bet of considering the local background of a given detail for its perceptual selection breaks the concept ofPeer ReviewedPostprint (published version

A bio-inspired image coder with temporal scalability

We present a novel bio-inspired and dynamic coding scheme for static images. Our coder aims at reproducing the main steps of the visual stimulus processing in the mammalian retina taking into account its time behavior. The main novelty of this work is to show how to exploit the time behavior of the retina cells to ensure, in a simple way, scalability and bit allocation. To do so, our main source of inspiration will be the biologically plausible retina model called Virtual Retina. Following a similar structure, our model has two stages. The first stage is an image transform which is performed by the outer layers in the retina. Here it is modelled by filtering the image with a bank of difference of Gaussians with time-delays. The second stage is a time-dependent analog-to-digital conversion which is performed by the inner layers in the retina. Thanks to its conception, our coder enables scalability and bit allocation across time. Also, our decoded images do not show annoying artefacts such as ringing and block effects. As a whole, this article shows how to capture the main properties of a biological system, here the retina, in order to design a new efficient coder.Comment: 12 pages; Advanced Concepts for Intelligent Vision Systems (ACIVS 2011

Blockade of alpha 2-adrenergic receptors in prelimbic cortex: impact on cocaine self-administration in adult spontaneously hypertensive rats following adolescent atomoxetine treatment

RATIONALE: Research with the spontaneously hypertensive rat (SHR) model of attention deficit/hyperactivity disorder demonstrated that chronic methylphenidate treatment during adolescence increased cocaine self-administration established during adulthood under a progressive ratio (PR) schedule. Compared to vehicle, chronic atomoxetine treatment during adolescence failed to increase cocaine self-administration under a PR schedule in adult SHR. OBJECTIVES: We determined if enhanced noradrenergic transmission at α2-adrenergic receptors within prefrontal cortex contributes to this neutral effect of adolescent atomoxetine treatment in adult SHR. METHODS: Following treatment from postnatal days 28–55 with atomoxetine (0.3 mg/kg) or vehicle, adult male SHR and control rats from Wistar-Kyoto (WKY) and Wistar (WIS) strains were trained to self-administer 0.3 mg/kg cocaine. Self-administration performance was evaluated under a PR schedule of cocaine delivery following infusion of the α2-adrenergic receptor antagonist idazoxan (0 and 10–56 μg/side) directly into prelimbic cortex. RESULTS: Adult SHR attained higher PR break points and had greater numbers of active lever responses and infusions than WKY and WIS. Idazoxan dose-dependently increased PR break points and active lever responses in SHR following adolescent atomoxetine vs. vehicle treatment. Behavioral changes were negligible after idazoxan pretreatment in SHR following adolescent vehicle or in WKY and WIS following adolescent atomoxetine or vehicle. CONCLUSIONS: α2-Adrenergic receptor blockade in prelimbic cortex of SHR masked the expected neutral effect of adolescent atomoxetine on adult cocaine self-administration behavior. Moreover, greater efficacy of acute idazoxan challenge in adult SHR after adolescent atomoxetine relative to vehicle is consistent with the idea that chronic atomoxetine may downregulate presynaptic α2A-adrenergic autoreceptors in SHR.National Institutes of Health grant DA011716. (DA011716 - National Institutes of Health)https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5693724/Published versio